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Chemical weathering in granitic environments
Chemical Geology 202 (2003) 225 – 256 www.elsevier.com/locate/chemgeo
Chemical weathering in granitic environments Priscia Oliva *, Je´rome Viers, Bernard Dupre´ LMTG (UMR 5563/OMP)-CNRS, IRD, UPS, 38 Rue des 36 ponts, 31400 Toulouse, France Received 2 November 2001; received in revised form 1 August 2002; accepted 2 August 2002
Abstract Factors controlling chemical weathering in granitic environments are deduced using an extensive database from the literature. Based on a transition state theory model, we evaluated the effects on chemical weathering of runoff, temperature and pH. The dependence between temperature and runoff, and chemical weathering is complicated by other factors such as the presence/depth of soil cover. Soil cover favors chemical weathering when it is thin and rich in weatherable minerals. In contrast, it reduces chemical weathering when is thick and poor in primary minerals. Processes that increase the contact time and the surface of the water – rock interactions such as physical denudation, increase chemical weathering rates in granitic environments. The effect of temperature can be quantified assuming an apparent activation energy value of 48.7 kJ/mol to describe chemical weathering of silica in granitic crystalline environment under high runoff conditions. D 2003 Elsevier B.V. All rights reserved. Keywords: Chemical weathering; Small watersheds; Granitic environment
1. Introduction At present, the scientific community faces important environmental problems concerning climate change (Ledley et al., 1999) and human impacts on the Earth ‘‘global ecosystem’’ (e.g., greenhouse effect and climate warming, air, water and soil pollution). Chemical reservoirs at the Earth’s surface (atmosphere, continent, biosphere, ocean) are tightly coupled and an understanding of these new environmental questions requires global study of the Earth surface. Many studies have focused on the impact of global change at the Earth’s surface, particularly within the
* Corresponding author. E-mail address: [email protected] (P. Oliva). 0009-2541/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.chemgeo.2002.08.001
global CO2 cycle in relation to the greenhouse effect or climate warming (Berner et al., 1983; Volk, 1987; Schwartzman and Volk, 1989). At a geological scale, two major processes intervene to control the atmospheric CO2 balance (Volk, 1987). The first is CO2 emission to the atmosphere, mainly from volcanic activity. The second is CO2 consumption by silicate weathering. Chemical weathering of silicate minerals produces HCO3 which is then transported by rivers to the oceans. HCO3 precipitates in the oceans to form carbonates, the net result being the removal of CO2 from the atmosphere. In contrast, carbonate weathering has no influence on the long-term atmospheric CO2 balance. Assuming (1) the increase of the Earth’s mean surface temperature is related to an increase of greenhouse gas (CO2, CH4. . .) concentrations in the atmosphere, and (2) increasing temperature enhances silicate weathering rates, atmospheric CO2 regulation
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must exist (Walker et al., 1981; Berner et al., 1983; Volk, 1987; Brady and Carroll, 1994). Specifically, increasing concentrations of atmospheric CO2 leads to an increase of CO2 consumption by silicate weathering (White et al., 1999). Due to its great variability and importance, the silicate weathering rates need to be better constrained to produce a viable model of atmospheric CO2 evolution (Volk, 1987; Brady, 1991). Weathering rates are strongly related to lithology (Garrels and MacKenzie, 1971; Meybeck, 1987; Bluth and Kump, 1994). Evaporites are more easily weathered than carbonates, and carbonates are more easily weathered than silicates. Climatic factors also play a key role. The main factors are temperature and runoff and/or precipitation amount. The effect of soil cover could also be of major importance; a soilcovered system has hydrological and physical properties (e.g., water residence time, high contact surface area between water and fresh minerals) that could play a strong role in chemical weathering. Thick soil profiles were used (Stallard, 1985) to explain the low chemical weathering rate of tropical rivers draining the Amazonian plains and equatorial African regions (Gaillardet et al., 1995; Edmond et al., 1995). Contradictory effects were found, however, concerning the role of vegetation. On the one hand, mineral dissolution should be enhanced by a low pH environment close to the root system (e.g., due to nitrification processes for example) and by the presence of organic acids resulting from plant biodegradation (Keeney, 1983; Heyes and Moore, 1992; Drever, 1994). On the other hand, vegetation protects the soil from physical erosion, and thus favours the development of thick soils, which isolate parent rock from weathering. Numerous studies have investigated the effect of acid precipitation on small watershed ecosystems (Wright et al., 1988; Sverdrup and Warfvinge, 1995; Probst et al., 1992). The effect of acid deposition on chemical weathering rates is still unclear and long-term studies are required to better evaluate the impact of such pollution (Probst et al., 1995). Other factors, poorly studied, could be important, such as frost and the partial pressure of CO2 (Ohte et al., 1995) which has changed during geological time. Many studies have considered chemical weathering in basaltic environments (e.g., Gislason et al.,
1996), particularly for the study of the CO2 cycle. Weathering rate laws (Dessert et al., 2001) explore the roles of temperature and runoff in the control of the basalt weathering rates. However up to now, no chemical weathering rate laws have been proposed for granitic crystalline and granitoids rocks. Among the different potential factors governing chemical weathering, White and Blum (1995) proposed that the effect of temperature dominates while others proposed that the effect of runoff dominates (Millot et al., 2002). White et al. (1999) considered both to be significant. Knowledge of chemical weathering rates laws for granitic environments have a great importance; as granitoid rocks cover f 25% of the land surface of the mean upper crust, the weathering of granites may have a major impact on the CO2 cycle. Both large- and small-scale studies have been focuses on granitic weathering rates. 1.1. Large fluvial basin studies Studies carried out on large rivers allow estimates of element fluxes from the continents to the oceans ( + 6, Summerfield and Hulton, 1994; Edmond et al., 1995; Dupre´ et al., 1996; Boeglin and Probst, 1998). By considering the ‘‘60’’ largest rivers in terms of water discharge, we account for more than 80% of the water draining the continents (Gaillardet et al., 1999). Note, however, that large rivers drain varied lithologic and climates which prevent a rigorous study of the factors controlling chemical weathering. Great advances were made in the past two decades to define chemical weathering rate laws based on large river chemistry. Using an inverse method (Ne´grel et al., 1993; Gaillardet et al., 1995), it is possible to calculate for each river the respective contribution of the different end-member lithologies (evaporite, carbonate, silicate) to the water chemical composition. This normalization is necessary to separate chemical fluxes due to silicate weathering from those due to evaporite and carbonate weathering. In a review of large-scale studies, Gaillardet et al. (1999) proposed that the second most important parameter controlling chemical weathering after lithology is runoff. These authors reveal also observed positive correlation between physical and chemical erosion for silicates. This correlation illuminates the role of the soil in the weathering of acidic crystalline
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
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rocks. When mechanical erosion prevents the formation of thick soils, waters interact directly with fresh crystalline rocks. In contrast, thick soils reduce these interactions resulting in a lower chemicalweathering rate. These systems are referred to as ‘‘transport-limited’’ (Carson and Kirkby, 1972; Stallard, 1985).
effect the particulate fraction. In large rivers, due to storage effects, the export of particulate matter may not reflect primary production from bedrock. This is another strong argument against comparing of the particulate matter load delivered by small and large basins.
1.2. Small watershed studies
2. The small watershed database
Small watersheds are well-defined hydrological areas and have been studied to better understand local ecosystem function (Likens and Bormann, 1995). Small watersheds offer the possibility to better constrain the parameters controlling chemical weathering. Other information can be obtained by comparing the chemistry of different small watersheds. White and Blum (1995) studied the chemistry of 68 small watersheds located in crystalline environments, and defined the relation between chemical weathering and climate. They found that both precipitation and temperature affect chemical weathering rates and that they are more significant than the role of topography and physical erosion. In contrast, a recent study comparing river chemistry in Canada with other small granitic watersheds (Millot et al., 2002) shows that chemical weathering is related to runoff and physical erosion rates. Small catchments are limited generally to the basin head, and located in more mountainous zones. Although large- and small-scale studies should be complementary, their comparison is quite difficult. Large watersheds are mainly located on alluvial flood plains and contain both depositional and weathering zones. In contrast, small watersheds are more representative of a single weathering environment. Another complication is that water and particulate matter fluxes are more difficult to quantify in small watersheds. This is principally due to role of flood events in small watersheds. Each flood influences significantly the annual mass balance of water, solutes, and particulate matter in small watersheds. In contrast, large rivers are much less affected by this phenomenon. In addition, in contrast to small watersheds, large watersheds generally have large alluvial plains constituted of sediments that are hydrologic reservoirs. Colluvial, alluvial and lacustrine formations do not have a large influence on the dissolved fraction behavior, but do
2.1. Sources of data The White and Blum (1995) database is the starting point for this work. It includes small watersheds located in granitic environments and was used to study the effect of temperature on chemical weathering. The present database is based on a critical review of the data included in the White and Blum (1995) database plus additional data taken from the literature. We selected watersheds for which precise information on the lithology (granitoids, gneiss, schists), topography, hydrogeology and pedology of the catchment, and the hydrochemistry of the streams and precipitation are available. A variety of environments were included in the database. Owing to the lack of studies in warm and humid environments we choose to add recent data (Bajpai, in preparation) from Southern Indian watersheds (i.e. 100,000 to 600,000 ha) which are not affected by urban and/or agricultural anthropogenic effects. Note that the Nsimi watershed (Oliva et al., 1999) is located in an intracratonic tropical province; this type of province represents about 1/3 of the total emerged surface. Warm and dry regions are not represented in the database. Studies considered in the database are very diverse. Some are longer-term studies (sampling weekly or monthly) of variable duration, whereas others are more or less punctuated studies consisting of one unique to only several samples. Considering our interest in using annual fluxes to estimate weathering rate laws, long-term studies are most useful. However, we also considered samples from short-term studies for which information on the variability of chemistry at an annual scale is available. Physical and chemical characteristics of the different watersheds in the database are given in Appendices A and B. Calculated concentrations and fluxes are provided in Table 1.
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Table 1 Calculated cation, silica and total dissolved solids concentrations and fluxes used in the different figures No. Watershed
Precip. Runoff T (mm) (mm) (jC)
pH Si Cations Cations Cations + (Amol/l) (Amol/l)a (Aeq/l)a Si (Amol/l)a
Qcations QSi (mol/ (mol/ ha/an) ha/an)a
1 2 3 4 5 6
1751 583 2146 2146 2146 4541
380 252 1010 1240 1040 3668
5.4 126 4.5 96 6.7 98 6.7 75 6.7 70 6.4 42
79 17 76 77 82 31
121 20 117 123 134 57
205 113 174 152 152 73
479 243 989 930 728 1534
268 34 815 1089 907 1714
412 37 1243 1716 1470 2844
20 7 52 60 49 98
804
278
2.4 6.0 191
156
244
347
530
292
455
23
804
383
2.4 6.0 132
87
127
219
506
224
316
21
804
287
2.4 6.0 143
103
162
246
411
120
190
15
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
Nsimi Exper. Lake Haney A.B.C. Haney B.B.C. Haney C.B.C. Jamieson Ck. B.C. Rawson lake E. Ont Rawson lake NE. Ont Rawson lake NW. Ont Slave Provinceb Greenville Provinceb Hartviko Lysina Salacova Lhota Vocadlo Liuhapiro Yli-Knuutila Estibe`re Latte Margeride Strengbach Barhalde Schluchsee Ilambalarib Cherakkobbanmalab Anamalai Hillsb Sivagirib Tarangamakanamb Pulachimalaib Pasukidamettub Matsuzawa Tsukuba Iu Birkenes Botnane Breidvikdalen Dyrdalen Kaarvatn Langtjern E1 Langtjern E2 Langtjern E3 Sogndal 1 Sogndal 3 Storgama Juiadalec
701 1016 685 736 766 750 1400 1806 1000 1477 2000 2300 3800 3400 2300 3200 3100 3600 2600 1672 1587 1945 1637
2252 876 876 876 984 984 1192 1220
24.0 2.4 9.2 9.2 9.2 3.4
Qcations (eq/ha/ an)a
Qcations + Si (kg/ha/ year)a
117
4.0 6.9
3
272
494
276
4
317
576
10
556
4.5 5.9
50
98
162
168
387
546
904
28
297 224 245 127
33 157 49 530 61 32 112 33 115 126
42 240 37 861 93 75 204 55 179 207
330 381 294 657
321 909 313 217
763 557 1255 1139 1189
92 298 23 48 285 242 181 263
120 519 446 803 518 448 315 456
490 380 616 441 381 313 431
188
234
546
3875 1284 1384 1411 3175 2019 1683 2433 2580
82 1066 407 2026 436 312 1675 775 1988 2811 1596 2570 10,489 4518 6594 4674 10,268 4827 4565 4169 2801
11 48 16 44
180 78 293 246
111 700 277 1233 286 174 877 477 1354 1800 1174 1942 6021 2563 3681 2568 5544 2773 2631 3554 2110
40 10 11 19 21 37 34 35 10 7 18 147
59 17 16 38 30 68 60 63 17 14 32 164
1049 267 397 629 423 271 263 258 145 84 268 565
1389 432 593 1265 597 467 438 441 214 155 420 629
108 406 128 171 480 198 1125 1246 705 952 1400 1974 2019 1013 822 902 2291 1533 1000 936 721 1790 1310 2560 3712 3305 1899 576 576 576 875 875 923 385
6.0 5.0 6.5 6.5
6.8 4.0 7.1 6.2 6.1 6.1 5.0 7.6 6.1 11.0 9.0 6.1 3.9 5.0 27.0 7.1 27.0 7.2 27.0 6.9 27.0 7.1 27.0 6.3 27.0 7.1 25.0 6.9 12.6 6.5 13.1 6.9 14.0 5.8 4.6 7.5 4.7 7.5 4.7 7.5 5.3 5.2 7.0 3.1 4.7 3.1 4.6 3.1 4.5 5.8 5.7 7.1 4.5 13.8
64 45 178 120 85 192 127 168 156 139 132 168 260 358
16 20 245
26 27
141 177
392
942
54 29 72 89 67 281 105 142 112 231 132 117 164 128
8 7 41
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
229
Table 1 (continued) No. Watershed
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
Rusapec Swartboskloof b Montseny Mtns Ticino lakeb Dantersta Lilla Tivjon Solmyren Storbergsba¨ckenc Vuoddasbacken Ciste Mhearadb Dargall Glendye Green Burn White Laggan Albany. Wyoming Andrews Creek. WA Bear Brook. ME Brier Creek. GA Cadwell Creek. MA Caribou P. Creek LoP. ALc Caribou P. Creek HiP. ALc Como creek. CO Coweeta 2. NC Coweeta 18. NCc Coweeta 34. NC Dry. VAc Emerald lake. CA Falling Creek. GA Filson Creek. MN Fort River. MA Halfmoon Creek. GA Holiday Creek. VA Hubbard Brook. NH Loch Vale. CO ALoch Vale. CO Log Creek. CA Martinell. CO Merced river. CA Mundberry Brook. MA Mt Moosilauke. NH Old Rag Mountain. VA
Precip. Runoff T (mm) (mm) (jC)
pH Si Cations Cations TDS (Amol/l) (Amol/l)a (Aeq/l)a (Amol/l)a
922 2920 870 2138 610 623 621 542 621 2000 2861 1375 2707 2822 720
88 1215 404 1797 115 203 348 247 383 1735 2464 1178 2135 2185 430
17.1 251 16.0 9.0 7.5 6.2 90 0.3 6.4 2.0 0.3 0.2 81 0.3 5.0 5.2 42 6.0 5.2 60 6.4 6.5 119 6.0 5.8 72 6.0 5.8 46
247 71 224 111 346 192 163 88 112 31 22 78 36 35 119
296 81 266 176 592 350 287 144 181 38 39 128 67 69 182
1650
480
7.0 7.6 170
310
1400 1140 1429
881 557 793
5.0 5.8 55 13.1 6.7 4 7.0 5.8 139
480
167
3.5
465
152
3.5
770 1771 1813 2009 1450 1830 1220 680 1080 1000
Qcations Qsi (mol/ (mol/ ha/an) ha/an)a
498
222
201
1617
169
201
73 82 198 108 81
730 1489 1403 1539 1004
519
480
86 76 59
142 99 98
124
374
131
123 854 955 955 400 1410 300 270 507 400
1050 1300
350 800
1104 1587 876 1295 1400 1429
604 1127 373 1507 665 562
2400
2400
1.5 5
1147
395
9.0 7
Qcations (eq/ha/ an)a
QTDS (kg/ha/ year)a
218 863 1018 1992 596 365 613 216 471 630 1674 2079 1469 1512 511
262 984 1258 3163 955 671 1067 356 759 761 2231 2880 2209 2349 784
816
1404
2389
70
141 80 166
485 24 849
730 377 464
1215 489 770
38 11 37
697
498
208
625
1164
27
333
611
464
199
505
929
23
6.8 11.7 6.9 151 11.0 6.9 10.6 6.9 139 167 6.0 6.0 28 16.0 7.4 320 3.5 5.0 204 8.4 6.1 165 3.0 7.5 95
60 35 66 78 17 715 134 78 416
73 45 92 110 26 1168 237 161 779
210
1287 1330 668 392 960 552 837 380
714 429 937 439 390 3888 603 792 3311
51
205 245 45 1035 339 244 511
574 334 669 314 253 2437 340 376 1787
56 28 19 98 25 35 69
14.0 6.8 200 5.0 4.9 74
171 87
253 137
371 161
700 590
754 635
1094 1007
41 36
6.3 25 6.9 46 7 131 6 27 7 100 7 146
38 61 219 43 54 136
63 105 313 69 88 222
64 106 350 69 154 281
154 515 486 401 665 820
257 831 772 635 922 793
424 1429 1100 1028 1317 1290
13 43 37 32 47 43
33
49
780
1176
89
119
258
346
9.0 7.2 0 12.0 7.0
156
245
615
12
93
12 37 88 97 86 73
24
(continued on next page)
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Table 1 (continued) No. Watershed
Precip. Runoff T (mm) (mm) (jC)
87
1450
400
1149 1170
338 720
933
169
1132 4300 1120 2040
88 89 90 91 92 93 94 95 96 97 98 99
Old Rag Mountain. VA Panola. GA Panther Lake. NY Pond Branch. ML Rabbit Ears. CO Rio Icacos. PR Silver Creek. ID South Cascade. WAc Tallulah river. GA Tarps Creek. CA Tesque Aspin. NM Tesuque Conifer. NM Woods Lake. NYb
pH Si Cations Cations TDS (Amol/l) (Amol/l)a (Aeq/l)a (Amol/l)a
Qcations Qsi (mol/ (mol/ ha/an) ha/an)a
Qcations (eq/ha/ an)a
QTDS (kg/ha/ year)a
158
41
47
199
632
166
187
22
15.3 5.0 6
200
111 113
159 201
311
676
425 958
601 1679
30
12.3
242
162
222
404
409
274
376
19
617 3680 437 2040
0.7 22.0 7 4.2 7
107 219 291 8
93 141
142 217
200 360
580 7552
880 10,820
36 443
54
95
62
659 8066 1270 171
1091
1932
46
1820
1160
12.0 7
140
79
116
219
1624
1146
1646
80
876
219
7.2 7
106
154
217
260
233
283
394
15
946
639
5.0
131
155
945
1169
1149
829
2.5
142
206
1230
1802
1230
760
5.0 5
53
93
101
199
Data from White et al. (1999) (Malaysia and United States) Sq. Lawing Sg. Lui Sg. Batu Sg. Selangor Sg. Batamgso Sg. Selangor Rasa Sg. Changkak Gem lake Fall river Big Thompson river Spruce Creek Fern Creek North St. Vrain Creek Boulder Brook
1180
25.0 25.6 24.3 25.6 25.0 25.6
304
2310 2490 2910 2930 3120 3590
1430 267 426
25.0 5.0 0 0
4 76 53
6110 61 204 224
611 739 536
0 0 0
46 45 64
280 333 341
251
0
153
384
814 924 1180
306 315 248
Qcations* were calculated by using Eq. (6), or Eq. (7) when precipitation data were not available. The data from Millot et al. (2002) were previously corrected for eolian deposits. Temperature values in italic were found on the WEB. a The weathering fluxes ( QSi, Qcations and QTDS) and the concentrations (Si, cations and TDS) are corrected for atmospheric inputs. b Sea-chloride corrected values. c Concentration values are deduced from given fluxes.
