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Dissertations, Theses, and Masters Projects Theses, Dissertations, & Master Projects
1999
Geochemistry of Small Mountainous Rivers of Papua New Guinea: Local Observations and Global Implications
Megan B. Raymond College of William and Mary - Virginia Institute of Marine Science
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Recommended Citation Raymond, Megan B., "Geochemistry of Small Mountainous Rivers of Papua New Guinea: Local Observations and Global Implications" (1999). Dissertations, Theses, and Masters Projects. Paper 1539617742. https://dx.doi.org/doi:10.25773/v5-n324-ja06
This Thesis is brought to you for free and open access by the Theses, Dissertations, & Master Projects at W&M ScholarWorks. It has been accepted for inclusion in Dissertations, Theses, and Masters Projects by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected]. GEOCHEMISTRY OF SMALL MOUNTAINOUS RIVERS OF PAPUA NEW GUINEA; LOCAL OBSERVATIONS AND GLOBAL IMPLICATIONS
A Thesis
Presented to
The Faculty of the School of Marine Science
The College of William and Mary in Virginia
In Partial Fulfillment
of the Requirements for the Degree of
Master of Science
by
Megan B. Raymond
1999 APPROVAL SHEET
This thesis is submitted in partial fulfillment of the requirements for the degree of
Master of Science
Megan B. Raymond
Approved, July 1999.
U(A ^ Johti D. Milliman, Ph.D. Co iimittee Co-Chairman/Co-Advisor
Jame6 /E. Bauer,yPh.D. Committee Co-A^hairman/Co-Advisor
■7 /7
Catherine J. Chisholm-Brause, Ph.D. TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS...... iv
LIST OF TABLES...... v
LIST OF FIGURES...... vi
ABSTRACT...... vii
INTRODUCTION...... 2
BACKGROUND...... 3
STUDY AREA...... 7
MATERIALS AND METHODS...... 8
RESULTS...... 14
DISCUSSION...... 25
CONCLUSIONS...... 47
REFERENCES...... 48
APPENDIX...... 52
VITA 56 ACKNOWLEDGEMENTS
The support and guidance provided by my advisor, Dr. John D. Milliman, as well as my entire thesis committee of Dr. Jim Bauer, Dr. Catherine Chisholm-Brause, and Dr. Hugh Ducklow, throughout my research project are gratefully acknowledged. The analysis of my samples would not have been possible without the help of Catherine Chisolm-Brause, Mark Schrope, Pete Raymond, Libby MacDonald, Barry Grant and John Edmond. I would also like to thank my friends and family for their continual support of me in all capacities.
iv LIST OF TABLES
Table Page
1. River names, catchment areas and headwater elevations ...... 10
2. Concentrations of major inorganic solutes ...... 16
3. Dissolved loads and yields ...... 20
4. Concentrations of DOC and POC ...... 23
5. Results from carbon isotope analysis of POC ...... 24
6 . Rainfall data 1990-1997 in Madang, PNG ...... 27
7. Supplementary data ...... 30
8 . Inorganic solute data from large PNG rivers ...... 36
9. Examples or rivers and regions included in morphoclimatic budget ...... 42
10. Morphoclimatic division of rivers ...... 43 LIST OF FIGURES
Figure Page
1. TSS and TDS yield as a function of basin area ...... 4
2. Study Area ...... 9
3. TDS load versus basin area for sampled PNG rivers ...... 21
4. PNG TDS yield as a function of basin area ...... 22
5. Monthly rainfall in Madang, PNG 1990-1997 ...... 26
6 . Estimated monthly river discharge of sampled PNG rivers ...... 29
7. Comparision of TDS concentration of sampled rivers to world average ...... 32
8 . Chehalis R. and Santa Clara R. annual discharge, TDS load and concentration...38
9. Morphoclimatic division of annual riverine TDS export to ocean ...... 44 ABSTRACT
Small, wet mountainous rivers (runoff > 0.63 m yr"1, headwater elevation > 1 0 0 0 m, basin area < 1 0 ,0 0 0 km2) contribute a disproportionate amount of sediment to the global ocean due to their steep high topography, erosive substrate, and often high precipitation. Scattered data have suggested a slight, but statistically insignificant, inverse relationship between total dissolved solid (TDS) yield (T knT" - 1 9 yr" ) and basin area, but small to very small rivers (basin areas < 1 0 ,0 0 0 km ) have been poorly documented. To fill this data gap, as well as to elucidate possible links between weathering and basin hydrology, nine small wet mountainous rivers, basin 2 ^ areas 22 km -2300 kmf, were sampled in late May 1997 in northeastern Papua New i Guinea. TDS concentrations ranged from 75 to 148 mg L’ , with no correlation to watershed area. The dissolved organic carbon (DOC) and particulate organic carbon (POC) values were low, with a mean DOC value of 135 pmol L'1; POC values were lower, averaging 31pmol L"1. TDS data, combined with large wet mountainous river TDS data, demonstrate a significant inverse relationship between TDS yield and basin area. As a result, small wet mountainous rivers contribute a disproportionate amount of TDS, and have the highest TDS yields of any class of river. This observation is attributed to the high runoff of the sampled rivers (~ 2 m yr"1), in addition to high rates of chemical weathering, which is facilitated by the erosive substrate and high rates of organic matter remineralization. GEOCHEMISTRY OF SMALL MOUNTAINOUS RIVERS OF NORTHEASTERN PAPUA NEW GUINEA; Local Implications and Global Observations 2
INTRODUCTION
Rivers are the primary link between the continents and the oceans, and the
transport of solutes from rivers plays a major role in oceanic chemistry. Mechanical and chemical weathering within the watershed exert the primary control on river chemistry.
Although mechanical weathering is a physical process and chemical weathering is driven by thermodynamic and kinetic processes, the two are coupled through the hydrodynamics of a river system (Stallard and Edmond 1983). As such, chemical weathering is largely dependent on the lithology of the source rocks, the elevation of the headwaters and relief of the watershed, climate (i.e. rainfall, watershed vegetation and temperature), and organic matter remineralization both within watershed soils and the river itself (Berner et al, 1983; Meybeck, 1987; Berner, 1992; Drever and Zobrist, 1992; Meybeck, 1994). The lithology of the source rocks in a river basin dictates the ions that will be weathered.
Organic matter remineralization increases the rate of chemical weathering through the production of H+ ions (i.e. from carbonic acid), which dissolves rocks to release ions from the bedrock. The relief of watershed topography enhances the rate of mechanical breakdown of rocks, thereby increasing the surface area of exposed material and thus the rate of chemical weathering (Lasaga et al. 1994). In areas of very high relief, soil formation is limited, which increases the exposure of fresh rock and mineral surfaces and hence chemical weathering (Stallard and Edmond 1983).
Small, wet mountainous rivers located in southeastern Asia and Oceania (runoff
> 0.63 m y r1, basin area < 10,000 km2, headwater elevation > 1000 m above sea level) represent a unique category of rivers because of their highly erosive substrate, steep high topography, and exposure to seasonally heavy monsoonal rainfall (Milliman and 3
Syvitski,1992). Due to the aforementioned characteristics, these rivers have high total
suspended sediment (TSS) yields (Figure 1), and thus contribute a disproportionately
large amount of sediment to the global ocean (Milliman and Syvitski, 1992). Visual
inspection of the few available total dissolved solids (TDS) data from wet mountainous
rivers suggests that chemical yield (i.e. TDS load/basin area) increases slightly with decreasing basin area (Milliman, 1997) (Figure 1); however, the relationship is weak and does not include data from rivers with basin areas smaller than 1 0 0 0 km2.