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
2.2. Parameters controlling chemical weathering rates Chemical-weathering rates are listed in Table 1. Chemical weathering fluxes are generally defined as the difference between fluxes leaving (export fluxes) and entering (precipitation fluxes) the water system (White and Brantley, 1995), and can be computed from: Elementary fluxes from chemical weathering ¼ export fluxes precipitation fluxes
ð1Þ
Eq. (1) is simplified because it does not consider cation exchange or biomass. For watersheds located in areas where precipitation is not strongly acidic, cation exchange is considered to be at steady state (Drever and Clow, 1995). A similar assumption can be made for the biomass if no vegetation modification (forest fire, human or natural deforestation, animal grazing, etc.) is occurring. This simplified relation is realistic if the ‘‘precipitation fluxes’’ term encompasses all atmospheric inputs including bulk precipitation, dry deposits and throughfall. In most cases, only bulk precipitation fluxes were measured which underestimate the true fluxes entering the watershed. The calculation of the net output fluxes due to chemical weathering will depend strongly on the atmospheric input term if it is similar in size to the exportation fluxes. Unfortunately, studies which compare dry and wet deposition, or which take into account throughfall deposition are scarce (Lindberg et al., 1986). Although some studies consider total atmospheric deposition (e.g., Feller, 1977; Cronan, 1980; Pace`s, 1986), large uncertainties are associated with estimates of dry deposition and throughfall. One way to account for total atmospheric deposition is to make a chloride (Cl) correction. This method is based on the assumption that (i) all Cl comes from the atmosphere and (ii) Cl behaves as a conservative element in the watershed. This correction accounts for dilution and evaporation processes, but neglects element fluxes due to biomass removal or dry deposition. On the other hand, as bulk precipitation composition may vary strongly from one sample to the next (Viers et al., 2001), data obtained from periodic sampling could underestimate or overestimate atmospheric input. In the present database, precipitation chemistry
231
data are very heterogeneous (see above). Therefore, to make the data as consistent as possible, two types of atmospheric corrections were performed: (1) based on the precipitation data, and (2) using marine values. Thus, two types of elementary weathering fluxes appear in the database: those corrected for precipitation and those corrected for sea salt (see paragraphs devoted to the calculation of chemical weathering fluxes). Transition state theory, provide framework to evaluate the different parameters controlling chemical weathering rates at a global scale. The following dissolution rate equation was proposed by Lasaga et al. (1994): ni RW ¼ k0 S eEa=RT anHHþ þ j ai i
ð1 e
DGr=RT
Þ
ð2Þ
RW represents the mineral dissolution rate expressed in mol per unit mineral surface per time. Each term of the relation will be explained below. . In Eq. (2), k0 is the ‘‘kinetic’’ rate constant of dissolution which is characteristic of each mineral. Although we have selected watersheds with granitic lithology, numerous differences exist among the rocks in the various watersheds. These differences which include mineralogy, granulometry, crystal defects and fracture density could effect strongly chemical weathering rates. These differences are defined as best as possible in the database (Appendix A). . S is the reactive surface area of the mineral. Chemical weathering increases with increasing S, which should be related to the watershed area (Appendix A). The surface area between the rock and the solution is difficult to estimate and can be affected by watershed history. The soil characteristics including the nature, thickness, porosity and hydrological pathways, etc., through the soil column are parameters that could strongly influence S. For example, a glaciated watershed may favor chemical weathering due to glacial abrasion, which increases the surface contact between rock and water. The ‘‘Glacial deposits’’ term in the Appendix A indicates the presence of glacial features including moraines and glacial till. . The e Ea/RT term corresponds to the temperature effect term. Ea is the activation energy which depends on pH and mineralogy (Lasaga et al., 1994), R is the
232
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
gas constant, and T is the temperature in Kelvin. This relation indicates an increase in the weathering rate with increasing temperature. Average annual temperature (Appendix A) representativity could be questioned, particularly for cold environments such as high mountain systems where temperature could have important daily variations. The mean annual temperatures of the watersheds in the database are listed in Appendix A. . The 1 e DGr/RT term accounts for the effect of the approach to equilibrium on a mineral dissolution rate. This term should be related to the water –rock interactions in the field, and particularly to the water residence time and the rock – water ratio. Finally, it may correspond to annual precipitation and runoff (both expressed in Table 1 in mm/year). Annual precipitation corresponds in the majority of the studies to a long-term average, but in certain cases (indicated), it is one to a few years average. Runoff is the surface water (river, stream) which effectively leaves the watershed. Deep-water circulation is generally neglected in small-scale studies (Drever and Clow, 1995). Thus, the difference between precipitation and runoff corresponds to the evapotranspiration term. Runoff values in the database are of two origins. Some correspond to data given by the authors, others are calculated from discharge data and surface area. These calculations could have large uncertainties. In all cases, large uncertainties remain, inherited from the database with annual runoff values from various origins (calculated from evapotranspiration, taken from other studies, etc.). Unfortunately, this adds to the inherent variability of the runoff parameter (Peel et al., 2001), particularly within high-elevation systems where it can change substantially within a short period (Peters and Leavesley, 1995) due to the variability of precipitation event or within deciduous forested cover, which can decrease evapotranspiration during the dormant period (Peel et al., 2001). Fortunately, the majority of the watersheds considered have coniferous or tropical rainforest cover. The presence of a glacier in a catchment could also be responsible for considerable variations in daily hydrologic and chemic exportations (Anderson et al., 1997). The presence of glacial deposits at the lowest level in the soil profile of glaciated watersheds could have an influence in chemical weathering by favoring drainage and contact time with the parent rock.
.
The other terms such as the proton and the ligand activities (aH+nH+, ji ani i ) are related to the concentration of the natural solutions (Appendix B). The effect of human activities on silicate weathering should be considered here. Crystalline environments are generally drained by dilute waters (low ionic strength) which are very reactive face to atmospheric acid deposition. In the database, we kept watersheds subject to acid rain to see if they can be distinguished from the non-polluted sites (Appendix A). . Others parameters (Appendix A) should be considered with respect to field studies devoted to chemical weathering. These include altitude, vegetation, soil, and the presence of disseminated calcite. (i) Minimum and maximum altitude, expressed in meters, gives information on the topography (relief, slope). (ii) Vegetation can have contrasting influence on chemical weathering processes: on one hand, biomass degradation under the influence of bacteria releases elements to the soil and delivers organic acids favoring mineral dissolution and rock disintegration; on another hand, plants take up nutrients (e.g., Ca, Mg, K, N, P, C) from the soil and protect soil from physical denudation. Generally, authors considered that watersheds are at steady state with respect to the vegetation element (uptake = release) and have neglected the vegetation parameter. However, some authors (Velbel, 1995; Likens and Bormann, 1995) have shown an effect of biomass on base cation or silica fluxes at the watershed scale. We indicate in the database those studies that appear to have non-steady state vegetation effect. (iii) Concerning the soil, we indicate in the database information about the nature and the thickness of the pedological formations when they were given by the authors. Soil reflects chemical weathering and could influence the river chemistry in various ways: by lowering the acid precipitation effect, by protecting the parent rock, or by favoring the vegetation growth. (iv) We indicate in the database, rivers supposed to drain disseminated calcite-bearing rocks because calcite dissolution could influence stream water chemistry. 2.3. Calculation of chemical weathering fluxes The calculations of chemical weathering fluxes require the precipitation chemistry, the stream water
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
chemistry and the associated fluxes of water (Appendix A). Chemical weathering fluxes computed using data provided in Appendices A and B are given in Table 1. The following paragraphs present equations used for these calculations.
233
salt (Celement, sea-chloride corrected) for the stream waters using (Ne´grel et al., 1993): Celement;
sea chloride corrected
¼ Celement;
2.4. Precipitation chemistry and fluxes The major element concentrations of precipitation from various watersheds are provided in Appendix B. In the majority of the studies, these concentrations of all major elements were measured in bulk precipitation. Only a small percentage of the studies reported precipitation chemistry including throughfall and/or dry deposition contributions. Elementary precipitation fluxes ( Qelement, precipitation) are related to precipitation, P, and element concentration, Celement, concentration, by the following relation: Qelement;
precipitation
ðmol=ha=yearÞ
¼ Celement; precipitation ðAmol=lÞ P ðmm=yearÞ=100 ð3Þ
river
ðCelement; sea=Cchloride; sea Þ Cchloride; river ð5Þ 2.7. Chemical weathering fluxes As mentioned above, two types of chemical weathering fluxes are calculated: elementary chemical weathering fluxes ( Qelement, weathering) and sea salt corrected elementary chemical weathering fluxes ( Qelement, sea-chloride corrected weathering). These fluxes are related by: Qelement;
weathering
¼ Qelement;
river
Qelement;
precipitation
ð6Þ Qelement; sea chloride corrected weathering ðmol=ha=yearÞ ¼ Celement; sea chloride corrected ðAmol=lÞ R ðmm=yearÞ=100
ð7Þ
2.5. Stream water chemistry and fluxes Major element concentrations (Celement, river) available in the literature are generally calculated averages, sometimes weighted averages for the long-term studies. Two major uncertainties remain in the database; (1) are punctuated measurements representative of the annual mean value and (2) do long-term studies minimize the impact of individual events on computed annual fluxes (Drever and Clow, 1995)? Stream chemical fluxes ( Qelement, river) are calculated in a similar manner to precipitation fluxes, by using the runoff value (R) and according to: Qelement;
river
ðmol=ha=yearÞ ¼ Celement;
river
ðAmol=lÞ
R ðmm=yearÞ=100 ð4Þ 2.6. Sea salt corrections Solute concentrations can be affected by evaporation or dilution processes. To address these effects, elementary concentrations were corrected from sea
3. Chemical weathering laws in granitic environments Chemical weathering in the present study is defined in terms of silica, major cations, and major cations + silica fluxes. Four temperature classes are defined to represent different climatic zones (i.e., cold, temperate, cool temperate and warm). These different zones are denoted by different symbols in the figures. The effects of various parameters on weathering rates are considered below. 3.1. Lithology Lithology has a strong effect on weathering rates (Bluth and Kump, 1994). Studies of the influence of lithology on weathering shows that water draining igneous and metamorphic rocks is more dilute in major cations and more acidic than waters draining more reactive lithologies zone such as serpentinite (Kram et al., 1997), mafic rocks (Gislason et al., 1996), and carbonates and evap-
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orites (Berner and Berner, 1987; Meybeck, 1987). In granitic environments, chemical weathering is dominated by the more easily weatherable minerals (Sverdrup and Warfvinge, 1995), essentially, feldspars, amphiboles, epidote, and apatite. Kram et al. (1997) observed that the chemical variability of the major cation concentrations in rivers draining silicate environments is controlled by lithology even in areas exposed to acid precipitation. In a study of chemical weathering of a high elevation crystalline basin in the Pakistan Himalayas, Blum et al. (1998) showed that the chemical weathering is strongly influenced by carbonate minerals although the comprise only 1% of the total watershed area. The connection between lithology and weathering rates is, however, complex. Watersheds containing disseminated calcite (e.g., Schindler et al., 1976; Drever and Zobrist, 1992; Mast et al., 1990; Clow and Drever, 1996) do not always have the highest calcium fluxes found for a given range of runoff and temperature. Indeed, although we have tried to eliminate the effects of lithology through a careful selection of sites in the database uncertainties remain in the mineralogical composition of those watersheds contained in the database. Consequently, it is impossible to eliminate completely the effect of lithology from this study.
3.2. Temperature One of the most important results of the White and Blum (1995) study is an observation of a positive correlation between temperature and the element fluxes, particularly for silica. The database in the present study shows an increased disparity of the silica flux vs. T correlation (R2 = 0.27) compared to White and Blum (1995) (see Fig. 1). When we look at the relation that may exist between T and Qcations or Qcations + silica, the influence of temperature on cation fluxes (R2 = 0.4) is not well established (Fig. 2). The relation between cations and/or silica concentrations (i.e., Csilica, Ccations) and temperature are also unclear (Figs. 3 and 4). This are inconsistent with the relation established by Lasaga et al. (1994) between Silica concentration versus T data (Fig. 4) from river data compiled by Meybeck (1980). Uncertainties of reported mean annual temperatures could mask a potential relation between temperature and weathering. Nevertheless, the data in our database suggest that the high temperature watersheds are even more inconsistent with this relationship. On one hand, despite of the disparity of the data, it is important to note that the highest silica or cations fluxes are obtained for warm watersheds (Rio Icacos and some Indian watersheds). On the other hand, for the same temperature, rivers from India, Rio Icacos
Fig. 1. Silica fluxes expressed in mol/ha/year versus temperature (jC) for all the selected watersheds (the present database and the East United States and Malaysian samples from White et al., 1999).
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
235
Fig. 2. Total dissolved cation fluxes expressed in mol/ha/year versus temperature (jC). *The weathering fluxes are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction.
and Nsimi exhibit a range for the flux of cations which is larger than the range obtained for all the remaining rivers regardless of temperature. Moreover, this range fluctuates between very low measured fluxes (Nsimi), which are comparable with low-temperature environment values, to the highest measured fluxes (Rio Icacos). Considering the previous considerations, it is evident that temperature
alone does not control chemical weathering in granitic environments. High-temperature points can be divided in two groups; the first corresponds to the humid tropical watersheds (i.e., Nsimi, Juiadale, Swartboskloof) and the warm temperate watersheds from eastern United States (i.e., Panola, Holiday Creek) which contain thick soil cover. The second corresponds to the
Fig. 3. Total dissolved cation concentrations (Amol/l) versus temperature (jC). *The concentrations are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction.