The purpose of the present study was to quantify the yield of dissolved inorganic components from small mountainous rivers of Papua New Guinea in order to identify the possible links between physical and chemical weathering and basin hydrology in small to very small river basins. Sampling such small rivers might help elucidate the factors controlling weathering in these basins and also document the importance of these rivers with respect to the export of TDS to the oceans. Because the rivers of New Guinea
Q j discharge large quantities of sediment (estimated at 1.7 * 10 T yr' , about 10% of the global total; Milliman, 1995) and water annually, the discharge of dissolved material from small New Guinea rivers may have global relevance. Dissolved and particulate organic carbon (DOC and POC, respectively) were also measured to document further the hydrologic and biologic dynamics in these small basins.
BACKGROUND
PROCESSES CONTROLLING RIVER CHEMISTRY
Substrate lithology and erosional regime exert the dominant controls on the dissolved load in the Amazon River basin, a large, wet mountainous river (Stallard and
Edmond, 1981; 1983; 1987). This river is interesting for comparison to the sampled 4
FIGURE 1. Total suspended sediment (TSS) yield (r2= 0.58, m= -0.42) and total dissolved sediment (TDS) yield (r2= 0.03, m= -0.05) as a function of basin area. Note the lack of TDS data from rivers 10,000km2 and smaller. Slope of TSS data was significant, p < 0.001. Slope of TDS was not significantly different than zero ( p < 0.001). Slope of TSS yield and TDS yield are significantly different from each other ( p > 0.05). Data from Milliman & Farnsworth, in prep.
Data was analyzed with both MODEL I and MODEL II regressions with no difference between the results, therefore MODEL I was used to generate these data. TDS and TSS yield (T/km2/yr) Basin Area Area Basin k ) (km • TSS yield TSS • O TDS yield TDS O
5
Papua New Guinea (PNG) rivers, because it was the first detailed study of watershed
control on dissolved solid load.
The erosional regime of a watershed can be classified further into either transport-
limited or weathering-limited denudation (Stallard and Edmond, 1983). Transport-
limited systems are those where the maximum weathering rate exceeds the rate of
removal of the weathered material, such as in low-lying terrain, and a thick soil develops
through time. In weathering-limited regimes weathered material is removed more rapidly
than processes can generate new material. The most erosive rocks are the major
contributors to the solute load, and weathering is quite selective, in contrast to the transport-limited regime where weathering is less selective. Weathering-limited regimes tend to occur in steep terrains, where thick regoliths can not form and the majority of eroded material is quickly entrained into river flow. Thus, the residence time of the suspended or bed load can become an important factor in controlling the dissolved solute concentration.
Plotting total dissolved salts against the ratio of Na+/ (Na++ Ca2+), Gibbs (1970) classified river chemistry into three groups: precipitation-dominant, evaporation- crystallization dominant, and rock dominant. Precipitation-dominated rivers are commonly low-lying tropical ones that drain highly leached soils and bedrock.
Evaporation-crystallization dominated rivers occur in hot, arid regions and have medium salinities. Rock-dominated rivers, such as those in Papua New Guinea, have water chemistry that reflects the weathering processes of the primary lithologies within the watershed. 6
In a study of the weathering regime along the Amazon River, Stallard and
Edmond (1987) found that the most easily eroded minerals are precipitates, followed by
the mafic minerals, and then sialic phases. In terms of rock susceptibility to chemical
weathering, Meybeck (1987) found a similar order (in order of decreasing rates of
weathering): rock salt > gypsum > > carbonate rocks » serpentine, marble, amphibolite
> shales > volcanic rocks (basalts) > gabbro, sandstone > granite, gneiss, micaschist.
Waters draining rocks that have been subaerially exposed for a longer period of time do
not have as high a concentration of dissolved ions as waters draining more recently exposed rocks of the same lithology as ions are continually leached from their parent material through time (Berner and Berner, 1996).
Subsurface flow of groundwater also may affect the concentrations of ions and organic matter found in rivers. If the permeability of the rocks and soils is high, a large proportion of rainfall may infiltrate the surface and become part of the groundwater comprising baseflow. Because of the increased contact time in the subsurface and the presence of organic acids that facilitate chemical weathering, groundwater discharged to the river can be solute-rich (Drever, 1997). In addition, if baseflow is a significant contributor to the fluvial discharge of rivers, the dissolved organic carbon (DOC) might be low, due to loss by microbial decomposition, adsorption and precipitation (Stevenson,
1985).
The major controls on dissolved solids concentrations of small tropical mountainous rivers therefore are thought to be interbasinal processes, namely chemical weathering facilitated by organic matter respiration. Subsurface flow may be a major contributor to the discharge of rivers and therefore of dissolved ion concentration, but it
is difficult, if not impossible, to quantify groundwater importance in the PNG system.
TSS vs. TDS SAMPLING
TDS concentrations in rivers, in contrast to TSS concentrations, remain relatively
stable in many rivers, generally being highest during low flow and lowest during high flow (Walling, 1984). This temporal stability of TDS concentrations increases the probability of procuring a representative TDS sample, because chemical erosion is a continuous and relatively constant function of time (Garrels and MacKenzie, 1971). TSS concentrations, in contrast, are more episodic by nature of their export dynamics, and a more rigorous sampling procedure is necessary to obtain a representative sample
(Walling, 1984).
STUDY AREA
Papua New Guinea (PNG) encompasses the eastern half of the island of New
Guinea, north of Australia, in the western subequatorial Pacific Ocean (Figure 2). New
Guinea occupies approximately 800,000 km 2 in area, representing less than 1 % of the total global land area that drains into the ocean; however, rivers on this island discharge
-1.7 * 109T of sediment per year, or about 10% of the global total (Milliman, 1995).
The island transports sediment from the actively uplifting mountains to the ocean via a number of small rivers. New Guinea does have a few larger river systems, including the
Mamberamo, (85,000 km2), the Sepik (72,000 km2) and the Fly, (76,000 km2) (Milliman,
1995), but a substantial portion of the annual sediment load is discharged by the numerous rivers whose basin areas are less than 5,000 kirf. A few of the larger river systems’ TDS export have been documented (Petr, 1983), but no TDS data exist for
rivers smaller than 13,200 km2.
The study area is located southeast of the coastal town of Madang (~ 5°20’S and
145°50’E) (Figure 2). The climate is monsoonal, with northwestern winds prevailing
from December through March, and with southeast trade winds dominating the remainder
of the year. The temperature is warm in the lowland areas, with an average high and low
of 30°C and 23°, respectively. Temperatures decrease with altitude, on the average of 8 0
for the first 1000 m in elevation and 5 0 for each additional 1000 m (Robinson et al.,
j 1976). Rainfall is moderate to heavy for most of the year, averaging about - 4 m yr' in the lowlands, but the higher elevations may receive more (Robinson et al, 1976).
Heaviest rains fall during the NW monsoon season. The area is vegetated by primary rainforest, and shows little anthropogenic disturbance.
Nine rivers were sampled in this study (Table 1). All drain the knife-edged
Finesterre Range (average elevations 2000 m), and with the exception of the Tapo R., which is a tributary to the Gogol R., all empty into Astrolabe Bay. The lithology of the drainage basins is dominated by Cenozoic marine carbonates and sandstones. Basalt, welded tuff, and other pyroclastics comprise minor components of the bedrock (Robinson et al., 1976). The sampled rivers can be divided into two distinctly different geomorphic classes: one lowland river (the Gum R., headwaters approximately 750 m in elevation) and eight mountainous rivers (all of whose headwaters are 1 0 0 0 m and greater).
MATERIALS AND METHODS
The 9 rivers were sampled May 20-24, 1997, at the end of the monsoon season, during the falling hydrograph. Water samples were collected by wading into the middle 9
Figure 2. Study area in PNG. See inset for location on island. Contour interval = 200 m for lower map, 1000 m for inset. 125E 130E 1 » g 140E T4$E 150E
Australia
0 200 400
Madang
Astrolabe Bay 10
TABLE 1. River Names, Catchment Areas and Headwater Elevations of Papua New
River Catchment Area (km2)1 Headwater elevation (m)2 Boku 2 2 1 0 0 0 Gogol 2366 1 1 0 0 Gum 158 750 Kabenau 281 2 1 0 0 Midijim 244 1300 Palypa 46 - 1 0 0 0 Tapo 28 1 0 0 0 Tyol 60 1 1 0 0 Youour 160 1 1 0 0 1 Estimated error = 0.06% in largest river to 6% in smallest one. 2 Estimated error = < 1.3%. of the river, free from riverbank influences, and at a location upstream from any tidal
influence. Water samples were taken with an acid-washed plastic bucket
that had been rinsed several times with ambient river water. Estimated water velocities in 3 1 all rivers were 50 - 100 cm' s’ . Samples were analyzed for major inorganic solutes, and
DOC and POC.
INORGANIC SOLUTES
Samples for major dissolved inorganic solutes were filtered through pre-weighed
0.45-pm Gelman polycarbonate filters using a hand-operated vacuum pump. Volumes
filtered ranged from 0.2 L to 1 L, and the filtered samples were placed in pre-acidified
(10% 2N HNO3’), sterile 50-ml plastic centrifuge tubes. Duplicate samples were
collected at each site.
The dissolved inorganic samples were analyzed for total silicon and sulfur with an
inductively-coupled plasma atomic emission spectrophotometer equipped with an axial
torch (ThermoJarrell Ash). Standard solutions were made by dilution of certified
standards to span concentrations in the samples (0-50 ppm), and were used to establish
calibration curves. The optimal wavelengths for each element were chosen using the
standard. The probe was acid-washed between each sample, and analyses were run in
quadruplicate.
Samples were analyzed for Na+, K+, Ca2+, and Mg2+in Dr. John Edmond’s
laboratory at MIT by flame atomic absorption (Perkin Elmer 403). Standards spanned the anticipated concentrations in the samples (0-110 jaM), and a Lanthanum spike was
added to each standard and sample for matrix stability. Linearity 12
was achieved with all four calibration curves (r2= 0.99). Samples were diluted 50:1 for
the Mg2+ analysis because of the high concentration of this ion.
BICARBONATE
The bicarbonate samples were stored in 50-ml glass serum vials, previously baked
at 550°C for four hours, and the samples were preserved with 2 pi of a saturated solution
of HgCb. The vials were purged with N2 gas to remove any CO 2 in the vial. In the field, the samples were injected into the serum vials using 2 2 -gauge needles, in order to minimize exchange with the atmosphere, and duplicates were collected.
The pH was measured in the lab (a consequence of losing the field pH meter en route to PNG). The validity of this method of pH measurement was confirmed by a field test at VIMS. To do this, five water samples from the Mattaponi R. (a tributary to the
York R.) were collected using the PNG method, and then stored for 4 weeks in similar 50 ml glass serum vials as were used in PNG. The pH was measured at the time of sampling, and again after 4 weeks of refrigerated storage. There was no difference between the two values (pH = 7.1 ± 0.0), indicating that measuring the pH in the lab, following addition of
HgCb to field samples, provided accurate pH values.
Bicarbonate was measured on a Shimadzu 5000A Total Organic Carbon analyzer used in total inorganic carbon mode. The acidified sample was injected into a reservoir, with the resultant CO 2 measured by non-dispersive IR (NDIR) spectroscopy, and compared with standard solutions ranging from 0 to 3500 pM in concentration. This provided an estimate of the total amount of inorganic carbon (TCO 2) in the sample. The pH was also measured and the speciation of the carbonate system components calculated using the dissociation constants at standard temperature and pressure (Drever, 1997). 13
Alkalinity also was measured by titration for all samples to re-check the TCO 2 and pH methods. Bicarbonate was also estimated using a mass-balance approach to balance the cations and anions on a milliequivalent basis.
DISSOLVED ORGANIC CARBON
Water for DOC analysis was filtered through 0.7 Jim GF/F Whatman disposable syringe filters (pre-soaked for 2 hours in Ultrapure H 2O), and the filtrate was collected in acid-washed 45-ml polycarbonate vials. Samples were preserved with 100 fiL of 2M
HC1, and duplicate samples were collected at each site.
DOC was quantified using a Shimadzu 5000A Total Organic Carbon analyzer.
The sample was acidified (from field preparation) to convert inorganic carbon to CO 2, which was stripped from the sample by bubbling with ultra-high purified air for 4 minutes. The sample was then injected onto a Pt-alumina catalyst at 680° to oxidize the organic carbon, and the resulting CO 2 was measured by NDIR spectroscopy and compared to standard solutions spanning 0-100 jiM in concentration.
PARTICULATE ORGANIC CARBON
The POC was collected by filtration of water samples through 0.7 jiim GF/F
Whatman filters (45-mm diameter). All filters were been previously baked at 550° for 4 hours. Water was filtered until the filter became visibly colored, with the total amount of sample filtered varying between 0.5 L to 1.5 L. After sampling, the filters were placed in a dessicator and dried. Drying time was rapid (15-45 min). The dried filters were then placed on a piece of baked aluminum foil with baked forceps, and wrapped and stored in an air-tight Ziploc bag. Duplicate samples were not collected. 14
Two 13-mm sub-cores were taken from the GF/F filter for analysis on a Fisons
CFtN elemental analyzer. A sulfanilamide standard was used (weights ranging from
0.065 mg to 1.6 mg) to establish a linear calibration curve (r = .98) to calculate POC
concentration. The remainders of the filters were sent to the Center for Accelerator Mass
Spectrometry at Lawrence Livermore National Laboratory (LLNL) for natural l4C
1 3 analyses. Splits of the samples (-10% of the total) were taken for C analyses, which
were analyzed by isotope ratio mass spectrometry (IRMS) by LLNL (Williams and
Gordon, 1970).
MAP WORK
Topographic and geologic maps were used to calculate drainage basin area and
identify the dominant lithologies of the basin area. Watershed boundaries were drawn on area topographic maps using topographic highs, and digitized using a Numonics
Corporation Electronic Graphics Calculator to calculate basin area. Topographic maps
(1:100,000 scale) were made by aerial photography in 1973, and estimated errors were ±
30 m in the horizontal direction, and ± 10 m in the vertical, indicating a range of error of the watershed areas to be from 0.06% in the Gogol R. to 6 % in the Boku R. Elevations of headwaters were estimated by visual inspection of the same topographic maps. The dominant lithologies in the watersheds were identified using the Madang quadrangle geologic map and map notes (Robinson et al, 1976).