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Fig. 4. Silica concentration (Amol/l) versus temperature (jC) for all the selected watersheds (the present database and the eastern United States and Malaysian samples from White et al., 1999). A linear relation between silica concentration and temperature, previously determined by Lasaga et al. (1994) using the Meybeck (1980) data is also shown in this diagram.
remaining points. In Fig. 2, the first group of samples, particularly the Nsimi watershed, exhibits Total Dissolved Cation fluxes lower than those of the other watersheds. The existence of thick soil covers in tropical environment masks the temperature dependence of silica and cationic fluxes. Thick soil covers protect the primary rocks from chemical weathering. A similar conclusion was reached by Carson and Kirkby (1972), Stallard (1985) and Gaillardet et al. (1999). 3.3. Runoff Fluxes and concentrations of cations, cations + silica, and silica versus runoff are plotted in Figs. 5 – 8. In each plot, the watershed temperature class is indicated. Fig. 5 indicates a decrease in Si concentration with runoff regardless of temperature class. For a given runoff, it seems that high temperature watersheds exhibit the highest silica concentrations. Moreover, it seems that the highest concentrations are for the lowest runoff ( < 1000 mm/year) at all the temperatures. The same type of relation is
observed for considering major cations concentrations (Fig. 6). This is in agreement with White and Blum (1995). However, low runoff and high-temperature samples such as the humid tropical samples of the Nsimi (Oliva et al., 1999; Viers et al., 2000), Swartboskloof (Britton, 1991) or Juiadale (Owens and Watson, 1979) watersheds exhibit a Total Dissolved Cation concentration that is lower than would be expected considering the other high-temperature points. This could be explained by the thick soils typical of tropical environments. Thick soils are generally depleted in major cations because they are mainly composed of secondary minerals such as clays (e.g., kaolinite) and oxy-hydroxides (e.g., gibbsite and goethite), and primary phases which weather slowly (e.g., quartz, heavy minerals). These minerals release silica, aluminum, and iron during chemical weathering (Nahon, 1991). Considering the draining role of the surficial soil formations, the released of those elements should follow the hydrological variations. In contrast, major cations are released during deeper weathering of the parent rock which is poorly related to the hydro-
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
237
Fig. 5. Silica concentration (Amol/l) versus runoff (mm/year) for all the selected watersheds (the present database and the eastern United States and Malaysian samples from White et al., 1999). The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend.
logic network. However, it is important to note that the Nsimi watershed (Oliva et al., 1999; Viers et al., 2000) sample does not really confirm the silica versus runoff relation and presents one of the lowest silica concentrations (f 125 Amol/l) for all the
selected high-temperature watersheds despite its low runoff. This may be related to the particular functioning of this watershed (Oliva et al., 1999), with kaolinite recrystallisation in the swamp zone and important silica recycling by plants.
Fig. 6. Total dissolved cation concentrations (Aeq/l) versus runoff (mm/year). *The concentrations are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction. The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend. The humid tropical watersheds and the warm temperate watersheds from eastern United States are indicated.
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Fig. 7. Silica flux expressed in mol/ha/year versus runoff (mm/year) for all the selected watersheds (the present database and the eastern United States and Malaysian samples from White et al., 1999). The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend. In the upper left corner, we plot the same data using a bilogarithmic diagram.
Figs. 7 and 8 present plot of silica and cations + silica fluxes versus runoff, respectively. An increase of the silica fluxes with runoff for high-temperature watersheds is evident in Fig. 7, although there is no clear relation between silica fluxes and runoff for the other temperature classes. In contrast to what is reported by White and Blum (1995), we do not observe one linear correlation but two distinct tendencies: (1) the strongest correlation (R2 = 0.88) is
that given by high-temperature watersheds and which is located above, (2) the majority of the other points which show only weak correlation when considered by temperature class. High-temperature watersheds show a rapid evolution of silica fluxes with runoff. The same tendencies are obtained when the cationic fluxes or cations + silica fluxes (R2 = 0.74 for T > 13 jC) are plotted as a function of runoff (Fig. 8). Are those two distinct trends between warm watersheds
Fig. 8. Total Dissolved Solid (Si + Na + Mg + K + Ca) fluxes (kg/ha/year) versus runoff (mm/year). *The TDS fluxes are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction. The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend.
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
and the rest of the data (temperate and cold samples) the result of a temperature effect? If we look at the figure that presents the silica concentrations corrected for atmospheric input versus T (Fig. 9) for the two distinct classes of runoff ( < 1000 mm) and (>1000 mm), we observe two patterns. (1) At low runoff ( < 1000 mm), there is a weak relation between silica concentration and temperature (R2 = 0.3). (2) At greater runoff (>1000 mm), silica concentrations increase more closely with temperature (R2 = 0.68). Temperature dependence is only visible at high runoff. At low runoff, other parameters, such as the presence of an important soil cover, prevail over temperature. We suggest that (i) effective contact time between solution and minerals and (ii) solution/minerals contact area are two important parameters effecting chemical weathering processes. In general, the more water passing through the watershed, the more con-
239
tact time and surface at the water –rock interface be favored. However, at the watershed scale, this is complicated by different types of water flows: (1) shallow flowpaths (surface flow) are rapid and do not favor chemical weathering at the water – rock interface and (2) deep flowpaths (base flow) are slow, penetrate deeper within the soil cover and thus favor water – rock interactions. In the humid tropical zone, soil thickness constrains the reactive fluid circulation to zone depleted in major cations and limit the contact between solution and weatherable minerals (Stallard, 1985). In contrast, in high runoff system such as Rio Icacos, important physical erosion limit the development of soil cover and thus favor contact surface and time between water and parent rock. In cold environments (generally high elevation systems in the database), slopes and/or the high porosity of the thin soil cover could induce a low contact time between minerals and solutions
Fig. 9. (A and B) Silica concentrations (Amol/l) versus temperature (jC) for (A) watershed with low runoff ( < 1000 mm/year) and (B) for watersheds with high runoff (>1000 mm/year).
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
(Meybeck, 1987). However, in alpine environments having high runoff, the persistence of an important snow cover during winter is known to favor the waterlogging of surface formations (e.g., soil, glacial deposit, etc.). Several authors (Brady, 1991; White and Blum, 1995; Dessert et al., 2001) proposed an equation to describe chemical weathering fluxes as a function of temperature and runoff. This equation is given by: Qelement;weathering ¼ Runoff C0 exp½Ea=Rð1=T 1=T0 Þ
ð8Þ
With C0 the element concentration at T0, a reference temperature expressed in Kelvin. Ea is an activation energy and R the gas constant. Because a coupled temperature and runoff dependence is apparent for watersheds with high runoff, an activation energy of chemical weathering of granitic environments is only computed for those watersheds. We used the relationship between silica concentration and temperature (Fig. 9) to quantify this activation energy. By plotting the logarithm of the dissolution rate versus the reciprocal of the temperature factor in Kelvin, we obtain the apparent activation energy of the overall reaction. Fig. 10 shows an Arrhenius plot for watersheds with runoff above 1000 mm/year. The linear
relationship between the two parameters can be expressed as: LogCsilica ¼ ðEa=R Ln10Þ1=T þ b
ð9Þ
The b term represent the intercept of the linear regression ( = 10.78). The calculated activation energy Ea for silica export in high runoff granitic watersheds is 48.7 kJ/mol, which close to the value of 51.3 kJ/mol calculated by White et al. (1999). Note, however, that silica fluxes may be strongly influenced (Alexandre et al., 1997) by biological processes (e.g., diatoms, phytolites). As only consider high runoff watersheds in our calculations, the calculated Ea of 48.7 kJ/mol may be representative of the effects of runoff and temperature on granitic rock weathering. 3.4. Other parameters According to transition state theory, the proton or aqueous activities of elements may influences chemical weathering rate. We found no correlation between elements concentrations and chemical fluxes. The effect of pH on weathering rates can be assessed with the aid of Fig. 11. Cation concentrations and fluxes show no clear correlation with the pH in small granitic watersheds. However, it seems that the greatest cation fluxes are obtained
Fig. 10. Logarithm of silica concentration versus 1/T (jK) for watersheds with runoff values >1000 mm/year. An apparent activation energy of 48.7 kJ/mol was calculated using the slope of the linear regression indicated in the diagram.
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
241
Fig. 11. Cation concentrations (Aeq/l) and cation fluxes in eq/ha/year (in the upper left corner) versus pH values. *The concentrations and fluxes were corrected for atmospheric inputs. If the precipitation values were not available, we made a sea-chloride correction. The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend.
for watersheds having higher pH. This may be related to the consumption of proton by chemical weathering reactions. It remains difficult, however to evaluate the effect of the proton or element activity on the chemical weathering because rivers water composition may be very different from that of the soil solutions in which weathering occurs. Stream water chemistry results from several processes and may not reflect directly water – rock interactions. The effect of acid deposition on chemical weathering is still unclear. Wright et al. (1988) studied the effect of acid rain and its reversibility in small granitic watersheds in Norway. They showed that stream water chemistry rapidly changes as a result of acid deposition. These changes were characterized by a net increase in aluminum, sulfate, calcium, and magnesium export. They proposed that the 50% increase of Ca+ and Mg+ fluxes in an acidified catchment resulted from the leaching of the exchangeable soil fraction (2% of the exchangeable fraction exported in 4 years) and an increase in chemical weathering rates. They also showed that the effects of acid rain are reversible; the stream
water recovered its initial chemistry rapidly after stopping acidification. In contrast, Driscoll et al. (1989) found, in a multidisciplinary study, that despite a decrease in acid deposition since the 1970s, silica concentrations remained the same in stream water. Sverdrup and Warfvinge (1995) concluded that the effect of acidification on chemical weathering is low. Note a pH decrease could increase the aluminum concentration in soil solutions which would inhibit silicate dissolution rates (Oelkers and Schott, 1995a,b). A study carried out on the Strengbach catchment (Probst et al., 1992, 1995) showed that the effect of acid deposition on chemical export can only be evaluated over the long term. In the present study, we see no evidence of an effect of pH on weathering rates at the global scale. Fig. 12 shows an example of cation fluxes vs. runoff, where acid rain-affected samples are indicated by filled points. The chemical weathering rates of glaciated watersheds are important to consider because hydrological fluxes in those environments are among the highest in the world. Anderson et al. (1997), in a review, compared the chemical weathering rate for several
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Fig. 12. Cation fluxes (eq/ha/year) versus runoff (mm/year). The lowest diagram uses a bilogarithmic scale. *The fluxes were corrected for atmospheric inputs. If the precipitation values are not available, we made a sea-chloride correction. Empty points correspond to watersheds affected by acid impactions.
glaciers with different types of substratum. One of the main conclusions of this study is that, despite high runoff, glaciers do not have as high a chemicalweathering rate as was expected, compared to nonglaciated areas. The effect of the presence of glaciers on weathering rates can be seen in Fig. 13. Only watersheds in our database within the two extreme
classes of temperature are considered. According to Anderson et al. (1997), glaciated environments do not have high chemical fluxes, compared to watersheds in warm climates. However, if we only consider cold environments (i.e., cold watershed and glacier), glaciated watersheds have higher annual solute exportations. This may be due to persistent ice cover which
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
243
Fig. 13. Cation fluxes (eq/ha/year) uncorrected for atmospheric input versus runoff (mm/year) for warm climate watersheds, cold climate watersheds (the present database) and the glacier data compiled by Anderson et al. (1997).
induces continual water flow and to the effect of physical erosion (e.g., grinding) that favors higher chemical weathering rates.
Physical erosion effects could be of major importance. Both large- and small-scale studies show that physical erosion is also closely related to water
Fig. 14. Cation fluxes (mol/ha/year) versus runoff (mm/year) for the cold climate watersheds of the present database. Filled points indicate those with glacial deposits. *The fluxes are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction.
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
flow and thus to runoff (Gaillardet et al., 1999; Millot et al., 2002). This is the likely origin of the positive correlation found between chemical weathering rate and runoff for high-temperature watersheds. Physical denudation limits soil-cover development, and thus permits more extensive water –rock interactions, particularly in the tropics. In mountainous environments, it will favor the production of fine-grained particles that accumulate in depressions leading to wetland formation (i.e., swamp, peat bog) with high porosity and available weathering surfaces. Theses zones are known to favor high chemical weathering rates (Oliva et al., 1999). Previously glaciated watershed substratum is generally composed of glacial till, which has high porosity. Watersheds known to be previously glaciated (filled points) are plotted in a log –log diagram showing cation fluxes for cold watersheds versus runoff in Fig. 14. This figure suggests little effect of the former presence of glaciers on weathering rates. The different weathering rates between active glaciers (Anderson) and ancient glaciated watersheds may be related to the duration of weathering which is not well established for natural systems. In environments with high physical erosion rates, fresh material is continuously exposed to erosion. In soil-covered systems with high runoff, one would suspect a long contact time between solution and weatherable materials, in contrast to the humid tropical environments, where the presence of thick soil cover is associated with low physical erosion. In these cases, the effect of soils or vegetation on chemical weathering rates may be secondary.
4. Conclusions Evidence presented in this study indicates that temperature and runoff are the parameters that best describe chemical weathering in granitic watersheds. However, there is no clear dependence relation between these factors and weathering fluxes are effected by other factors including water/rock contact time and the surface of the water – rock interaction. Special attention were aimed at illuminating the effects of the soil cover which (1) favors chemical weathering when it is thin and rich in weatherable minerals and (2) inhibits chemical weathering when is thick and poor in primary minerals. In optimal cases, the temperature effect can be quantified. An apparent activation energy value for silica of 48.7 kJ/mol is found to describe chemical weathering in granitic watersheds under high runoff conditions. No relation between anthropogenic effects and chemical weathering was found, and although glaciers seem to favor chemical weathering, the effect of glacial materials on chemical weathering rates is unclear. Further work is required to determine those parameters that control the reactive surface of the materials and the time contact between water and rock.
Acknowledgements We are very grateful to E. Oelkers, S. Anderson, A. Blum and two anonymous reviewers for their objective corrections and suggestions. We would like to thank J. Gaillardet, J. Schott, F. Martin, A. Probst, and C. Dessert for helpful discussions and to J. Escalier for analytical support. [EO]
Appendix A General characteristics of the selected watersheds underlain by crystalline granitic formations. In this table, the bedrock nature is indicated (chn: charnokite; dio: diorite; gb: gabbros; gn: gneiss; gr: granite; grdio: granodiorite; meta: various metamorphic rocks; mig: migmatite; mon: monzonite; msch: micaschist; qtz: quartzite; qzdio: quartzdiorite; sch: schist), the soil type, physical characteristics (surface area of the watershed, elevations), climatic characteristics (precipitation and runoff amount in mm/year, mean annual temperature in jC) and watershed particularities concerning human impacts, the presence of disseminated calcite or glacial deposits (G. deposits).
No. Watershed
Rock
Soils
Area ha
Elevation (m) max.
500
700
Oliva et al., 1999; Viers et al., 2000
chn
laterites
Allan et al., 1993
gr
acid soils
7
Feller and Kimmins, 1984 Feller and Kimmins, 1984 Feller and Kimmins, 1984 Zeman, 1975
dio/grdio
acid soils
23
145
dio/grdio
acid soils
68
dio/grdio
acid soils
gr/meta
Schindler et al., 1976
grdio
Schindler et al., 1976
grdio
Schindler et al., 1976
grdio
Millot et al., 2002
gn
117
Millot et al., 2002
chn
556
Czechoslovakia 12 Hartviko
Pace`s, 1985, 1986
gn/qtz
13
Kram et al., 1997
gr
Canada 2 Exper. Lake 3
Haney A.B.C.
4
Haney B.B.C.
5
Haney C.B.C.
6
Jamieson Ck. B.C. Rawson lake E. Ont Rawson lake NE. Ont Rawson lake NW. Ont Slave Province
7 8 9 10 11
Greenville Province
Lysina
humoferrugineous humoferrugineous humoferrugineous
acid brown soils podzol
60
min.