RESULTS
MAJOR INORGANIC SOLUTES
The TDS concentrations of all samples ranged from 50 to 148 mg L'1, averaging
111 mg L' 1 (Table 2). Ca2+ (8-15 mg L'1) and Mg2+ (7-11 mg L'1) comprised an 15 average of 90% of the total cations, and were present in almost equal proportions; Na+ and K+ were present in far smaller concentrations (0.3-3 mg L’1). The average coefficient of variation for all duplicates was less than 3%. The dominant anion was
HCO3 ', determined by both direct analysis and the subtraction of SO4 2' (the only other significant anion from the total cations. Because the cations and anions must balance, this is an acceptable, though not ideal, method (J. Edmond, pers. comm.). This
calculation was necessary because the anions (i.e. HCO3 ") were approximately two times higher than the cations on a milliequivalent basis (average cations =1218 meq. + 306, average anions = 2556 meq. ± 735). This imbalance may have been caused by poor sampling procedure. For example, since the DIC samples were not filtered, any
particulate CaCCF would contaminate the sample resulting in high HCO3 . DIC samples must be filtered when working in a carbonate environment to avoid this. The dissolved
solids in all nine rivers were dominated by Mg2+:Ca2+/ HCO3 ", which accounted for 80% of the total dissolved inorganic solutes. There was no significant correlation (p > 0.05) between TDS concentration and basin area (Table 1 and 2). 16
TABLE 2. Concentrations of major inorganic solutes (mg L'1) for all PNG rivers sampled in the present study. ______river aCa2+ aMg2+ aNa+ aK+ bSi0 2 bso42' chc o 3 TDS C.V. % Boku 13 11 2.0 3.3 13 4.3 101 147 2 Gogol 13 8.0 0.9 1.5 14 2.7 80 120 3 G ogol4 15 8.5 0.6 1.0 12 2.5 85 125 5 Gum 9.5 6.8 0.7 0.9 16 1.9 64 100 7 Gum 4 3.7 3.3 0.4 0.2 14 0.7 28 50 2 Kabenau 8.5 6.4 1.1 0.2 11 4.4 55 88 2 Midijim 11 8.8 2.0 0.7 9.6 12 68 112 2 Palypa 9.9 7.9 0.9 1.1 14 3.6 69 106 2 Tapo 11 8.6 1.4 0.3 11 11 65 109 2 Tyol 10 9.3 2.7 2.1 12 6.6 80 123 2 Youour 13 8.5 1.9 0.4 9.3 13 70 116 2 C.V. % 3 5 2 3 2 1 3 a AA- Spectrophotometer b ICP-AES analyses c Calculated value: HCO3 ' (meq) = Xcations (meq) - (SO42' (meq)) Average Coeff. Of Variation (C. V.) of all Samples <3% 4 Samples collected after local rain event 17
Both the Gum and Gogol rivers were sampled before and after an intense evening rainstorm; TDS on the Gum R. decreased by half (100 vs. 50 mg L’1) as a result of increased discharge following the storm, whereas Gogol R. TDS remained essentially the same (120 vs. 125 mg L’1). The decrease in TDS concentration on the Gum R. is likely due to dilution, which is a characteristic response for rivers when discharge increases, although not every ion will exhibit an inverse relationship to increased discharge
(Walling, 1984). The greater influence of this rain event on the Gum was also indicated by the fact that river was considerably higher after the rainstorm compared to the Gogol, whose much larger basin area may have mitigated some of the effects of the heavy rains.
The annual dissolved loads of the rivers were calculated by the product of inorganic solute concentration and estimated annual water discharge, according to the equation:
Load (T yr’1) = Concentration (T km’3) * Discharge (km3 yr'1) (1)
Water discharge was calculated from the following equation:
Discharge (km3 yr’1) = Runoff (km yr'1) * Basin Area (km2) (2)
where runoff was calculated from precipitation minus evaporation. The rivers were estimated to receive 4 m of rainfall, of which 2 m are evaporated (Baumgartner and
Reichel, 1976, Robinson, 1976). This value of 50% total evaporation to total 18
precipitation agrees with independent studies of tropical rain forest hydrology
(Schlesinger, 1991).
Calculated annual loads of TDS ranged from 8,000 T yr'1 to 635,000 T yr'1 (Table
3, Figure 3). This large range was primarily a function of the two-order-of-magnitude
range of basin areas of the sampled rivers, which produces a large range in discharge
(Equation 2). The dissolved yields of the sampled rivers, load normalized to basin area,
ranged from 130 to 400 T km'2 yr'1 (average = 280 ± 66 T km'2 yr'1) (Table 3), and when
combined with data from larger wet mountainous rivers, displayed a slight, but
statistically significant (p < 0.001), inverse relationship to basin area (Figure 4).
ORGANIC CARBON
Dissolved Organic Carbon
The concentrations of dissolved organic carbon in the nine rivers ranged from
0.58 to 4.6 mgC L '1 (average = 1.63 mgC L '1 ± 1.38) (Table 4). The lowest
concentrations (0.58 mgC L'1) were found in the mountainous rivers and higher
concentrations (2-4 mgC L"1) were found in the Gogol and the lowland river, the Gum.
The concentration of DOC after the rain event decreased by half. The coefficient of
variation of duplicate samples was <5.5%.
Particulate Organic Carbon
The concentrations of particulate organic carbon were all lower than the corresponding DOC values and ranged from 0.07 mgC L'1 to 0.91 mgC L'1, averaging
0.37 mgC L'1 ± 0.28 (Table 4). As with the DOC samples, the higher POC values were found in the Gum R. and the Gogol R., generally with lower values occurring in the mountainous rivers. The POC concentration increased in the Gum River after a heavy 19
rain event; a similar but smaller increase was observed in the Gogol River. The
coefficient of variation between cores of the same filter was less than 10%.
The 813C analyses of five of the POC filters ranged from -32%<> to -22%<> (Table
5). The radiocarbon ages of the same filters ranged from 350 to 2690 years before
present (B.P.). The youngest POC age (350 yr. B.P.) was from the Gum River (basin
area =158 km ), a lowland river, after a heavy rain; before the rain the age of the
sampled POC was 790 yr. B.P.. The oldest age was from the Youour River (2690 yr.
B.P.), a mountainous river with a basin area of 160 km2. POC from other mountainous rivers all had ages greater than 1300 yr. B.P. 20
TABLE 3. Dissolved Loads and Yields of Papua New Guinea Rivers Sampled River Load Yield (T y r 1) ( T k m - V 1) Boku 7,310 332 Gogol 624,000 264 Gogols 651,000 275 Gum 34,800 220 G um i 17,400 110 Kabenau 54,300 193 Midijim 60,000 246 Palypa 10,700 232 Tapo 6,600 236 Tyol 16,200 270 Youour 40,800 255 ♦ Samples collected after local rain event. 21
Figure 3. TDS load versus basin area for sampled PNG rivers. Note the strong linear correlation (r2 = 0.97, m = 0.95, p < 0.001). The point deviating from strong linear correlation is the Gum R. when it was sampled after a rain event. River Area(km ) 22
FIGURE 4. PNG small mountainous rivers and SE Asian wet mountainous rivers TDS yield, y = -0.086 + 2.53. Slope is significantly different than zero (p < 0.001). Note the difference in slope and significance from Figure 1. Data was analyzed with both MODEL I and MODEL II regressions with no difference between the results, therefore MODEL I was used to generate this data.
Data from rivers > 10,000 km2 (black circles) is from Milliman & Farnsworth, in prep. TDS yield (T/km^/yr) SE Asian and PNG Wet Mt RiversMtPNGWet andSE Asian • Other Wet Mountainous RiversMountainous Wet Other • □ Sampled PNG Rivers PNG Sampled □ ai ra(m ) Basin(kmArea
2 106 TABLE 4. Concentrations of DOC and POC of the PNG rivers sampled. River DOC a P O C b (m gCL1) (mgCL-1) Boku 0.93 0.23 Gogol 1.05 0.74 Gogol ♦ 3.98 0.91 Gum 4.60 0.22 Gum ♦ 2.02 0.56 Kabenau 0.58 0.59 Midijim 0.73 0.27 Palypa 0.85 0.15 Tapo 1.41 0.07 Tyol 1.03 0.14 Youour 0.74 0.25 aAverage Coeff. Of Variation < 5.5 % bAverage Coeff. Of Variation < 10 % ♦ Samples collected after local rain event 24
TABLE 5. A14C and 5"C13, for river POC. River Basin area §13C 3 Fraction A14C b 14Caged modern0 (km2) (7oo) ( °/oo) (yrs B p ) Boku R. 22 -22.3 0.782 -218 1980
Gogol R. 2366 -27.4 0.816 -183 1630
Gum R. 158 -25.9 0.906 -94 790
Gum R. ♦ 158 -27.1 0.957 -43 350
Kabenau R. 280 -24.9 0.850 -150 1300
Youour R. 160 -32.6 0.716 -285 2690
3 Standard error of 513C: + 0.01% b Standard error of A14C: + 5.2 c Standard error of fraction modern: + 0.0052 d Standard error of 14C age: + 55 yrs. ♦ Sample collected after local rain event 25
DISCUSSION
REPRESENTATIVE DATA
Basing a discussion of TDS export on the results from a single sampling period is
dubious at best, particularly for rivers in a monsoon-dominated climate. To some degree,
however, this problem can be minimized by comparing the sampling period with monthly
rainfall recorded at the Christiansen Research Institute, a biological research station
located on the coast 5 km northwest of Madang. Rainfall, and therefore river runoff in
New Guinea rivers, varies according to the Southern Oscillation Index, with runoff
decreasing markedly during El Nino years. While total annual rainfall at the Christiansen
Research Institute between 1990 and 1996 varied by about 50 percent (2.4 to 4.0 m y r1),
the range of rainfall during May and June between 1990 and 1997 was considerably
greater, 52 to 760 mm in May and 14 to 327 mm in June (Table 6). May 1997 saw
greater than normal rainfall at Madang (the fourth highest monthly total in the 1990's),
but much of this probably occurred during the early parts of the month, as rainfall during
June was substantially below normal (45.7 mm) (Figure 5). River conditions during the
third week of May therefore were probably about average for the falling hydrograph or perhaps slightly below the eight-year mean depicted in Figure 6. However, for the sake of this discussion, the assumption is that the May 1997 samples were collected at mean flow, and therefore the measured concentrations may represent median TDS values for the nine rivers.
In humid and wet rivers, dissolved solid concentrations remain relatively constant throughout the annual cycle, increasing during low-flow periods and decreasing during maximum discharge (Walling, 1984). Suspended solid concentrations, in contrast, are 26
greatly dependent on the flow regime, often increasing orders of magnitude during peak
flow. As a result, the majority of TSS export occurs during highest runoff, often during
infrequent flood events, whereas TDS export occurs more evenly throughout the year.
Other supporting evidence for the representativeness of the data in the present study
comes from additional sampling of the Palypa and Gogol rivers in January 1997 and
March 1998. The results from these water samples, collected as part of an environmental
impact study conducted by an Australian engineering company (I. Hargreaves, 1998,
verbal and written communication), allowed the comparison of the May 1997 falling
hydrograph data with those collected during pre-monsoon (January) and peaking flow
(March) periods.
These data, collected as the El Nino was beginning to wane in the western
Pacific, show that TDS values in the Palypa River were roughly twice as high in January
and March (217 and 188 mg L'1) compared to May 1997 (108 mg L’1), whereas the
Gogol River values remained relatively constant throughout the two sampling periods
(Table 7). The greater constancy in the Gogol may reflect the fact that it is roughly two
orders of magnitude greater in basin area, thus moderating temporal and spatial
variations. Nevertheless, a factor-of-two difference in concentration for the Palypa River
is probably less than the roughly order-of-magnitude variation in river discharge (Figure
6, based on rainfall noted in Table 6), suggesting that dissolved solid export from these
small rivers is probably more a function of river flow than TDS concentration. These
data, plus the fact that the May ’97 data were collected during approximately mean flow,
suggest that the one-time sampling can be assumed to represent mean TDS values for these nine rivers. \0 VD ON rH P co (^- (N Ti — r j | \ <3 i- m \f >n vo oo on H m U 1—( o O VO r - ON O CN CO in OO p ON 00 V© P TsJ- CO CO in co Q ' 1 * VO CN r- oo ON vo o > r - ON o CN ON ON CO rr CO CO i n ON (N CO t> o © u VO ON ON m i n VO 1—I vo pC CO CO r - VO co U < P a> pC p VO P CO i n P oo CO m P i n 0 " ON i—( NO p in rk as P NO CO CO i n CO NO p r^- m TJa> c-> NO P CN v o ON p CN NO i"k ja> P o ON 1—1 oo CO o OO CN 0© CO CN CO CO CO CN CN CN © as i__ ( ■w Figure 5. Monthly rainfall in Madang, PNG collected at Christiansen Research Institute, 1990-1997. Rainfall (mm) 200 400 600 - - - ^ > c ^ ^ ' S ’97 ’96 ’95 ’94 ’93 ’92 ’90 29 Figure 6. Estimated monthly river discharge of sampled PNG rivers and TDS data from rivers with more than one sample month (i.e. Gogol and Palypa R.). Note: These discharges were calculated from rainfall data collected at a coastal site. The mean rainfall was 3.1 m, 0.9 m less than estimated from the Atlas of World Water Balance (1977) used to calculate annual river discharge. The discrepancy between the two values is attributed to the difference in elevation between the headwaters of the sampled rivers and the site of rainfall collection. MONTHLY PNG RIVER DISCHARGE (m3/s) 1000 n 240 - 220 100 - - 200 CO 180 JZ 160 140 TDS concentration (mg/l) 1 - 120 0.1 100 & . o ' r S • KABENAU MONTH o TYOL V MONTH vs BOKU V MIDIJIM ■ YOUOUR □ PALYPA O TAPO O GUM ▲ GOGOL 0 PALYPA R. TDS [ ] • GOGOL R. TDS [ ] 30 TABLE 7. Supplementary data. March 1998 was an El-Nino year, which causes drought-like conditions in the South Pacific. The discharge was therefore lower and TDS concentrations most likely higher than under typical March (monsoon) conditions. River- Ca 2+ Mg 2+ Na+ K+ S i0 2 SO/' h c o 3 Sum TDS Sampling month Gogol R.- May 1997 14 8.3 0.7 1.3 13 2.6 83.0 123 March 1998 21 5.5 7.8 1.9 * 5.7 92.0 135 Palypa R.- January 1997 35 8.3 14 1.9 * 3.9 171 217 May 1997 9.9 7.9 0.9 1.1 14 3.6 69.0 108 March 1998 31 7.5 11 2.3 * 13 121 188 All units mg L 1. • Values not available. • Data from Hargreaves, 1997-1998. 