Precip. Runoff T (mm) (mm) (jC)
Notes
Anthropogenic effect
1751
380
24.0
583
252
310
2146
1010
2.4 glaciolacustrine deposits 9.2
235
455
2146
1240
9.2
44
295
455
2146
1040
9.2
299
304
1280
4541
3668
3.4
170
100
300
804
278
2.4 calcite
suspected
10
150
210
804
383
2.4 calcite
suspected
63
100
260
804
287
2.4 calcite
suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Cameroun 1 Nsimi
Reference
4.0 G. deposits. eolian inputs 4.5 G. deposits. eolian inputs
98
672
724
701
108
6.0
acid rain
27
829
949
1016
406
5.0 growing forest
acid rain 245
(continued on next page)
246
Appendix A (continued) No. Watershed
Reference
Rock
Soils
Area ha
Elevation (m)
557
744
685
128
6.5
acid rain
59
570
635
736
171
6.5
acid rain
165 7
42 42
92 92
766 750
480 198
700 1650 20 1150 8900 650
2350 1500 1250
1400 1806 1000
1125 1246 705
11.0
883
1146
1477
952
9.0
acid rain
1000 11 1150
3000 1250
2000 2300
1400 1974
3.9 G. deposits 5.0
acid rain acid rain
3800 3400 2300 3200 3100 3600 2600
2019 1013 822 902 2291 1533 1000
27.0 27.0 27.0 27.0 27.0 27.0 25.0
15
Vocadlo
Pace`s, 1985, 1986
gn/qtz
Finland 16 Liuhapiro 17 Yli-Knuutila
Lepisto et al., 1988 Lepisto et al., 1988
gn gn
France 18 Estibe`re 19 Latte 20 Margeride
Oliva et al., in press Probst et al., 1995 Ne´grel, 1999
podzol
21
Strengbach
Probst et al., 1995
gr gr gr/gn/ msch gr
podzol
80
Germany 22 Barhalde 23 Schluchsee
Stahr et al., 1980 Feger et al., 1990
gr gr
podzol
India 24 Ilambalari 25 Cherakkobbanmala 26 Anamalai Hills 27 Sivagiri 28 Tarangamakanam 29 Pulachimalai 30 Pasukidamettu
Bajpai, Bajpai, Bajpai, Bajpai, Bajpai, Bajpai, Bajpai,
gn/sch gn/sch gn/sch gn/sch gn/sch gn/sch gn/sch
Japan 31 Matsuzawa
32
Tsukuba
33
Iu
prep. prep. prep. prep. prep. prep. prep.
White and Blum, 1995; Shimada et al., 1993; Ohte et al., 1995 Hirata and Muraoka, 1993 Wakatsuki and Rasyidin, 1992
292,300 112,200 618,600 539,800 155,400 223,500 148,400
gr
0.5 – 5 m
gr
brown soils. 1–2 m
gr
5.0
6
190
255
1672
936
12.6
68
200
380
1587
721
13.1
1945
1790
14.0
180
suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
168
gn/qtz
in in in in in in in
Anthropogenic effect
max.
Pace`s, 1985, 1986
podzol
Notes
min.
Czechoslovakia 14 Salacova Lhota
acid brown soils acid brown soils
Precip. Runoff T (mm) (mm) (jC)
No. Watershed
Norway 34 Birkenes Botnane Breidvikdalen Dyrdalen Kaarvatn
39 40 41 42 43 44
Langtjern E1 Langtjern E2 Langtjern E3 Sogndal 1 Sogndal 3 Storgama
Rhodesia 45 Juiadale 46
Rusape
South Africa 47 Swartboskloof Spain 48 Montseny Mtns
Switzerland 49 Ticino lake
Sweden 50 Dantersta 51 52
Lilla Tivjon Solmyren
Rock
Soils
Wright and Johannessen, 1980 Skartveit, 1981 Skartveit, 1981 Skartveit, 1981 White and Blum, 1995 Wright, 1983 Wright, 1983 Wright, 1983 Frogner, 1990 Frogner, 1990 White and Blum, 1995
gr
gn/gr gn/gr gn/gr gn gn gr
podzol podzol podzol thin. acidic thin. acidic
Owens and Watson, 1979 Owens and Watson, 1979
gr
laterite
gr
Britton, 1991
Area ha
Elevation (m) min.
max.
41
200
300
340 189 330 2500
180 295 438 200
650 695 806 1375
479 181 75 963 432 60
516 516 516 900 900 580
720 720 720
Precip. Runoff T (mm) (mm) (jC)
Notes
Anthropogenic effect
1637
1310
5.8 7.5 7.5 7.5 5.2
acid rain acid rain
2252
2560 3712 3305 1899 576 576 576 875 875 923
3.1 3.1 3.1
acid rain acid rain acid rain
690
876 876 876 984 984 1192
91 1800
2000
1220
385
13.8
laterite
733 1500
1600
922
88
17.1
gr
laterite
373
320
1200
2920
1215
Avila and Roda`, 1988
sch
ranker
4
700
1035
870
404
Drever and Zobrist, 1992
gn
ranker + podzol
360
2400
2138
1797
calcite
Bergstro¨m and Gustafson, 1985 Calles, 1983 Calles, 1983
gn
31
610
115
0.3 G. deposits
suspected
1288 2700
623 621
203 348
2.0 G. deposits 0.3 G. deposits
suspected suspected
gn gn an gn/qtz
gr/gn gr/gn
7.1
16.0 growing forest
9.0
suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
35 36 37 38
Reference
(continued on next page)
247
248
Appendix A (continued) No. Watershed
53
Storbergsba¨cken
54
Vuoddasbacken
57 58
Glendye Green Burn
59
White Laggan
United States 60 Albany. Wyoming 61 Andrews Creek. WA 62 63
Bear Brook. ME Brier Creek. GA
64
Cadwell Creek. MA
65
67 68 69
Caribou P. Creek LoP. AL Caribou P. Creek HiP. AL Como creek. CO Coweeta 2. NC Coweeta 18. NC
70 71
Coweeta 34. NC Dry. VA
72
Emerald lake. CA
66
Land and Ohlander, 2000 Calles, 1983
Cooper et al., 1987 Farley and Werritty, 1989 Creasey et al., 1986 Farley and Werritty, 1989 Farley and Werritty, 1989
Rock
gr
Soils
podzol
gr/gn
Area ha
Elevation (m)
940
min.
max.
110
375
621
383
0.3 G. deposits
suspected
1250 716
2000 2861
1735 2464
5.0 6.0 G. deposits
suspected acid rain
6.4 G. deposits 6.0 G. deposits. growing forest 6.0 G. deposits. growing forest
suspected acid rain
podzol podzol
gr/gn/sch gr/gn/metam
podzol podzol
4125 250
300 225
500 716
1375 2707
1178 2135
gr/gn/metam
podzol
570
225
716
2822
2185
gr mon/qzdio
acid soils podzol
1750 5700 1311
3050 2647
720 1650
430 480
gr/gn gn/sch/metam
acid soils
msch
MacLean et al., 1999 Lewis and Grant, 1979 White and Blum, 1995 Johnson and Swank, 1973 White and Blum, 1995 Stauffer and Wittchen, 1991 Williams et al., 1993
0.2
Anthropogenic effect
247
gr gr/gn/metam
Knight et al., 1985 Mast and Clow, 2000 Norton et al., 1994 Buell and Peters, 1988 Yuretich et al., 1989, 1993 MacLean et al., 1999
Notes
542
4200
37 1030 210 225
Precip. Runoff T (mm) (mm) (jC)
suspected
acid rain
7.0 G. deposits
15 440
213 646
446 1269
1400 1140
881 557
73
165
344
1108
793
permafrost
520
300
450
480
167
3.5
suspected
msch
permafrost
570
300
450
465
152
3.5
suspected
gr gn/sch gr/gn/sch
podzol
664 2908 12 709 12 721
3559 1004 1006
770 1771 1813
123 854 955
11.7 11.0
acid rain suspected suspected
34 243
670 488
1585
2009 1450
955 400
10.6
suspected suspected
120 2800
3416
1830
1410
6.0
suspected
gn/Qzt/metam
7m
gn/sch grdio gr/grdio
35 cm
5.0 13.1 7.0 G. deposits
suspected acid rain suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
United Kingdom 55 Ciste Mhearad 56 Dargall
Reference
No. Watershed
73 74
Reference
Soils
1220 680
300 270
1052
46
380
1080
507
8.4 G. deposits
suspected
6100 2969 2200 144 3076 213
4399 280 1101
1000 1050 1300
400 350 800
3.0 G. deposits 14.0 5.0
suspected acid rain acid rain
860 3110
4010
1104
604
3300 50 2067
3300 2397
1587 876
1127 373
8 3420 46,900 1224
3560 3997 300
1295 1400 1428
1507 665 562
0 12.0 7.0
suspected
1462 1060
2400 1147
2400 395
1.5 9.0
acid rain suspected
1450
400
9.0
suspected
279 727 182 3035
1149 1170 933 1132
338 720 169 617
616
800
4300
3680
186 1395 1616
2079 2518
1120 2040
437 2040
4.2
14,600 573 13 2067
2 2255
1820 876
1160 219
12.0 7.2
gr
3 2941
3658
946
639
5.0
gr
164 2804
3444
1149
829
2.5
gn
207
728
1230
760
5.0 G. deposits
thin soils acid soils
82 83 84
Martinell. CO Merced river. CA Mundberry Brook. MA Mt Moosilauke. NH Old Rag Mountain. VA Old Rag Mountain. VA Panola. GA Panther Lake. NY Pond Branch. ML Rabbit Ears. CO
gr gr/grdio gn/sch
podzol
88 89 90 91 92 93 94 95 96 97 98
gn/qzt
gn/sch sch/gn mon/gn
acid soils podzol
gr/gn
gn/sch gr
271
1250 530
gr
282
439
gr/sch gn/charn sch mon/grdio/qzdio
41 224 124 557 38 121 200 2910
qzdio
podzol
laterite
mon mig gr/gn/sch grdio
acid soils acid soils
326
606
16.0 3.5
suspected
0
G. deposits. calcite 9.0 calcite 7.2
suspected suspected
suspected
15.3 calcite 5.0 G. deposits 12.3 0.7 G. deposits. eolian inputs 22.0 calcite
suspected suspected suspected suspected
G. deposits. calcite suspected
suspected
249
99
acid soils
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
gr/gn grdio
87
Anthropogenic effect
244
ALoch Vale. CO Log Creek. CA
Cronan, 1980 White and Blum, 1995 Stauffer and Wittchen, 1991 Peters, 1994 April et al., 1986 Cleaves et al., 1970 Peters and Leavesley, 1995 Rio Icacos. PR McDowell and Asbury, 1994 Silver Creek. ID Clayton, 1986, 1998 South Cascade. WA Drever and Hurcomb, 1986 Tallulah river. GA Mast and Turk, 1999 Tarps Creek. CA Williams and Melack, 1997 Tesque Aspin. NM White and Blum, 1995 Tesuque Conifer. NM White and Blum, 1995 Woods Lake. NY April et al., 1986
Notes
113
80 81
85 86
Precip. Runoff T (mm) (mm) (jC)
18,700 2520
79
Clow and Drever, 1996 Williams and Melack, 1997 Reddy, 1988 Mast and Clow, 2000 Yuretich et al., 1993
Elevation (m) max.
gn/gb gr/mon/gb
76 77 78
Area ha
min.
Mast and Turk, 1999 Siegel and Pfannkuch, 1984 Fort River. MA Yuretich and Bachelder, 1988; Yuretich et al., 1993 Halfmoon Creek. GA Clark et al., 2000 Holiday Creek. VA Mast and Turk, 1999 Hubbard Brook. NH Likens et al., 1977; Johnson et al., 1969 Loch Vale. CO Mast et al., 1990
75
Falling Creek. GA Filson Creek. MN
Rock
250
Appendix B Major element concentration values in precipitation and surface waters for the selected watersheds expressed in Amol/l.
No.
Watershed
Cameroun 1 Nsimi
380
T (jC)
pH
24.0 5.4
Napp (Amol/l)
Na (Amol/l)
Kpp (Amol/l)
K (Amol/l)
Capp (Amol/l)
Ca (Amol/l)
Mgpp (Amol/l)
Mg (Amol/l)
1.3
47.4
3.6
7.9
3.5
33.9
1.1
25.3
21.8 54.2 49.7 50.6 30.3 67.5 41.2 44.7 70.1 35.9
1.5 0.9 1.1 1.1 0.5 3.5 2.7 3.5
5.6 3.8 5.2 3.9 1.8 13.9 14.4 6.9 24.8 17.1
11.0 4.0 4.4 4.5 4.0 11.7 9.6 11.7
19.0 40.7 44.7 52.0 28.3 65.3 31.1 40.4 142.1 46.1
3.6 1.5 1.7 1.7 2.0 4.8 4.8 4.7
15.9 13.4 16.5 15.0 9.9 41.2 23.2 30.3 85.9 20.6
252 1010 1240 1040 3668 278 383 287 117 556
2.4 9.2 9.2 9.2 3.4 2.4 2.4 2.4 4.0 4.5
4.5 6.7 6.7 6.7 6.4 6.0 6.0 6.0 6.9 5.9
5.0 8.3 9.1 9.3 12.6 8.7 7.4 8.7
Czechoslovakia 12 Hartviko 13 Lysina 14 Salacova Lhota 15 Vocadlo
108 406 128 171
6.0 5.0 6.5 6.5
6.8 4.0 7.1 6.2
6.8 11.1 5.8 4.9
173.1 89.7 139.3 241.6
3.3 1.7 3.0 2.5
22.7 24.5 22.0 47.9
17.8 7.4 14.9 12.9
69.3 86.0 115.0 347.3
4.1 2.2 3.7 3.7
45.7 28.3 86.7 187.6
Finland 16 Liuhapiro 17 Yli-Knuutila
480 198
6.1 6.1
9.5 28.8
40.2 118.2
3.4 5.5
8.8 29.8
9.1 24.7
33.3 136.4
2.3 6.8
16.3 52.5
Strengbach
1125 1246 705 952
5.0 7.6 6.1 11.0 9.0 6.1
17.6 28.5 14.1 12.5
37.0 54.1 116.3 83.5
9.2 4.1 10.2 2.9
3.4 7.5 20.4 23.3
36.4 16.6 9.4 8.4
117.8 33.9 53.3 93.0
4.5 5.1 2.5 2.5
4.0 21.4 53.5 30.0
Germany 22 Barhalde 23 Schluchsee
1400 1974
3.9 5.0
3.7 3.2
52.8 58.6
2.6 2.3
9.9 14.4
5.0 5.9
30.3 26.3
1.4 1.8
8.9 14.4
2019 1013 822 902 2291
27.0 27.0 27.0 27.0 27.0
4 5 6 7 8 9 10
Haney B.B.C. Haney C.B.C. Jamieson Ck. B.C. Rawson lake E. Ont Rawson lake NE. Ont Rawson lake NW. Ont Slave Province
11
Greenville Province
France 18 Estibe`re 19 Latte 20 Margeride 21
India 24 Ilambalari 25 26 27 28
Cherakkobbanmala Anamalai Hills Sivagiri Tarangamakanam
7.1 7.2 6.9 7.1 6.3
127.2 140.1 202.8 122.4 157
17.9 20.2 18.2 23.1 23.8
127.9 113.9 208.4 137.3 126.9
102.4 92.3 163.1 108.7 98.6
Sipp (Amol/l)
Si (Amol/l)
Clpp (Amol/l)
Cl (Amol/l)
2.6
9.7
96.4 97.9 75.0 70.0 41.8 190.6 132.0 143.3 3.83 69.6
2.6 13.0 14.2 14.7 14.4 7.5 7.4 7.5
5.6 31.8 34.4 35.0 29.3 8.3 7.0 5.1 51.5 21.6
296.8 224.0 244.8 127.0
9.7 12.2 8.3 8.4
84.3 39.0 95.3 103.5
8.5 26.3
13.1 121.7
11.8 29.3 19.8 13.9
8.7 45.1 70.4 54.8
11.0
18.1
126
0.6
6.3 8.9 6.2
3.6 3.1 2.4
1.5
0.8
67.8 44.7 178.0 119.6
NO3 pp (Amol/l)
24.4
NO3 (Amol/l)
2.4
SO4 pp (Amol/l)
24.2 22.5 25.6 29.2
32.6
5.5 10.0 14.9 36.0
HCO3 (Amol/l)
2.4
1.3
12.9 8.9 9.6 9.9 1.5 3.9 4.0 3.9
33.7 20.5 24.8 23.7 2.6 12.5 13.4 8.8 24.5 25.1
18.1 31.7 15.9 9.0
194.4 211.0 97.7 42.1
26.6 60.9
41.0 292.4
27.0 61.4 22.4 21.5
11.0 82.7 39.1 105.5
20.0 262.3 44.5
18.6 23.2 37.6 16.3 23.4
265.1 229.1 414.7 207.9 187.2
0.8 4.4
42.3
SO4 (Amol/l)
16.2 3.3 3.6 376.5 69.1
85.0
191.9 126.7 168.4 156.4 138.6
77.8 114.5 145.7 107.6 165.7
18.6 13.9 57 42.3
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Canada 2 Exper. Lake 3 Haney A.B.C.
Runoff (mm)
No.