31 MAJOR INORGANIC SOLUTES Dissolved Solid Concentrations TDS and Ca/Mg Concentrations of major ions and organic carbon in these small rivers can provide insights regarding the geology of the river basin, and terrestrial primary production | within the watershed. The concentrations of major ions in all of the samples (111 mg L' , avg.) are similar to the world average of 120 mg L'1 (Figure 7), with significantly lower concentrations (50 mg L"1) found in the Gum R. after a rain event. Dissolved cations in most rivers are dominated by calcium, often 3 to 10-fold greater than magnesium (Livingstone, 1963; Schlesinger, 1991). The average Ca2+/Mg2+ ratio of sampled PNG rivers is 1.33 ± 0.22 (mass ratio); in contrast, the Ca2+/Mg2+ ratio for larger basin area PNG rivers is 10 (Petr, 1983). The low Ca2+/Mg2+ ratios in these small rivers may reflect the abundance of volcanic rocks, magnesian carbonates and dolomites in the river basins. Other rivers in the area that drain the Finesterre Range display similar low Ca2+/Mg2+ ratios, and rivers proximal to the study site also display Mg2+ as the dominant cation, with Ca2+/Mg2+ ratios as low as 0.07 (Hargreaves, 1998). The low ratio of Ca2+/Mg2+ in area rivers further attests to the local lithological influence on the chemical concentrations in the sampled rivers. 32 Figure 7. Comparison of TDS concentrations in rivers sampled in present study to the world average of 120 mg L'1. Error bars denote the summed standard deviation of inorganic solute analyses in each river. Samples collected after local rain event. TDS Concentration (mg L'1) 160 VER E IV R 33 Importance of Lithology in determining TDS Values A long-standing controversy with river geochemists revolves around whether climate (Berner and Berner, 1997), litho logy (Edmond et al, 1996) or a combination of the two plays the dominant role in determining river chemistry. The data from small PNG rivers indicate that lithology, rather than climate, is the most important variable in controlling the solute concentration for small rivers. Because of the short length and moderately high flow rates, the residence times of surface water in these small PNG rivers may be only a matter of hours or a few days, and only readily soluble minerals will dissolve to release ions to the rivers. The warm temperatures and vegetation help accelerate chemical weathering reactions, but the rate acceleration is still limited by the kinetics of the reaction between the water and the rock. The dissolution of carbonates and evaporites involves simple congruent reactions, and has the potential to yield a high amount of ions to surface waters even with short contact time between rainfall and bedrock. A similar study in SW India also examined the dissolved material of small mountainous rivers. These small rivers had an average TDS concentration of 39 mg L 1 (Bajpayee et al., 1999), which the authors attribute to insufficient contact time between rainfall and catchment rocks due to the rivers’ steep gradients and short lengths. However, given the high TDS in the studied PNG rivers, with similarly high relief and short length, the low concentrations in SW India may reflect more the primary lithology drained by these rivers, that is Precambrian igneous and metamorphic sialic rocks, in addition to their hydrological dynamics. 34 An unknown but perhaps significant factor affecting the TDS load in the PNG rivers may be the contribution of groundwater baseflow to total surface flow. Groundwater input, particularly in a karst topography (as PNG most likely has), could reflect in a longer residence time of water and thus explain the higher TDS concentrations. This possibility is suggested because the TDS concentrations in the rivers are high, in spite of the short residence time of surface water in these small basins. Concentrations in the Boku R. and Tapo R. (148 and 109 mg L'1), for example, are similar to the Gogol R., even though they are two orders of magnitude smaller in basin area (22 and 28 km2 vs. 2366 km2). The unknown groundwater contribution adds a degree of uncertainty to the interpretation of the data, because the concentration of dissolved solids exclusively from surface water runoff is not determined. Nonetheless, the main conclusion that the lithology signal dominates the river chemistry in the sampled rivers does not change. The chemical susceptibility to weathering of watershed bedrock is the dominant factor controlling the dissolved load in this weathering-limited system. 35 DOC and Chemical Weathering The low concentrations of DOC provide evidence of low net ecosystem production (NEP) in these watersheds. NEP is defined as gross autotrophic production minus total respiration (Odum, 1969). The primary production in tropical rainforest ecosystems is known to be amongst the highest in nature (Nkounkou and Probst, 1987), and the lack of organic matter accumulation in tropical soils reflects rapid remineralization by soil microbes (Berner and Berner, 1996). Thus the high rates of primary production presumably are balanced by high rates of respiration on land, which minimizes the accumulation of organic matter content in the soils. The DOC exported by rivers therefore can be thought of as an estimate of NEP since, other than respiration, export to the river is the primary sink for DOC. In addition, high rates of respiration within watershed soils provide the hydrogen ions that facilitate chemical weathering reactions, e.g.: CO2 (from respiration) + H 2O —> H2CO3 —» H+ + HCO3 (3) This observation supports the hypothesis that chemical weathering of the dominant lithologies controls the high TDS export from wet small mountainous rivers in PNG. Petr (1983) also found high rates of chemical weathering in five large rivers of Papua New Guinea (Table 8). 36 Table 8. Inorganic solute data from major PNG rivers. (Petr, 1983) River Ca2+ Mg2+ Na+ K+ SO 42 HCO3 Sum TDS (all mg L'1) Ramu 6 3 3 1 5 45 63 Vailala 12 2 4 1 4 42 65 Fly 16 1 2 0.5 0 56 76 Sepik 13 3 30.5 6 57 83 Purari 21 3 3 1 2 81 111 Kikori 37 4 1 0.3 1 125 168 37 Dissolved Load Dissolved load is the product of TDS concentration (T km'') and3 discharge (km' 3 1 I yr' ). Because the sampled rivers are assumed to have similar runoff (2 m yr‘ ), and because TDS concentrations vary by a factor of two (75 - 147 mg L'1, mean =111 mg L'1), the calculated TDS load of the sampled PNG rivers is largely controlled by basin area (Figure 3). Comparison of two small US west coast mountainous rivers, the Chehalis River (basin area = 3300 km 9, runoff = 1 m yr' ) Iand the Santa Clara River (basin area = 4100 km2 , runoff = 0.01 m yr' ),1 with the Gogol River (basin area =2366 km , 2-1runoff = 2m yr" ) highlights the factors that control a river’s TDS load and export. The Chehalis River, a wet river draining the Cascade Mountains in southern Washington, has an annual load of 0.05* 106 T yr'1, whereas the Santa Clara, an arid river in southern California, has a similar annual dissolved load of 0.06* 106 T y r1; in comparison, the Gogol’s annual load is an order of magnitude greater (0.6* 106 T yr'1). The similarity between the TDS load of the Chehalis and the Santa Clara belies the fact that the former has much lower TDS concentration (11 vs. 895 mg L '1), but a much higher discharge (3 vs. 0.12 km3 yr'1) (Figure 8). The Gogol River, in contrast, has both high TDS concentration (123 mg L'1), and high discharge because of high rainfall. Because the flows of the Chehalis and the Gogol are continuous, both rivers have weathering-limited erosional regimes (Stallard and Edmond, 1983). The contrast in dissolved export, therefore, reflects primarily the difference between metamorphic and igneous sialic rocks eroded by the Chehalis compared to the soluble carbonates and extrusive mafic and intermediate igneous