Watershed
Runoff (mm)
29 30
Pulachimalai Pasukidamettu
1533 1000
27.0 7.1 25.0 6.9
936 721 1790
12.6 6.5 13.1 6.9 14.0
Japan 31 Matsuzawa 32 Tsukuba 33 Iu Norway 34 Birkenes 35 Botnane
T (jC)
pH
105.7 112.0 104.0 113.0 18.0 25.9 28.8 26.0 48.4 44.7 30.9
3.7 3.0 2.0 3.0 1.7 4.0 4.0 4.0 5.2 5.2 1.6
5.1 4.0 3.0 11.0 3.1 4.7 4.2 4.7 4.8 1.1 1.4
4.2 3.5 3.0 3.5 2.0 3.9 3.9 3.9 2.4 2.4 2.6
23.8 9.0 6.5 18.0 10.0 33.0 27.8 30.4 9.2 9.1 16.3
5.8 13.0 11.5 15.0 4.4 1.4 1.4 1.4 3.4 3.4 1.9
14.6 18.0 14.0 19.5 6.8 9.3 10.8 9.5 5.2 5.7 6.1
113.1 173.9
0.4 0.3
19.7 30.7
0.2 0.3
14.0 37.4
0.0 0.0
3.3 15.2
404
9.0 7.5
1797
6.2
115 203 348 247 383
0.3 6.4 2.0 0.3 0.2 0.3
United Kingdom 55 Ciste Mhearad 56 Dargall 57 Glendye
1735 2464 1178
230.9
33.5
8.9
14.9
Si (Amol/l)
Clpp (Amol/l)
131.6 168.3
3.6
259.9 357.8
16.2 20.2
4.1
10.1
54.5
118.6
8.5
76.3
40.9
6.6
11.4
14.8
50.3
3.9
15.6
6.1 13.8 19.8 2.6 19.8
209.6 71.5 72.3 33.3 76.7
0.8 4.1 4.4 2.6 4.4
33.0 12.1 16.5 9.2 10.6
3.1 15.0 15.4 1.0 15.4
170.4 144.8 118.2 41.1 71.3
1.1 4.4 5.0 0.8 5.0
164.3 65.6 48.8 19.3 36.8
5.0 5.2 45.0 6.0 5.2 111.3 6.4 6.5 87.0
79.0 170.9 203.1
2.0 4.3 4.5
4.0 8.7 12.3
1.5 3.5 11.7
5.5 17.9 55.1
5.0 11.9 9.3
9.5 22.6 37.4
NO3 pp (Amol/l)
69.5 114.3
NO3 (Amol/l)
SO4 pp (Amol/l)
14.6 17.7
SO4 (Amol/l)
HCO3 (Amol/l)
15.8 20.2
154.4 262.7
100.0 43.8
169.1
57.7 102.0 110.0 143.0 43.9 8.7 8.7 8.7 36.0 36.0 17.2
113.9 127.0 115.0 141.0 55.1 18.4 19.3 18.4 45.9 43.0 32.0
5.1
28.4
12.0 18.0 12.0
6.0 5.0 5.0
28.9 28.9 28.9 7.2 7.2
1.9 1.6 2.4 1.4 0.5
15.3
37.1
29.9 18.0 24.5 23.0 5.0 28.4 28.4 28.4 10.2 10.2 21.4
52.7 21.0 23.5 22.5 8.0 42.5 43.4 40.8 12.1 11.5 35.3
11.0 4.2
244.8 251.4
18.9
263.7
Cl (Amol/l)
204.7
42.5
90.0
103.3
0.1
5.2
7.4
0.4
82.3
10.3
41.8
33.2
68.5
39.1
58.4
8.0 11.1 11.5
167.0 30.3 24.0
5.2 4.1
1.5 0.7
7.9 38.7 39.3
81.7 60.7 61.3
11.5
21.6
4.1
0.9
39.3
26.3
3.4 130.0 87.0
68.0 197.0 177.4
7.0 3.7
9.0 7.1
11.5 25.4 15.8
19.5 40.7 25.2
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
4.6 41.7 4.7 87.0 4.7 95.0 5.3 123.0 7.0 5.0 4.7 7.6 4.6 7.6 4.5 7.6 5.8 34.3 5.7 34.3 7.1 4.5 13.7
0.4 0.5
Sipp (Amol/l)
68.2 97.7
43.5 71.7 36.2
Spain 48 Montseny Mtns
Storbergsba¨cken Vuoddasbacken
73.8 109
Mg (Amol/l)
6.3 11.1
16.0
53 54
Mgpp (Amol/l)
62.0 90.5 37.4
1215
Switzerland 49 Ticino lake Sweden 50 Dantersta 51 Lilla Tivjon 52 Solmyren
14.3 21.3
Ca (Amol/l)
15.9 19.0
South Africa 47 Swartboskloof
Rhodesia 45 Juiadale 46 Rusape
Capp (Amol/l)
63.6 23.4 25.6
13.8 17.1
Sogndal 1 Sogndal 3 Storgama
93.5 148.3
K (Amol/l)
3.9 7.3
385 88
42 43 44
Kpp (Amol/l)
282.5 245.6 217.4
5.8 7.5 7.5 7.5 5.2 3.1 3.1 3.1
Breidvikdalen Dyrdalen Kaarvatn Langtjern E1 Langtjern E2 Langtjern E3
Na (Amol/l)
14.0 25.4
1310 2560 3712 3305 1899 576 576 576 875 875 923
36 37 38 39 40 41
Napp (Amol/l)
81.4
1.7 2.3
42.1 60.4 119.1
29.6 69.2
(continued on next page)
251
252
Appendix B (continued) No.
Watershed
Runoff (mm)
58 59
Green Burn White Laggan
2135 2185
United States 60 Albany. Wyoming 61 Andrews Creek. WA
69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
pH
Napp (Amol/l)
Na (Amol/l)
Kpp (Amol/l)
K (Amol/l)
Capp (Amol/l)
Ca (Amol/l)
Mgpp (Amol/l)
Mg (Amol/l)
6.0 5.8 111.3 6.0 5.8 111.3
172.0 172.4
4.3 4.3
8.7 7.8
3.5 3.5
24.7 28.3
11.9 11.9
29.5 29.9
5.8 6.0 13.2 3.3 30.3 1.2 1.2 13.2 7.7 13.0 9.0 4.0 2.1 7.3 12.1 9.4 4.4 4.9 5.2 2.5 1.9 5.1 2.3 3.8 10.0 4.0 6.7 4.0 5.2 9.8 12.1 1.4 70.8
52.8 96.0 41.9 43.4 82.6 41.7 42.9 56.0 58.2 44.4 52.7 56.7 9.6 330.0 64.4 45.7 65.0 120.0 38.3 16.4 15.7 116.5 15.9 78.0 79.4 13.0 58.2 43.7 94.7 44.3 133.7 40.8 190.2
3.2 0.3 0.5 0.7 9.5 0.2 0.2 6.3 2.5 4.5 2.4 0.8 0.9 1.2 9.0 22.3 1.3 0.7 1.8 0.5 0.6 3.3 3.6 0.3 6.9 3.0 2.2 0.8 1.0 5.6 4.6 1.2 2.5
17.5 13.0 7.7 12.1 9.1 13.8 15.1 7.0 13.9 13.8 13.3 8.0 2.5 33.0 17.1 14.1 15.0 15.0 5.9 4.0 8.5 22.2 5.2 10.0 14.9 10.0 10.6 10.0
8.6 0.8 1.5 1.9 16.4 0.3 0.3 15.2 5.1 8.5 4.8 4.1 1.3 2.0 16.9 6.9 6.0 1.9 4.2 3.2 3.2 4.2 5.9 1.2 5.8 4.5 3.3 4.1 1.6 19.7 8.6 5.1 8.7
2.3 1.0 1.0 0.6 6.9 0.1 0.1 2.9 1.8 2.9 1.8 2.1 0.4 2.0 10.9 3.2 1.6 0.9 2.1 0.9 0.7 3.9 0.6 0.6 2.4 1.5 1.0 2.1 0.8 4.8 4.4 1.0 9.1
9.6 5.4 5.1 3.4 2.3 8.5
8.7 52.0 89.9 106.3 71.7 17.0
1.9 0.5 3.0 2.2 1.1 5.3
55.7 170.0 43.4 15.5 56.9 225.6 223.2 46.0 15.9 18.1 23.0 33.5 10.0 250.0 82.3 74.0 250.0 60.0 41.2 27.5 45.7 87.1 27.6 55.0 83.4 13.0 23.3 17.5 35.8 111.9 66.3 39.5 67.8 27.2 36.0 30.0 65.9 16.7 60.0 32.8
26.1 41.0 15.7 9.9 23.6 98.5 56.8 22.0 14.8 13.3 19.0 20.0 1.8 250.0 85.3 29.4 150.0 45.5 15.6 7.6 13.0 20.2 4.1 8.0 26.0 9.5 11.6 10.0 24.3 28.3 65.7 20.3 41.8 22.4 8.2 17.0 17.5 36.2 23.0 8.2
7.0 5.0 13.1 7.0 3.5 3.5
7.6 5.8 6.7 5.8
6.8 11.7 6.9 11.0 6.9 10.6 6.9 6.0 16.0 3.5 8.4 3.0 14.0 5.0
9.0 7.2
12.0 7.0 1.5 9.0
6.0 7.4 5.0 6.1 7.5 6.8 4.9 6.3 6.9 6.9 6.4 6.8 6.7 4.7 6.8
15.3 5.0 6.2 12.3 0.7 22.0 6.8 4.2 6.6 12.0 6.9 7.2 6.9 5.0 2.5 5.0 4.7
13.6 60.4 9.4 11.8 11.2 15.0 13.0 20.7 14.9 12.5 5.8
1.9 1.8 4.2 10.6 8.8 13.4
1.1 0.8 3.9 1.5 1.3 3.9
Sipp (Amol/l)
Clpp (Amol/l)
Cl (Amol/l)
72.1 45.9
130.0 130.0
197.3 201.7
170.0 55.1 4.3 139.2 124.3 131.0
7.0 46.4 16.4 20.9
8.5 63.7 13.0 37.5
0.5
150.7
7.3
18.5
0.5
139.3 167.0 27.8 320.0 204.4 165.1 95.0 200.0 73.7 25.5 45.7 130.5 26.6 100.0 148.3
8.4 4.6 1.1 7.6 18.2 10.3 2.9 6.1 14.4 1.9 1.6 9.1 5.0 4.1 10.1 7.0 24.2 4.6 7.2 4.7 19.1 2.3 88.0
19.7
37.3 10.0 105.3 4.4 165.0
5.6 9.1 4.8 4.2 4.5
7.2 22.0 22.4 11.6 7.8 10.9
5.1
Si (Amol/l)
155.7 158.0 200.0 1.8
242.0 106.8 219.2 290.6 8.4 140.0 106.4
*10. 11. 18. 20: average of punctual values. 49: punctual values. 24 – 30: high flow and low flow discharge weighted punctual value averages.
NO3 pp (Amol/l)
3.6 3.6
1.8 7.5 0.3 0.3 4.4
NO3 (Amol/l)
5.0 5.7
15.9 1.8 2.0 2.0 0.1
14.8 1.7 90.0 42.8 20.9 14.0 51.0 15.2 4.6 5.7 14.8 4.1 68.0 27.4 7.0 27.1
26.3
45.0
23.1 8.9 10.3 8.5
31.1 15.1 8.0 0.1
9.7 21.0
2.7 15.0
14.8
8.5
3.7
SO4 pp (Amol/l)
SO4 (Amol/l)
HCO3 (Amol/l)
25.4 25.4
45.6 45.1
54.0 50.9
3.6 28.6 7.5 18.5 1.0 1.1 15.1 17.1
19.0 52.7 64.2 80.7 13.1 14.4 4.0 5.0
520.0
17.9 36.0 1.6 16.0 26.0 27.0 11.0 23.0 29.9 5.6 5.4 3.7 8.2 3.0 32.4 37.5 24.6 36.0 21.5 32.1 19.8 6.0 5.3 10.0 10.8 14.0 3.7 20.6 20.0 30.6
6.0 49.5 2.1 40.5 26.6 66.8 55.0 29.0 64.9 13.2 13.1 2.5 15.3 11.5 63.3 66.0 19.7 17.5 20.4 56.9 109.5 29.5 6.8 15.8 10.1 10.5 10.7 56.3 63.4 68.9
42.2 25.8
181.0
51.0 22.1 1040.0 119.6 50.6 760.0 260.0 34.1 79.4
140.0 121.9 15.0 61.0
140.0
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
62 63 64 65 66 67 68
430 480 Bear Brook. ME 881 Brier Creek. GA 557 Cadwell Creek. MA 793 Caribou P. Creek LoP. AL 167 Caribou P. Creek HiP. AL 152 Como creek. CO 123 Coweeta 2. NC 854 Coweeta 18. NC 955 Coweeta 34. NC 955 Dry. VA 400 Emerald lake. CA 1410 Falling Creek. GA 300 Filson Creek. MN 270 Fort River. MA 507 Halfmoon Creek. GA 400 Holiday Creek. VA 350 Hubbard Brook. NH 800 Loch Vale. CO 604 ALoch Vale. CO 1127 Log Creek. CA 373 Martinell. CO 1507 Merced river. CA 665 Mundberry Brook. MA 562 Mt Moosilauke. NH 2400 Old Rag Mountain. VA 395 Old Rag Mountain. VA 400 Panola. GA 338 Panther Lake. NY 720 Pond Branch. ML 169 Rabbit Ears. CO 617 Rio Icacos. PR 3680 Silver Creek. ID 437 South Cascade. WA 2040 Tallulah river. GA 1160 Tarps Creek. CA 219 Tesque Aspin. NM 639 Tesuque Conifer. NM 829 Woods Lake. NY 760
T (jC)
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
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Chemical weathering in granitic environments Priscia Oliva *, Je´rome Viers, Bernard Dupre´ LMTG (UMR 5563/OMP)-CNRS, IRD, UPS, 38 Rue des 36 ponts, 31400 Toulouse, France Received 2 November 2001; received in revised form 1 August 2002; accepted 2 August 2002
Abstract Factors controlling chemical weathering in granitic environments are deduced using an extensive database from the literature. Based on a transition state theory model, we evaluated the effects on chemical weathering of runoff, temperature and pH. The dependence between temperature and runoff, and chemical weathering is complicated by other factors such as the presence/depth of soil cover. Soil cover favors chemical weathering when it is thin and rich in weatherable minerals. In contrast, it reduces chemical weathering when is thick and poor in primary minerals. Processes that increase the contact time and the surface of the water – rock interactions such as physical denudation, increase chemical weathering rates in granitic environments. The effect of temperature can be quantified assuming an apparent activation energy value of 48.7 kJ/mol to describe chemical weathering of silica in granitic crystalline environment under high runoff conditions. D 2003 Elsevier B.V. All rights reserved. Keywords: Chemical weathering; Small watersheds; Granitic environment
1. Introduction At present, the scientific community faces important environmental problems concerning climate change (Ledley et al., 1999) and human impacts on the Earth ‘‘global ecosystem’’ (e.g., greenhouse effect and climate warming, air, water and soil pollution). Chemical reservoirs at the Earth’s surface (atmosphere, continent, biosphere, ocean) are tightly coupled and an understanding of these new environmental questions requires global study of the Earth surface. Many studies have focused on the impact of global change at the Earth’s surface, particularly within the
* Corresponding author. E-mail address: [email protected] (P. Oliva). 0009-2541/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.chemgeo.2002.08.001
global CO2 cycle in relation to the greenhouse effect or climate warming (Berner et al., 1983; Volk, 1987; Schwartzman and Volk, 1989). At a geological scale, two major processes intervene to control the atmospheric CO2 balance (Volk, 1987). The first is CO2 emission to the atmosphere, mainly from volcanic activity. The second is CO2 consumption by silicate weathering. Chemical weathering of silicate minerals produces HCO3 which is then transported by rivers to the oceans. HCO3 precipitates in the oceans to form carbonates, the net result being the removal of CO2 from the atmosphere. In contrast, carbonate weathering has no influence on the long-term atmospheric CO2 balance. Assuming (1) the increase of the Earth’s mean surface temperature is related to an increase of greenhouse gas (CO2, CH4. . .) concentrations in the atmosphere, and (2) increasing temperature enhances silicate weathering rates, atmospheric CO2 regulation
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must exist (Walker et al., 1981; Berner et al., 1983; Volk, 1987; Brady and Carroll, 1994). Specifically, increasing concentrations of atmospheric CO2 leads to an increase of CO2 consumption by silicate weathering (White et al., 1999). Due to its great variability and importance, the silicate weathering rates need to be better constrained to produce a viable model of atmospheric CO2 evolution (Volk, 1987; Brady, 1991). Weathering rates are strongly related to lithology (Garrels and MacKenzie, 1971; Meybeck, 1987; Bluth and Kump, 1994). Evaporites are more easily weathered than carbonates, and carbonates are more easily weathered than silicates. Climatic factors also play a key role. The main factors are temperature and runoff and/or precipitation amount. The effect of soil cover could also be of major importance; a soilcovered system has hydrological and physical properties (e.g., water residence time, high contact surface area between water and fresh minerals) that could play a strong role in chemical weathering. Thick soil profiles were used (Stallard, 1985) to explain the low chemical weathering rate of tropical rivers draining the Amazonian plains and equatorial African regions (Gaillardet et al., 1995; Edmond et al., 1995). Contradictory effects were found, however, concerning the role of vegetation. On the one hand, mineral dissolution should be enhanced by a low pH environment close to the root system (e.g., due to nitrification processes for example) and by the presence of organic acids resulting from plant biodegradation (Keeney, 1983; Heyes and Moore, 1992; Drever, 1994). On the other hand, vegetation protects the soil from physical erosion, and thus favours the development of thick soils, which isolate parent rock from weathering. Numerous studies have investigated the effect of acid precipitation on small watershed ecosystems (Wright et al., 1988; Sverdrup and Warfvinge, 1995; Probst et al., 1992). The effect of acid deposition on chemical weathering rates is still unclear and long-term studies are required to better evaluate the impact of such pollution (Probst et al., 1995). Other factors, poorly studied, could be important, such as frost and the partial pressure of CO2 (Ohte et al., 1995) which has changed during geological time. Many studies have considered chemical weathering in basaltic environments (e.g., Gislason et al.,
1996), particularly for the study of the CO2 cycle. Weathering rate laws (Dessert et al., 2001) explore the roles of temperature and runoff in the control of the basalt weathering rates. However up to now, no chemical weathering rate laws have been proposed for granitic crystalline and granitoids rocks. Among the different potential factors governing chemical weathering, White and Blum (1995) proposed that the effect of temperature dominates while others proposed that the effect of runoff dominates (Millot et al., 2002). White et al. (1999) considered both to be significant. Knowledge of chemical weathering rates laws for granitic environments have a great importance; as granitoid rocks cover f 25% of the land surface of the mean upper crust, the weathering of granites may have a major impact on the CO2 cycle. Both large- and small-scale studies have been focuses on granitic weathering rates. 1.1. Large fluvial basin studies Studies carried out on large rivers allow estimates of element fluxes from the continents to the oceans ( + 6, Summerfield and Hulton, 1994; Edmond et al., 1995; Dupre´ et al., 1996; Boeglin and Probst, 1998). By considering the ‘‘60’’ largest rivers in terms of water discharge, we account for more than 80% of the water draining the continents (Gaillardet et al., 1999). Note, however, that large rivers drain varied lithologic and climates which prevent a rigorous study of the factors controlling chemical weathering. Great advances were made in the past two decades to define chemical weathering rate laws based on large river chemistry. Using an inverse method (Ne´grel et al., 1993; Gaillardet et al., 1995), it is possible to calculate for each river the respective contribution of the different end-member lithologies (evaporite, carbonate, silicate) to the water chemical composition. This normalization is necessary to separate chemical fluxes due to silicate weathering from those due to evaporite and carbonate weathering. In a review of large-scale studies, Gaillardet et al. (1999) proposed that the second most important parameter controlling chemical weathering after lithology is runoff. These authors reveal also observed positive correlation between physical and chemical erosion for silicates. This correlation illuminates the role of the soil in the weathering of acidic crystalline
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
227
rocks. When mechanical erosion prevents the formation of thick soils, waters interact directly with fresh crystalline rocks. In contrast, thick soils reduce these interactions resulting in a lower chemicalweathering rate. These systems are referred to as ‘‘transport-limited’’ (Carson and Kirkby, 1972; Stallard, 1985).