rocks in the Gogol watershed. In contrast, the 38 FIGURE 8. Top Chehalis R., bottom Santa Clara R. Note the similarity between the TDS loads of the rivers, but the difference in discharge and TDS concentration. The Chehalis discharge pattern is much more regular than the Santa Clara, and is the result of relatively constant rainfall. The Santa Clara River is event driven. Data from Alexander et al. (1996) Discharge (m^ s~l) and TDS load (g s"l) Discharge (m^ s" *) and TDS load (g s 'l 0 - 102 „G A A A VA °V o o o a O bJ) 39 transport-limited Santa Clara, has high TDS concentrations but very low annual discharge, such that weathered material, both dissolved and particulate, is generated at rates faster than it can be removed. Thus, while discharge and lithology both play important roles in TDS load, rivers having both high discharge and erodable lithologies must be considered as particularly important. Global ramifications of this observation will be discussed in a following section. Dissolved Yield The dissolved yield of wet mountainous rivers increases slightly with decreasing basin size, in contrast to the marked increase in suspended solid (TSS) yield (Figs.l and 4). The inverse relationship between suspended yield and basin size results both from the decreased storage capacity of small rivers (Milliman and Syvitski, 1992) and the greater response of small rivers to major flood events (Milliman and Warrick, in prep.). The significant difference between the slopes of TDS and TSS yield against basin area indicates that their export is controlled by different processes, i.e. chemical vs. physical constraint (Figure 1). The reason for the increase (albeit small) in dissolved yield and basin area is more difficult to explain, since at some critical basin size reduced sediment storage and shorter residence time of river water should result in lower dissolved yield (Milliman, 1997). In fact, TDS concentrations do not decrease dramatically in the PNG rivers relative to much larger rivers, as indicated by the fact that the average TDS value for these rivers (111 mg L’1) is extremely close to the global average for wet mountainous rivers (120 mg L"1). The reason that the yield increases for these smaller rivers thus may be related to 40 the very high runoff of these rivers. For example, runoff of the PNG rivers (at 2 m yr’1) is about twice that for the Amazon River. One might then ask why the TDS concentrations are so high in these small PNG rivers - and why the TDS concentrations are about as high in very small Boku and Tapo rivers as the much larger Gogol River. To some degree this maybe related to the dissolution kinetics of the environment, the wet tropical mountainous climate providing optimal conditions for chemical weathering and also limiting the build-up and accumulation of thick regoliths (Berner and Berner, 1997). This suggestion is supported by the fact that lowland rivers generally have lower TDS concentrations because the low hydraulic gradient results in extensive soil horizons that limit chemical weathering products from directly entering surface flow; in addition, soil absorption capacity is greater in thick regoliths (Berner and Berner, 1997). Kinetics alone, however, may not explain the high TDS concentrations observed in rivers such as the Boku and Tapo, since their very short length (less than 10 km) means that surface flow presumably can last no longer than a few hours once the water reaches the rivers or their tributaries. A more complete explanation for such high concentrations may require another factor, most likely groundwater flow, which would permit subsurface flow to remain within the catchment for longer period, and thereby accumulating greater concentrations of dissolved solids. Because groundwater generally serves as the base flow for rivers, the groundwater input would be greatest during lower hydrologic stages, surface runoff becoming more important during higher discharge events (Walling, 1984). The relative role and importance of groundwater flow to these small rivers, however, remain undetermined. 41 Annual TDS Export to the Ocean To estimate the contribution of small, wet mountainous rivers to the annual TDS export to the global ocean, an existing classification was augmented with data from various world rivers and data the present study. Rivers were tabulated according to annual runoff, air temperature, and relief, in accordance with Meybeck’s (1988) morphoclimatic division of world rivers. There are 12 divisions using this method (Table 9), and rivers used for the analysis were chosen based on the criteria provided for each division (Table 10). The dissolved solid concentrations of the rivers were averaged in each of the twelve categories and multiplied by the percentage of the world runoff of 32,350 km3 yr'1 (Meybeck, 1988)that the category contributed to the ocean. Using the sum of the dissolved load values from the twelve categories, the total export of dissolved ions was estimated at 39.4 * 10 14 g yr' , 1 similar to estimates of Garrels and MacKenzie (1971) (36 * 1014 g yr'1) and Meybeck (1976, 1977) (37.6 * 1014 g yr'1). The sampled rivers in Papua New Guinea are included in the wet tropics highland group (Figure 9). The other rivers in this category are larger ones, so the contribution of small rivers exclusively is not determined with this exercise. However, the wet tropics highland group contributes 28% of the annual dissolved solid load to the oceans, and 24% of the annual water discharge, yet occupy 8% of the total land area that discharges to the ocean. Water discharge alone, however, does not drive the high export of dissolved ions from this class of river. For instance, the Amazon R., the largest river in the world, discharges 18% of the annual freshwater export to the oceans, but only 8% of the annual 42 TABLE 9. Examples of rivers and regions for each morphoclimatic division * Climate Regime Example rivers and/or regions TAIGA and TUNDRA Russia; Yukon, Nelson & MacKenzie R. HUMID TAIGA Finland, Quebec ; Fraser R. WET TAIGA British Columbia, Iceland, Norway SEMI-ARID TEMPERATE Turkey, Ukraine TEMPERATE Mississippi R.; Poland, Latvia, Romania HUMID TEMPERATE St. Lawrence, Columbia R. ; Sweden WET TEMPERATE (highlands) Brahamaputra R.; New Zealand, lapan ARID Orange, Rio Grande & Nueces R. SAVANNA Ghana, Cote d’Ivoire, Gambia HUMID TROPICS Irwaddy & Salween R.; Malaysia WET TROPICS (lowlands) Orinoco R., Guyana WET TROPICS (highlands) Papua New Guinea; Mekong R. * Data from Meybeck & Ragu (1995) & Milliman & Farnsworth (in prep.) TABLE 10. Morphoclimatic division of rivers based on runoff, air temperature, and relief rn Tt O Tf NO O ro < O oo «K t> O -*1 t o NO in -H os B MM MM Q £ w -2 H 1 1 s H O m m O c n h 2 in m < > < z Z -31 < O' O rq OO ON n O t m < in S\ NO ON Oi < m n o H M O O ^ ^ H H C Cm mm U m W JS oo O' < Tf cf"^ r<> O o c4 f T * -3 jx w fO Figure 9. Morphoclimatic division of annual dissolved load to the ocean. See Table 9 for examples of regions and rivers for each climate category and Table 10 for physical descriptions of the divisions. % Global Total Global Morphoclimatic Budget of TDS Load TDS of Budget Morphoclimatic Global 20 - 25 30 10 - 15 - 5 - COLD DRY -> TEMP.DRY -» WET TEMP. -> WARM DRY -> WARM WET WARM -> DRY WARM -> TEMP. WET -» TEMP.DRY -> DRY COLD 1 S ■ j 1 , ii % Discharge % % land area land % % TDS export TDS % CLIMATE * & r i i l 45 dissolved solid load. The wet tropics highland rivers are unique in that they receive large amounts of annual rainfall (runoff > 0.63 m y r1) and are located in a geologically active area, resulting in geologically young (and thus erodable) substrate. In addition to the age of watershed bedrock, the primary lithologies in the wet tropic highland watershed are typically marine carbonates, interspersed with extrusive igneous rocks, rocks that are readily susceptible to chemical weathering. The lithology and hydrology of the wet tropical basins combined with their inability to develop thick soil horizons creates a high TDS export, seemingly independent of basin area. ORGANIC CARBON POC Isotope Data The S13C values of the POC indicate a terrestrial source of organic matter, although there is a considerable range in the concentration, from -32°/00 to -22 °/00 (Table 5). This range may represent the mixture of different types of plant material, i.e., of C 3 and C 4 plants. Plants with a C 4 pathway are commonly grasses, and have a 513C signature ranging from -17 7 00 to -9 7 00, with a mean of -13 7 00 (Boutton, 1991). The majority of terrestrial plant species are C 3, with a 8 i3C signature ranging from -32 °/00 to - 207oo, averaging -2 1 °/00 (Boutton, 1991), although C 4 plants may be significant components of plant communities, primarily in warm, arid or semiarid environments (Osmond et al., 1982). Thus, it is likely that the 5I3C signal found in the sampled PNG rivers is a mixture of the two plant types, as grasses were observed along the riparian zone in most of the sampled rivers. 46 The radiocarbon dates of the riverine POC (Table 5) indicate that the organic material has had centuries to millennia to degrade in the terrestrial environment. Few studies have examined the age of POC in river systems, but the few rivers studied are mountainous ones, and are on opposite ends of the spectrum both in terms of basin area and results. Hedges et al. (1986) found organic matter (coarse and fine fractions) in the Amazon R. to be enriched in ‘bomb carbon’, indicating a rapid (i.e. decadal) turnover of the organic carbon pool there. In contrast, a small mountainous river in Taiwan, the Lanyang Hsi, exports POC with radiocarbon ages >10,000 yr. B.P., which the authors attribute to kerogen weathering (Kao and Liu, 1996). The ages of the POC of the sampled PNG rivers relates to the presumption of low net ecosystem production discussed earlier in the paper. With the low DOC concentrations found in the sampled rivers, despite high rates of primary production in the watershed, it seems that the primary production is balanced approximately 1:1 by respiration. The old POC attests to this, as it seems that the younger, more labile organic matter (i.e. perhaps as DOC) is respired. The POC remaining in the watershed, and eventually being exported to the river, is older, recalcitrant plant material that is not metabolically efficient for bacteria to respire (Opsahl and Benner, 1997). The more refractory POC remains in the watershed, perhaps sorbed to mineral grains (Keil, et al, 1997), and is eventually exported to the river, where it may mix with younger plant material that was eroded before it was respired. The POC sample from the Gum R. after the rain event may represent a mixture of older recalcitrant POC and fresh plant material, thus causing the observed decrease from 790 to 350 yr. B.P. between the two samples. CONCLUSIONS The conclusions from the data obtained in this study are that basin area, a proxy for sediment residence time, does not affect total dissolved solid concentration or load in the sampled wet small mountainous rivers. However, it is impossible to ascertain how much of the surface flow of the sampled rivers is affected by groundwater baseflow, and therefore the possibility if a basin area control on TDS concentration for other small rivers is left undetermined. Lithology of the watershed is considered to be the most important variable controlling the dissolved load of small rivers. The data from this project fill the noticeable gap in dissolved data from the world rivers. Small rivers, basin areas less than 10,000 km2, have been neglected in the global data set, and this project provides evidence that they are important contributors of dissolved constituents to the global ocean. Their class of river exports 28% of the annual global export of 39 * 1014 g yr-1 of dissolved ions. And based on the TDS yield data, small mountainous rivers contribute a disproportionately high amount of inorganic solutes to the global ocean. 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Included among these are data from proximal PNG rivers (documenting the high concentration of Mg2+), the in-situ suspended matter of the rivers at the time of sampling, the measured pH and alkalinity of the samples, and the raw dissolved inorganic carbon data. 53 APPENDIX Table A -l. In situ suspended sediment for PNG rivers sampled in this study. River TSS Boku 10 Gogol 3 3 GogoU 6 Gum 5 Gum* 8 5 3 Kabenau 3 8 Midijim 281 Tapo 6 Tyol 58 Youour 4 7 units: mg/L Table A-2. TDS data from rivers draining Finesterre Range, Madang Quadrangle, PNG. CM + O River (0 Mg2+ Na+ K+ cr h c o 3' SO42' Aik. Ramu R at Brahnman 26 8 15 0.97 1.7 185 1 0 152 Ramu R at Sepu 29 8 11 0.95 1.5 144 8.9 118 Ramu R at Ainome 2 0 9 8.4 0.81 2 . 2 137 6.9 1 1 2 Maruru R 9 8.4 1.9 0.49 0.46 74 2.4 60 Anagri River 16 16 5.7 0.36 7.2 127 4 104 Banap Creek 1 . 2 17 0.45 0.09 0.21 90 1 . 2 74 Brahman ORWB 36 8 . 2 2 0 2 . 2 1.3 187 5.5 153 Banap Oxbow 2.3 17 0.96 0.12 0.38 98 1 81 Keranguru Lagoon 18 4.7 4.3 0.66 0.45 8 8 0 . 8 72 Palypa R 35 8.3 14 1.9 3.9 171 14 140 Nuru R 36 1 0 43 1.4 15 204 44 167 units: mg/L, alkalinity units: mgCaC03/L from Hargreaves, 1998 Table A-3. pH values of sampled rivers River pH Boku 7.33 Gogol 7.11 Gogol ♦ 7.22 Gum 7.05 Gum ♦ 6.69 Kabenau 7.21 Midijim 7.18 Palypa 7.09 Tapo 7.13 Tyol 7.35 jYouour______7.26 ♦ Sampled after a rain event. 54 Table A-4. Measured alkalinity of sampled rivers by acid titration, and estimated corresponding bicarbonate values. Compare to measured bicarbonate values in Table A-5, and [HCO3 ] based on mass-balance in Table 2. River alkalinity HC03' mgCaC03 umol Boku 210.5263 4127.967 Gogol 159.4595 3126.656 Gogol* 163.1579 3199.174 Gum 126.3158 2476.78 Gum* 47.36842 928.7926 Kabenau 118.9189 2331.744 Midijim 144.1176 2825.836 Palypa 128.5714 2521.008 Tapo 188.8889 3703.704 Tyol 176.3158 3457.172 Youour 158.3333 3104.575 Table A-5. Dissolved inorganic carbon raw data. ' _l _ ‘5 -J OC ML 00 LO CM CO CO O ^ 'N - r ^ > 0 cr 3 3 E S > 2 ) l f L L LLI LLI LLI LLI UJ O 7— K 00 d o o d r- o r- 00 CO o o i— o ■M" CD O O O O 00 O CO T— o o 00 CD 00 O O O O O CD LO d DCD CD OC MCM CM CO CO OLO CO O M ■M"-M- M O■M"■M- CO DCM CD LO - r 1— o •'vf ■^f OCM CO LO ■M" d CD 00 CD CM CMCD 00 MCO CM O CMCM I-. 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Graduated from The Williams School, New London, Connecticut, in 1990. Earned a B.S. in Geological Sciences and Environmental Studies from Tufts University, in Medford, Massachussets, in 1995. Entered the Master’s program at the College of William and Mary, School of Marine Science in 1996.