effect the particulate fraction. In large rivers, due to storage effects, the export of particulate matter may not reflect primary production from bedrock. This is another strong argument against comparing of the particulate matter load delivered by small and large basins.
1.2. Small watershed studies
2. The small watershed database
Small watersheds are well-defined hydrological areas and have been studied to better understand local ecosystem function (Likens and Bormann, 1995). Small watersheds offer the possibility to better constrain the parameters controlling chemical weathering. Other information can be obtained by comparing the chemistry of different small watersheds. White and Blum (1995) studied the chemistry of 68 small watersheds located in crystalline environments, and defined the relation between chemical weathering and climate. They found that both precipitation and temperature affect chemical weathering rates and that they are more significant than the role of topography and physical erosion. In contrast, a recent study comparing river chemistry in Canada with other small granitic watersheds (Millot et al., 2002) shows that chemical weathering is related to runoff and physical erosion rates. Small catchments are limited generally to the basin head, and located in more mountainous zones. Although large- and small-scale studies should be complementary, their comparison is quite difficult. Large watersheds are mainly located on alluvial flood plains and contain both depositional and weathering zones. In contrast, small watersheds are more representative of a single weathering environment. Another complication is that water and particulate matter fluxes are more difficult to quantify in small watersheds. This is principally due to role of flood events in small watersheds. Each flood influences significantly the annual mass balance of water, solutes, and particulate matter in small watersheds. In contrast, large rivers are much less affected by this phenomenon. In addition, in contrast to small watersheds, large watersheds generally have large alluvial plains constituted of sediments that are hydrologic reservoirs. Colluvial, alluvial and lacustrine formations do not have a large influence on the dissolved fraction behavior, but do
2.1. Sources of data The White and Blum (1995) database is the starting point for this work. It includes small watersheds located in granitic environments and was used to study the effect of temperature on chemical weathering. The present database is based on a critical review of the data included in the White and Blum (1995) database plus additional data taken from the literature. We selected watersheds for which precise information on the lithology (granitoids, gneiss, schists), topography, hydrogeology and pedology of the catchment, and the hydrochemistry of the streams and precipitation are available. A variety of environments were included in the database. Owing to the lack of studies in warm and humid environments we choose to add recent data (Bajpai, in preparation) from Southern Indian watersheds (i.e. 100,000 to 600,000 ha) which are not affected by urban and/or agricultural anthropogenic effects. Note that the Nsimi watershed (Oliva et al., 1999) is located in an intracratonic tropical province; this type of province represents about 1/3 of the total emerged surface. Warm and dry regions are not represented in the database. Studies considered in the database are very diverse. Some are longer-term studies (sampling weekly or monthly) of variable duration, whereas others are more or less punctuated studies consisting of one unique to only several samples. Considering our interest in using annual fluxes to estimate weathering rate laws, long-term studies are most useful. However, we also considered samples from short-term studies for which information on the variability of chemistry at an annual scale is available. Physical and chemical characteristics of the different watersheds in the database are given in Appendices A and B. Calculated concentrations and fluxes are provided in Table 1.
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Table 1 Calculated cation, silica and total dissolved solids concentrations and fluxes used in the different figures No. Watershed
Precip. Runoff T (mm) (mm) (jC)
pH Si Cations Cations Cations + (Amol/l) (Amol/l)a (Aeq/l)a Si (Amol/l)a
Qcations QSi (mol/ (mol/ ha/an) ha/an)a
1 2 3 4 5 6
1751 583 2146 2146 2146 4541
380 252 1010 1240 1040 3668
5.4 126 4.5 96 6.7 98 6.7 75 6.7 70 6.4 42
79 17 76 77 82 31
121 20 117 123 134 57
205 113 174 152 152 73
479 243 989 930 728 1534
268 34 815 1089 907 1714
412 37 1243 1716 1470 2844
20 7 52 60 49 98
804
278
2.4 6.0 191
156
244
347
530
292
455
23
804
383
2.4 6.0 132
87
127
219
506
224
316
21
804
287
2.4 6.0 143
103
162
246
411
120
190
15
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
Nsimi Exper. Lake Haney A.B.C. Haney B.B.C. Haney C.B.C. Jamieson Ck. B.C. Rawson lake E. Ont Rawson lake NE. Ont Rawson lake NW. Ont Slave Provinceb Greenville Provinceb Hartviko Lysina Salacova Lhota Vocadlo Liuhapiro Yli-Knuutila Estibe`re Latte Margeride Strengbach Barhalde Schluchsee Ilambalarib Cherakkobbanmalab Anamalai Hillsb Sivagirib Tarangamakanamb Pulachimalaib Pasukidamettub Matsuzawa Tsukuba Iu Birkenes Botnane Breidvikdalen Dyrdalen Kaarvatn Langtjern E1 Langtjern E2 Langtjern E3 Sogndal 1 Sogndal 3 Storgama Juiadalec
701 1016 685 736 766 750 1400 1806 1000 1477 2000 2300 3800 3400 2300 3200 3100 3600 2600 1672 1587 1945 1637
2252 876 876 876 984 984 1192 1220
24.0 2.4 9.2 9.2 9.2 3.4
Qcations (eq/ha/ an)a
Qcations + Si (kg/ha/ year)a
117
4.0 6.9
3
272
494
276
4
317
576
10
556
4.5 5.9
50
98
162
168
387
546
904
28
297 224 245 127
33 157 49 530 61 32 112 33 115 126
42 240 37 861 93 75 204 55 179 207
330 381 294 657
321 909 313 217
763 557 1255 1139 1189
92 298 23 48 285 242 181 263
120 519 446 803 518 448 315 456
490 380 616 441 381 313 431
188
234
546
3875 1284 1384 1411 3175 2019 1683 2433 2580
82 1066 407 2026 436 312 1675 775 1988 2811 1596 2570 10,489 4518 6594 4674 10,268 4827 4565 4169 2801
11 48 16 44
180 78 293 246
111 700 277 1233 286 174 877 477 1354 1800 1174 1942 6021 2563 3681 2568 5544 2773 2631 3554 2110
40 10 11 19 21 37 34 35 10 7 18 147
59 17 16 38 30 68 60 63 17 14 32 164
1049 267 397 629 423 271 263 258 145 84 268 565
1389 432 593 1265 597 467 438 441 214 155 420 629
108 406 128 171 480 198 1125 1246 705 952 1400 1974 2019 1013 822 902 2291 1533 1000 936 721 1790 1310 2560 3712 3305 1899 576 576 576 875 875 923 385
6.0 5.0 6.5 6.5
6.8 4.0 7.1 6.2 6.1 6.1 5.0 7.6 6.1 11.0 9.0 6.1 3.9 5.0 27.0 7.1 27.0 7.2 27.0 6.9 27.0 7.1 27.0 6.3 27.0 7.1 25.0 6.9 12.6 6.5 13.1 6.9 14.0 5.8 4.6 7.5 4.7 7.5 4.7 7.5 5.3 5.2 7.0 3.1 4.7 3.1 4.6 3.1 4.5 5.8 5.7 7.1 4.5 13.8
64 45 178 120 85 192 127 168 156 139 132 168 260 358
16 20 245
26 27
141 177
392
942
54 29 72 89 67 281 105 142 112 231 132 117 164 128
8 7 41
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
229
Table 1 (continued) No. Watershed
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
Rusapec Swartboskloof b Montseny Mtns Ticino lakeb Dantersta Lilla Tivjon Solmyren Storbergsba¨ckenc Vuoddasbacken Ciste Mhearadb Dargall Glendye Green Burn White Laggan Albany. Wyoming Andrews Creek. WA Bear Brook. ME Brier Creek. GA Cadwell Creek. MA Caribou P. Creek LoP. ALc Caribou P. Creek HiP. ALc Como creek. CO Coweeta 2. NC Coweeta 18. NCc Coweeta 34. NC Dry. VAc Emerald lake. CA Falling Creek. GA Filson Creek. MN Fort River. MA Halfmoon Creek. GA Holiday Creek. VA Hubbard Brook. NH Loch Vale. CO ALoch Vale. CO Log Creek. CA Martinell. CO Merced river. CA Mundberry Brook. MA Mt Moosilauke. NH Old Rag Mountain. VA
Precip. Runoff T (mm) (mm) (jC)
pH Si Cations Cations TDS (Amol/l) (Amol/l)a (Aeq/l)a (Amol/l)a
922 2920 870 2138 610 623 621 542 621 2000 2861 1375 2707 2822 720
88 1215 404 1797 115 203 348 247 383 1735 2464 1178 2135 2185 430
17.1 251 16.0 9.0 7.5 6.2 90 0.3 6.4 2.0 0.3 0.2 81 0.3 5.0 5.2 42 6.0 5.2 60 6.4 6.5 119 6.0 5.8 72 6.0 5.8 46
247 71 224 111 346 192 163 88 112 31 22 78 36 35 119
296 81 266 176 592 350 287 144 181 38 39 128 67 69 182
1650
480
7.0 7.6 170
310
1400 1140 1429
881 557 793
5.0 5.8 55 13.1 6.7 4 7.0 5.8 139
480
167
3.5
465
152
3.5
770 1771 1813 2009 1450 1830 1220 680 1080 1000
Qcations Qsi (mol/ (mol/ ha/an) ha/an)a
498
222
201
1617
169
201
73 82 198 108 81
730 1489 1403 1539 1004
519
480
86 76 59
142 99 98
124
374
131
123 854 955 955 400 1410 300 270 507 400
1050 1300
350 800
1104 1587 876 1295 1400 1429
604 1127 373 1507 665 562
2400
2400
1.5 5
1147
395
9.0 7
Qcations (eq/ha/ an)a
QTDS (kg/ha/ year)a
218 863 1018 1992 596 365 613 216 471 630 1674 2079 1469 1512 511
262 984 1258 3163 955 671 1067 356 759 761 2231 2880 2209 2349 784
816
1404
2389
70
141 80 166
485 24 849
730 377 464
1215 489 770
38 11 37
697
498
208
625
1164
27
333
611
464
199
505
929
23
6.8 11.7 6.9 151 11.0 6.9 10.6 6.9 139 167 6.0 6.0 28 16.0 7.4 320 3.5 5.0 204 8.4 6.1 165 3.0 7.5 95
60 35 66 78 17 715 134 78 416
73 45 92 110 26 1168 237 161 779
210
1287 1330 668 392 960 552 837 380
714 429 937 439 390 3888 603 792 3311
51
205 245 45 1035 339 244 511
574 334 669 314 253 2437 340 376 1787
56 28 19 98 25 35 69
14.0 6.8 200 5.0 4.9 74
171 87
253 137
371 161
700 590
754 635
1094 1007
41 36
6.3 25 6.9 46 7 131 6 27 7 100 7 146
38 61 219 43 54 136
63 105 313 69 88 222
64 106 350 69 154 281
154 515 486 401 665 820
257 831 772 635 922 793
424 1429 1100 1028 1317 1290
13 43 37 32 47 43
33
49
780
1176
89
119
258
346
9.0 7.2 0 12.0 7.0
156
245
615
12
93
12 37 88 97 86 73
24
(continued on next page)
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Table 1 (continued) No. Watershed
Precip. Runoff T (mm) (mm) (jC)
87
1450
400
1149 1170
338 720
933
169
1132 4300 1120 2040
88 89 90 91 92 93 94 95 96 97 98 99
Old Rag Mountain. VA Panola. GA Panther Lake. NY Pond Branch. ML Rabbit Ears. CO Rio Icacos. PR Silver Creek. ID South Cascade. WAc Tallulah river. GA Tarps Creek. CA Tesque Aspin. NM Tesuque Conifer. NM Woods Lake. NYb
pH Si Cations Cations TDS (Amol/l) (Amol/l)a (Aeq/l)a (Amol/l)a
Qcations Qsi (mol/ (mol/ ha/an) ha/an)a
Qcations (eq/ha/ an)a
QTDS (kg/ha/ year)a
158
41
47
199
632
166
187
22
15.3 5.0 6
200
111 113
159 201
311
676
425 958
601 1679
30
12.3
242
162
222
404
409
274
376
19
617 3680 437 2040
0.7 22.0 7 4.2 7
107 219 291 8
93 141
142 217
200 360
580 7552
880 10,820
36 443
54
95
62
659 8066 1270 171
1091
1932
46
1820
1160
12.0 7
140
79
116
219
1624
1146
1646
80
876
219
7.2 7
106
154
217
260
233
283
394
15
946
639
5.0
131
155
945
1169
1149
829
2.5
142
206
1230
1802
1230
760
5.0 5
53
93
101
199
Data from White et al. (1999) (Malaysia and United States) Sq. Lawing Sg. Lui Sg. Batu Sg. Selangor Sg. Batamgso Sg. Selangor Rasa Sg. Changkak Gem lake Fall river Big Thompson river Spruce Creek Fern Creek North St. Vrain Creek Boulder Brook
1180
25.0 25.6 24.3 25.6 25.0 25.6
304
2310 2490 2910 2930 3120 3590
1430 267 426
25.0 5.0 0 0
4 76 53
6110 61 204 224
611 739 536
0 0 0
46 45 64
280 333 341
251
0
153
384
814 924 1180
306 315 248
Qcations* were calculated by using Eq. (6), or Eq. (7) when precipitation data were not available. The data from Millot et al. (2002) were previously corrected for eolian deposits. Temperature values in italic were found on the WEB. a The weathering fluxes ( QSi, Qcations and QTDS) and the concentrations (Si, cations and TDS) are corrected for atmospheric inputs. b Sea-chloride corrected values. c Concentration values are deduced from given fluxes.
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
2.2. Parameters controlling chemical weathering rates Chemical-weathering rates are listed in Table 1. Chemical weathering fluxes are generally defined as the difference between fluxes leaving (export fluxes) and entering (precipitation fluxes) the water system (White and Brantley, 1995), and can be computed from: Elementary fluxes from chemical weathering ¼ export fluxes precipitation fluxes
ð1Þ
Eq. (1) is simplified because it does not consider cation exchange or biomass. For watersheds located in areas where precipitation is not strongly acidic, cation exchange is considered to be at steady state (Drever and Clow, 1995). A similar assumption can be made for the biomass if no vegetation modification (forest fire, human or natural deforestation, animal grazing, etc.) is occurring. This simplified relation is realistic if the ‘‘precipitation fluxes’’ term encompasses all atmospheric inputs including bulk precipitation, dry deposits and throughfall. In most cases, only bulk precipitation fluxes were measured which underestimate the true fluxes entering the watershed. The calculation of the net output fluxes due to chemical weathering will depend strongly on the atmospheric input term if it is similar in size to the exportation fluxes. Unfortunately, studies which compare dry and wet deposition, or which take into account throughfall deposition are scarce (Lindberg et al., 1986). Although some studies consider total atmospheric deposition (e.g., Feller, 1977; Cronan, 1980; Pace`s, 1986), large uncertainties are associated with estimates of dry deposition and throughfall. One way to account for total atmospheric deposition is to make a chloride (Cl) correction. This method is based on the assumption that (i) all Cl comes from the atmosphere and (ii) Cl behaves as a conservative element in the watershed. This correction accounts for dilution and evaporation processes, but neglects element fluxes due to biomass removal or dry deposition. On the other hand, as bulk precipitation composition may vary strongly from one sample to the next (Viers et al., 2001), data obtained from periodic sampling could underestimate or overestimate atmospheric input. In the present database, precipitation chemistry
231
data are very heterogeneous (see above). Therefore, to make the data as consistent as possible, two types of atmospheric corrections were performed: (1) based on the precipitation data, and (2) using marine values. Thus, two types of elementary weathering fluxes appear in the database: those corrected for precipitation and those corrected for sea salt (see paragraphs devoted to the calculation of chemical weathering fluxes). Transition state theory, provide framework to evaluate the different parameters controlling chemical weathering rates at a global scale. The following dissolution rate equation was proposed by Lasaga et al. (1994): ni RW ¼ k0 S eEa=RT anHHþ þ j ai i
ð1 e
DGr=RT
Þ
ð2Þ
RW represents the mineral dissolution rate expressed in mol per unit mineral surface per time. Each term of the relation will be explained below. . In Eq. (2), k0 is the ‘‘kinetic’’ rate constant of dissolution which is characteristic of each mineral. Although we have selected watersheds with granitic lithology, numerous differences exist among the rocks in the various watersheds. These differences which include mineralogy, granulometry, crystal defects and fracture density could effect strongly chemical weathering rates. These differences are defined as best as possible in the database (Appendix A). . S is the reactive surface area of the mineral. Chemical weathering increases with increasing S, which should be related to the watershed area (Appendix A). The surface area between the rock and the solution is difficult to estimate and can be affected by watershed history. The soil characteristics including the nature, thickness, porosity and hydrological pathways, etc., through the soil column are parameters that could strongly influence S. For example, a glaciated watershed may favor chemical weathering due to glacial abrasion, which increases the surface contact between rock and water. The ‘‘Glacial deposits’’ term in the Appendix A indicates the presence of glacial features including moraines and glacial till. . The e Ea/RT term corresponds to the temperature effect term. Ea is the activation energy which depends on pH and mineralogy (Lasaga et al., 1994), R is the
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
gas constant, and T is the temperature in Kelvin. This relation indicates an increase in the weathering rate with increasing temperature. Average annual temperature (Appendix A) representativity could be questioned, particularly for cold environments such as high mountain systems where temperature could have important daily variations. The mean annual temperatures of the watersheds in the database are listed in Appendix A. . The 1 e DGr/RT term accounts for the effect of the approach to equilibrium on a mineral dissolution rate. This term should be related to the water –rock interactions in the field, and particularly to the water residence time and the rock – water ratio. Finally, it may correspond to annual precipitation and runoff (both expressed in Table 1 in mm/year). Annual precipitation corresponds in the majority of the studies to a long-term average, but in certain cases (indicated), it is one to a few years average. Runoff is the surface water (river, stream) which effectively leaves the watershed. Deep-water circulation is generally neglected in small-scale studies (Drever and Clow, 1995). Thus, the difference between precipitation and runoff corresponds to the evapotranspiration term. Runoff values in the database are of two origins. Some correspond to data given by the authors, others are calculated from discharge data and surface area. These calculations could have large uncertainties. In all cases, large uncertainties remain, inherited from the database with annual runoff values from various origins (calculated from evapotranspiration, taken from other studies, etc.). Unfortunately, this adds to the inherent variability of the runoff parameter (Peel et al., 2001), particularly within high-elevation systems where it can change substantially within a short period (Peters and Leavesley, 1995) due to the variability of precipitation event or within deciduous forested cover, which can decrease evapotranspiration during the dormant period (Peel et al., 2001). Fortunately, the majority of the watersheds considered have coniferous or tropical rainforest cover. The presence of a glacier in a catchment could also be responsible for considerable variations in daily hydrologic and chemic exportations (Anderson et al., 1997). The presence of glacial deposits at the lowest level in the soil profile of glaciated watersheds could have an influence in chemical weathering by favoring drainage and contact time with the parent rock.
.
The other terms such as the proton and the ligand activities (aH+nH+, ji ani i ) are related to the concentration of the natural solutions (Appendix B). The effect of human activities on silicate weathering should be considered here. Crystalline environments are generally drained by dilute waters (low ionic strength) which are very reactive face to atmospheric acid deposition. In the database, we kept watersheds subject to acid rain to see if they can be distinguished from the non-polluted sites (Appendix A). . Others parameters (Appendix A) should be considered with respect to field studies devoted to chemical weathering. These include altitude, vegetation, soil, and the presence of disseminated calcite. (i) Minimum and maximum altitude, expressed in meters, gives information on the topography (relief, slope). (ii) Vegetation can have contrasting influence on chemical weathering processes: on one hand, biomass degradation under the influence of bacteria releases elements to the soil and delivers organic acids favoring mineral dissolution and rock disintegration; on another hand, plants take up nutrients (e.g., Ca, Mg, K, N, P, C) from the soil and protect soil from physical denudation. Generally, authors considered that watersheds are at steady state with respect to the vegetation element (uptake = release) and have neglected the vegetation parameter. However, some authors (Velbel, 1995; Likens and Bormann, 1995) have shown an effect of biomass on base cation or silica fluxes at the watershed scale. We indicate in the database those studies that appear to have non-steady state vegetation effect. (iii) Concerning the soil, we indicate in the database information about the nature and the thickness of the pedological formations when they were given by the authors. Soil reflects chemical weathering and could influence the river chemistry in various ways: by lowering the acid precipitation effect, by protecting the parent rock, or by favoring the vegetation growth. (iv) We indicate in the database, rivers supposed to drain disseminated calcite-bearing rocks because calcite dissolution could influence stream water chemistry. 2.3. Calculation of chemical weathering fluxes The calculations of chemical weathering fluxes require the precipitation chemistry, the stream water
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
chemistry and the associated fluxes of water (Appendix A). Chemical weathering fluxes computed using data provided in Appendices A and B are given in Table 1. The following paragraphs present equations used for these calculations.
233
salt (Celement, sea-chloride corrected) for the stream waters using (Ne´grel et al., 1993): Celement;
sea chloride corrected
¼ Celement;
2.4. Precipitation chemistry and fluxes The major element concentrations of precipitation from various watersheds are provided in Appendix B. In the majority of the studies, these concentrations of all major elements were measured in bulk precipitation. Only a small percentage of the studies reported precipitation chemistry including throughfall and/or dry deposition contributions. Elementary precipitation fluxes ( Qelement, precipitation) are related to precipitation, P, and element concentration, Celement, concentration, by the following relation: Qelement;
precipitation
ðmol=ha=yearÞ
¼ Celement; precipitation ðAmol=lÞ P ðmm=yearÞ=100 ð3Þ
river
ðCelement; sea=Cchloride; sea Þ Cchloride; river ð5Þ 2.7. Chemical weathering fluxes As mentioned above, two types of chemical weathering fluxes are calculated: elementary chemical weathering fluxes ( Qelement, weathering) and sea salt corrected elementary chemical weathering fluxes ( Qelement, sea-chloride corrected weathering). These fluxes are related by: Qelement;
weathering
¼ Qelement;
river
Qelement;
precipitation
ð6Þ Qelement; sea chloride corrected weathering ðmol=ha=yearÞ ¼ Celement; sea chloride corrected ðAmol=lÞ R ðmm=yearÞ=100
ð7Þ
2.5. Stream water chemistry and fluxes Major element concentrations (Celement, river) available in the literature are generally calculated averages, sometimes weighted averages for the long-term studies. Two major uncertainties remain in the database; (1) are punctuated measurements representative of the annual mean value and (2) do long-term studies minimize the impact of individual events on computed annual fluxes (Drever and Clow, 1995)? Stream chemical fluxes ( Qelement, river) are calculated in a similar manner to precipitation fluxes, by using the runoff value (R) and according to: Qelement;
river
ðmol=ha=yearÞ ¼ Celement;
river
ðAmol=lÞ
R ðmm=yearÞ=100 ð4Þ 2.6. Sea salt corrections Solute concentrations can be affected by evaporation or dilution processes. To address these effects, elementary concentrations were corrected from sea
3. Chemical weathering laws in granitic environments Chemical weathering in the present study is defined in terms of silica, major cations, and major cations + silica fluxes. Four temperature classes are defined to represent different climatic zones (i.e., cold, temperate, cool temperate and warm). These different zones are denoted by different symbols in the figures. The effects of various parameters on weathering rates are considered below. 3.1. Lithology Lithology has a strong effect on weathering rates (Bluth and Kump, 1994). Studies of the influence of lithology on weathering shows that water draining igneous and metamorphic rocks is more dilute in major cations and more acidic than waters draining more reactive lithologies zone such as serpentinite (Kram et al., 1997), mafic rocks (Gislason et al., 1996), and carbonates and evap-
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
orites (Berner and Berner, 1987; Meybeck, 1987). In granitic environments, chemical weathering is dominated by the more easily weatherable minerals (Sverdrup and Warfvinge, 1995), essentially, feldspars, amphiboles, epidote, and apatite. Kram et al. (1997) observed that the chemical variability of the major cation concentrations in rivers draining silicate environments is controlled by lithology even in areas exposed to acid precipitation. In a study of chemical weathering of a high elevation crystalline basin in the Pakistan Himalayas, Blum et al. (1998) showed that the chemical weathering is strongly influenced by carbonate minerals although the comprise only 1% of the total watershed area. The connection between lithology and weathering rates is, however, complex. Watersheds containing disseminated calcite (e.g., Schindler et al., 1976; Drever and Zobrist, 1992; Mast et al., 1990; Clow and Drever, 1996) do not always have the highest calcium fluxes found for a given range of runoff and temperature. Indeed, although we have tried to eliminate the effects of lithology through a careful selection of sites in the database uncertainties remain in the mineralogical composition of those watersheds contained in the database. Consequently, it is impossible to eliminate completely the effect of lithology from this study.
3.2. Temperature One of the most important results of the White and Blum (1995) study is an observation of a positive correlation between temperature and the element fluxes, particularly for silica. The database in the present study shows an increased disparity of the silica flux vs. T correlation (R2 = 0.27) compared to White and Blum (1995) (see Fig. 1). When we look at the relation that may exist between T and Qcations or Qcations + silica, the influence of temperature on cation fluxes (R2 = 0.4) is not well established (Fig. 2). The relation between cations and/or silica concentrations (i.e., Csilica, Ccations) and temperature are also unclear (Figs. 3 and 4). This are inconsistent with the relation established by Lasaga et al. (1994) between Silica concentration versus T data (Fig. 4) from river data compiled by Meybeck (1980). Uncertainties of reported mean annual temperatures could mask a potential relation between temperature and weathering. Nevertheless, the data in our database suggest that the high temperature watersheds are even more inconsistent with this relationship. On one hand, despite of the disparity of the data, it is important to note that the highest silica or cations fluxes are obtained for warm watersheds (Rio Icacos and some Indian watersheds). On the other hand, for the same temperature, rivers from India, Rio Icacos
Fig. 1. Silica fluxes expressed in mol/ha/year versus temperature (jC) for all the selected watersheds (the present database and the East United States and Malaysian samples from White et al., 1999).
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
235
Fig. 2. Total dissolved cation fluxes expressed in mol/ha/year versus temperature (jC). *The weathering fluxes are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction.
and Nsimi exhibit a range for the flux of cations which is larger than the range obtained for all the remaining rivers regardless of temperature. Moreover, this range fluctuates between very low measured fluxes (Nsimi), which are comparable with low-temperature environment values, to the highest measured fluxes (Rio Icacos). Considering the previous considerations, it is evident that temperature
alone does not control chemical weathering in granitic environments. High-temperature points can be divided in two groups; the first corresponds to the humid tropical watersheds (i.e., Nsimi, Juiadale, Swartboskloof) and the warm temperate watersheds from eastern United States (i.e., Panola, Holiday Creek) which contain thick soil cover. The second corresponds to the
Fig. 3. Total dissolved cation concentrations (Amol/l) versus temperature (jC). *The concentrations are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction.
236
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Fig. 4. Silica concentration (Amol/l) versus temperature (jC) for all the selected watersheds (the present database and the eastern United States and Malaysian samples from White et al., 1999). A linear relation between silica concentration and temperature, previously determined by Lasaga et al. (1994) using the Meybeck (1980) data is also shown in this diagram.
remaining points. In Fig. 2, the first group of samples, particularly the Nsimi watershed, exhibits Total Dissolved Cation fluxes lower than those of the other watersheds. The existence of thick soil covers in tropical environment masks the temperature dependence of silica and cationic fluxes. Thick soil covers protect the primary rocks from chemical weathering. A similar conclusion was reached by Carson and Kirkby (1972), Stallard (1985) and Gaillardet et al. (1999). 3.3. Runoff Fluxes and concentrations of cations, cations + silica, and silica versus runoff are plotted in Figs. 5 – 8. In each plot, the watershed temperature class is indicated. Fig. 5 indicates a decrease in Si concentration with runoff regardless of temperature class. For a given runoff, it seems that high temperature watersheds exhibit the highest silica concentrations. Moreover, it seems that the highest concentrations are for the lowest runoff ( < 1000 mm/year) at all the temperatures. The same type of relation is
observed for considering major cations concentrations (Fig. 6). This is in agreement with White and Blum (1995). However, low runoff and high-temperature samples such as the humid tropical samples of the Nsimi (Oliva et al., 1999; Viers et al., 2000), Swartboskloof (Britton, 1991) or Juiadale (Owens and Watson, 1979) watersheds exhibit a Total Dissolved Cation concentration that is lower than would be expected considering the other high-temperature points. This could be explained by the thick soils typical of tropical environments. Thick soils are generally depleted in major cations because they are mainly composed of secondary minerals such as clays (e.g., kaolinite) and oxy-hydroxides (e.g., gibbsite and goethite), and primary phases which weather slowly (e.g., quartz, heavy minerals). These minerals release silica, aluminum, and iron during chemical weathering (Nahon, 1991). Considering the draining role of the surficial soil formations, the released of those elements should follow the hydrological variations. In contrast, major cations are released during deeper weathering of the parent rock which is poorly related to the hydro-
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
237
Fig. 5. Silica concentration (Amol/l) versus runoff (mm/year) for all the selected watersheds (the present database and the eastern United States and Malaysian samples from White et al., 1999). The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend.
logic network. However, it is important to note that the Nsimi watershed (Oliva et al., 1999; Viers et al., 2000) sample does not really confirm the silica versus runoff relation and presents one of the lowest silica concentrations (f 125 Amol/l) for all the
selected high-temperature watersheds despite its low runoff. This may be related to the particular functioning of this watershed (Oliva et al., 1999), with kaolinite recrystallisation in the swamp zone and important silica recycling by plants.
Fig. 6. Total dissolved cation concentrations (Aeq/l) versus runoff (mm/year). *The concentrations are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction. The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend. The humid tropical watersheds and the warm temperate watersheds from eastern United States are indicated.
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Fig. 7. Silica flux expressed in mol/ha/year versus runoff (mm/year) for all the selected watersheds (the present database and the eastern United States and Malaysian samples from White et al., 1999). The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend. In the upper left corner, we plot the same data using a bilogarithmic diagram.
Figs. 7 and 8 present plot of silica and cations + silica fluxes versus runoff, respectively. An increase of the silica fluxes with runoff for high-temperature watersheds is evident in Fig. 7, although there is no clear relation between silica fluxes and runoff for the other temperature classes. In contrast to what is reported by White and Blum (1995), we do not observe one linear correlation but two distinct tendencies: (1) the strongest correlation (R2 = 0.88) is
that given by high-temperature watersheds and which is located above, (2) the majority of the other points which show only weak correlation when considered by temperature class. High-temperature watersheds show a rapid evolution of silica fluxes with runoff. The same tendencies are obtained when the cationic fluxes or cations + silica fluxes (R2 = 0.74 for T > 13 jC) are plotted as a function of runoff (Fig. 8). Are those two distinct trends between warm watersheds
Fig. 8. Total Dissolved Solid (Si + Na + Mg + K + Ca) fluxes (kg/ha/year) versus runoff (mm/year). *The TDS fluxes are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction. The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend.
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
and the rest of the data (temperate and cold samples) the result of a temperature effect? If we look at the figure that presents the silica concentrations corrected for atmospheric input versus T (Fig. 9) for the two distinct classes of runoff ( < 1000 mm) and (>1000 mm), we observe two patterns. (1) At low runoff ( < 1000 mm), there is a weak relation between silica concentration and temperature (R2 = 0.3). (2) At greater runoff (>1000 mm), silica concentrations increase more closely with temperature (R2 = 0.68). Temperature dependence is only visible at high runoff. At low runoff, other parameters, such as the presence of an important soil cover, prevail over temperature. We suggest that (i) effective contact time between solution and minerals and (ii) solution/minerals contact area are two important parameters effecting chemical weathering processes. In general, the more water passing through the watershed, the more con-
239
tact time and surface at the water –rock interface be favored. However, at the watershed scale, this is complicated by different types of water flows: (1) shallow flowpaths (surface flow) are rapid and do not favor chemical weathering at the water – rock interface and (2) deep flowpaths (base flow) are slow, penetrate deeper within the soil cover and thus favor water – rock interactions. In the humid tropical zone, soil thickness constrains the reactive fluid circulation to zone depleted in major cations and limit the contact between solution and weatherable minerals (Stallard, 1985). In contrast, in high runoff system such as Rio Icacos, important physical erosion limit the development of soil cover and thus favor contact surface and time between water and parent rock. In cold environments (generally high elevation systems in the database), slopes and/or the high porosity of the thin soil cover could induce a low contact time between minerals and solutions
Fig. 9. (A and B) Silica concentrations (Amol/l) versus temperature (jC) for (A) watershed with low runoff ( < 1000 mm/year) and (B) for watersheds with high runoff (>1000 mm/year).
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
(Meybeck, 1987). However, in alpine environments having high runoff, the persistence of an important snow cover during winter is known to favor the waterlogging of surface formations (e.g., soil, glacial deposit, etc.). Several authors (Brady, 1991; White and Blum, 1995; Dessert et al., 2001) proposed an equation to describe chemical weathering fluxes as a function of temperature and runoff. This equation is given by: Qelement;weathering ¼ Runoff C0 exp½Ea=Rð1=T 1=T0 Þ
ð8Þ
With C0 the element concentration at T0, a reference temperature expressed in Kelvin. Ea is an activation energy and R the gas constant. Because a coupled temperature and runoff dependence is apparent for watersheds with high runoff, an activation energy of chemical weathering of granitic environments is only computed for those watersheds. We used the relationship between silica concentration and temperature (Fig. 9) to quantify this activation energy. By plotting the logarithm of the dissolution rate versus the reciprocal of the temperature factor in Kelvin, we obtain the apparent activation energy of the overall reaction. Fig. 10 shows an Arrhenius plot for watersheds with runoff above 1000 mm/year. The linear
relationship between the two parameters can be expressed as: LogCsilica ¼ ðEa=R Ln10Þ1=T þ b
ð9Þ
The b term represent the intercept of the linear regression ( = 10.78). The calculated activation energy Ea for silica export in high runoff granitic watersheds is 48.7 kJ/mol, which close to the value of 51.3 kJ/mol calculated by White et al. (1999). Note, however, that silica fluxes may be strongly influenced (Alexandre et al., 1997) by biological processes (e.g., diatoms, phytolites). As only consider high runoff watersheds in our calculations, the calculated Ea of 48.7 kJ/mol may be representative of the effects of runoff and temperature on granitic rock weathering. 3.4. Other parameters According to transition state theory, the proton or aqueous activities of elements may influences chemical weathering rate. We found no correlation between elements concentrations and chemical fluxes. The effect of pH on weathering rates can be assessed with the aid of Fig. 11. Cation concentrations and fluxes show no clear correlation with the pH in small granitic watersheds. However, it seems that the greatest cation fluxes are obtained
Fig. 10. Logarithm of silica concentration versus 1/T (jK) for watersheds with runoff values >1000 mm/year. An apparent activation energy of 48.7 kJ/mol was calculated using the slope of the linear regression indicated in the diagram.
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
241
Fig. 11. Cation concentrations (Aeq/l) and cation fluxes in eq/ha/year (in the upper left corner) versus pH values. *The concentrations and fluxes were corrected for atmospheric inputs. If the precipitation values were not available, we made a sea-chloride correction. The different types of point correspond to different temperature classes related to different climate types. The temperature correspondences are indicated in the legend.
for watersheds having higher pH. This may be related to the consumption of proton by chemical weathering reactions. It remains difficult, however to evaluate the effect of the proton or element activity on the chemical weathering because rivers water composition may be very different from that of the soil solutions in which weathering occurs. Stream water chemistry results from several processes and may not reflect directly water – rock interactions. The effect of acid deposition on chemical weathering is still unclear. Wright et al. (1988) studied the effect of acid rain and its reversibility in small granitic watersheds in Norway. They showed that stream water chemistry rapidly changes as a result of acid deposition. These changes were characterized by a net increase in aluminum, sulfate, calcium, and magnesium export. They proposed that the 50% increase of Ca+ and Mg+ fluxes in an acidified catchment resulted from the leaching of the exchangeable soil fraction (2% of the exchangeable fraction exported in 4 years) and an increase in chemical weathering rates. They also showed that the effects of acid rain are reversible; the stream
water recovered its initial chemistry rapidly after stopping acidification. In contrast, Driscoll et al. (1989) found, in a multidisciplinary study, that despite a decrease in acid deposition since the 1970s, silica concentrations remained the same in stream water. Sverdrup and Warfvinge (1995) concluded that the effect of acidification on chemical weathering is low. Note a pH decrease could increase the aluminum concentration in soil solutions which would inhibit silicate dissolution rates (Oelkers and Schott, 1995a,b). A study carried out on the Strengbach catchment (Probst et al., 1992, 1995) showed that the effect of acid deposition on chemical export can only be evaluated over the long term. In the present study, we see no evidence of an effect of pH on weathering rates at the global scale. Fig. 12 shows an example of cation fluxes vs. runoff, where acid rain-affected samples are indicated by filled points. The chemical weathering rates of glaciated watersheds are important to consider because hydrological fluxes in those environments are among the highest in the world. Anderson et al. (1997), in a review, compared the chemical weathering rate for several
242
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Fig. 12. Cation fluxes (eq/ha/year) versus runoff (mm/year). The lowest diagram uses a bilogarithmic scale. *The fluxes were corrected for atmospheric inputs. If the precipitation values are not available, we made a sea-chloride correction. Empty points correspond to watersheds affected by acid impactions.
glaciers with different types of substratum. One of the main conclusions of this study is that, despite high runoff, glaciers do not have as high a chemicalweathering rate as was expected, compared to nonglaciated areas. The effect of the presence of glaciers on weathering rates can be seen in Fig. 13. Only watersheds in our database within the two extreme
classes of temperature are considered. According to Anderson et al. (1997), glaciated environments do not have high chemical fluxes, compared to watersheds in warm climates. However, if we only consider cold environments (i.e., cold watershed and glacier), glaciated watersheds have higher annual solute exportations. This may be due to persistent ice cover which
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
243
Fig. 13. Cation fluxes (eq/ha/year) uncorrected for atmospheric input versus runoff (mm/year) for warm climate watersheds, cold climate watersheds (the present database) and the glacier data compiled by Anderson et al. (1997).
induces continual water flow and to the effect of physical erosion (e.g., grinding) that favors higher chemical weathering rates.
Physical erosion effects could be of major importance. Both large- and small-scale studies show that physical erosion is also closely related to water
Fig. 14. Cation fluxes (mol/ha/year) versus runoff (mm/year) for the cold climate watersheds of the present database. Filled points indicate those with glacial deposits. *The fluxes are corrected for atmospheric inputs. If precipitation values were not available, we made a sea-chloride correction.
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P. Oliva et al. / Chemical Geology 202 (2003) 225–256
flow and thus to runoff (Gaillardet et al., 1999; Millot et al., 2002). This is the likely origin of the positive correlation found between chemical weathering rate and runoff for high-temperature watersheds. Physical denudation limits soil-cover development, and thus permits more extensive water –rock interactions, particularly in the tropics. In mountainous environments, it will favor the production of fine-grained particles that accumulate in depressions leading to wetland formation (i.e., swamp, peat bog) with high porosity and available weathering surfaces. Theses zones are known to favor high chemical weathering rates (Oliva et al., 1999). Previously glaciated watershed substratum is generally composed of glacial till, which has high porosity. Watersheds known to be previously glaciated (filled points) are plotted in a log –log diagram showing cation fluxes for cold watersheds versus runoff in Fig. 14. This figure suggests little effect of the former presence of glaciers on weathering rates. The different weathering rates between active glaciers (Anderson) and ancient glaciated watersheds may be related to the duration of weathering which is not well established for natural systems. In environments with high physical erosion rates, fresh material is continuously exposed to erosion. In soil-covered systems with high runoff, one would suspect a long contact time between solution and weatherable materials, in contrast to the humid tropical environments, where the presence of thick soil cover is associated with low physical erosion. In these cases, the effect of soils or vegetation on chemical weathering rates may be secondary.
4. Conclusions Evidence presented in this study indicates that temperature and runoff are the parameters that best describe chemical weathering in granitic watersheds. However, there is no clear dependence relation between these factors and weathering fluxes are effected by other factors including water/rock contact time and the surface of the water – rock interaction. Special attention were aimed at illuminating the effects of the soil cover which (1) favors chemical weathering when it is thin and rich in weatherable minerals and (2) inhibits chemical weathering when is thick and poor in primary minerals. In optimal cases, the temperature effect can be quantified. An apparent activation energy value for silica of 48.7 kJ/mol is found to describe chemical weathering in granitic watersheds under high runoff conditions. No relation between anthropogenic effects and chemical weathering was found, and although glaciers seem to favor chemical weathering, the effect of glacial materials on chemical weathering rates is unclear. Further work is required to determine those parameters that control the reactive surface of the materials and the time contact between water and rock.
Acknowledgements We are very grateful to E. Oelkers, S. Anderson, A. Blum and two anonymous reviewers for their objective corrections and suggestions. We would like to thank J. Gaillardet, J. Schott, F. Martin, A. Probst, and C. Dessert for helpful discussions and to J. Escalier for analytical support. [EO]
Appendix A General characteristics of the selected watersheds underlain by crystalline granitic formations. In this table, the bedrock nature is indicated (chn: charnokite; dio: diorite; gb: gabbros; gn: gneiss; gr: granite; grdio: granodiorite; meta: various metamorphic rocks; mig: migmatite; mon: monzonite; msch: micaschist; qtz: quartzite; qzdio: quartzdiorite; sch: schist), the soil type, physical characteristics (surface area of the watershed, elevations), climatic characteristics (precipitation and runoff amount in mm/year, mean annual temperature in jC) and watershed particularities concerning human impacts, the presence of disseminated calcite or glacial deposits (G. deposits).
No. Watershed
Rock
Soils
Area ha
Elevation (m) max.
500
700
Oliva et al., 1999; Viers et al., 2000
chn
laterites
Allan et al., 1993
gr
acid soils
7
Feller and Kimmins, 1984 Feller and Kimmins, 1984 Feller and Kimmins, 1984 Zeman, 1975
dio/grdio
acid soils
23
145
dio/grdio
acid soils
68
dio/grdio
acid soils
gr/meta
Schindler et al., 1976
grdio
Schindler et al., 1976
grdio
Schindler et al., 1976
grdio
Millot et al., 2002
gn
117
Millot et al., 2002
chn
556
Czechoslovakia 12 Hartviko
Pace`s, 1985, 1986
gn/qtz
13
Kram et al., 1997
gr
Canada 2 Exper. Lake 3
Haney A.B.C.
4
Haney B.B.C.
5
Haney C.B.C.
6
Jamieson Ck. B.C. Rawson lake E. Ont Rawson lake NE. Ont Rawson lake NW. Ont Slave Province
7 8 9 10 11
Greenville Province
Lysina
humoferrugineous humoferrugineous humoferrugineous
acid brown soils podzol
60
min.
Precip. Runoff T (mm) (mm) (jC)
Notes
Anthropogenic effect
1751
380
24.0
583
252
310
2146
1010
2.4 glaciolacustrine deposits 9.2
235
455
2146
1240
9.2
44
295
455
2146
1040
9.2
299
304
1280
4541
3668
3.4
170
100
300
804
278
2.4 calcite
suspected
10
150
210
804
383
2.4 calcite
suspected
63
100
260
804
287
2.4 calcite
suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Cameroun 1 Nsimi
Reference
4.0 G. deposits. eolian inputs 4.5 G. deposits. eolian inputs
98
672
724
701
108
6.0
acid rain
27
829
949
1016
406
5.0 growing forest
acid rain 245
(continued on next page)
246
Appendix A (continued) No. Watershed
Reference
Rock
Soils
Area ha
Elevation (m)
557
744
685
128
6.5
acid rain
59
570
635
736
171
6.5
acid rain
165 7
42 42
92 92
766 750
480 198
700 1650 20 1150 8900 650
2350 1500 1250
1400 1806 1000
1125 1246 705
11.0
883
1146
1477
952
9.0
acid rain
1000 11 1150
3000 1250
2000 2300
1400 1974
3.9 G. deposits 5.0
acid rain acid rain
3800 3400 2300 3200 3100 3600 2600
2019 1013 822 902 2291 1533 1000
27.0 27.0 27.0 27.0 27.0 27.0 25.0
15
Vocadlo
Pace`s, 1985, 1986
gn/qtz
Finland 16 Liuhapiro 17 Yli-Knuutila
Lepisto et al., 1988 Lepisto et al., 1988
gn gn
France 18 Estibe`re 19 Latte 20 Margeride
Oliva et al., in press Probst et al., 1995 Ne´grel, 1999
podzol
21
Strengbach
Probst et al., 1995
gr gr gr/gn/ msch gr
podzol
80
Germany 22 Barhalde 23 Schluchsee
Stahr et al., 1980 Feger et al., 1990
gr gr
podzol
India 24 Ilambalari 25 Cherakkobbanmala 26 Anamalai Hills 27 Sivagiri 28 Tarangamakanam 29 Pulachimalai 30 Pasukidamettu
Bajpai, Bajpai, Bajpai, Bajpai, Bajpai, Bajpai, Bajpai,
gn/sch gn/sch gn/sch gn/sch gn/sch gn/sch gn/sch
Japan 31 Matsuzawa
32
Tsukuba
33
Iu
prep. prep. prep. prep. prep. prep. prep.
White and Blum, 1995; Shimada et al., 1993; Ohte et al., 1995 Hirata and Muraoka, 1993 Wakatsuki and Rasyidin, 1992
292,300 112,200 618,600 539,800 155,400 223,500 148,400
gr
0.5 – 5 m
gr
brown soils. 1–2 m
gr
5.0
6
190
255
1672
936
12.6
68
200
380
1587
721
13.1
1945
1790
14.0
180
suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
168
gn/qtz
in in in in in in in
Anthropogenic effect
max.
Pace`s, 1985, 1986
podzol
Notes
min.
Czechoslovakia 14 Salacova Lhota
acid brown soils acid brown soils
Precip. Runoff T (mm) (mm) (jC)
No. Watershed
Norway 34 Birkenes Botnane Breidvikdalen Dyrdalen Kaarvatn
39 40 41 42 43 44
Langtjern E1 Langtjern E2 Langtjern E3 Sogndal 1 Sogndal 3 Storgama
Rhodesia 45 Juiadale 46
Rusape
South Africa 47 Swartboskloof Spain 48 Montseny Mtns
Switzerland 49 Ticino lake
Sweden 50 Dantersta 51 52
Lilla Tivjon Solmyren
Rock
Soils
Wright and Johannessen, 1980 Skartveit, 1981 Skartveit, 1981 Skartveit, 1981 White and Blum, 1995 Wright, 1983 Wright, 1983 Wright, 1983 Frogner, 1990 Frogner, 1990 White and Blum, 1995
gr
gn/gr gn/gr gn/gr gn gn gr
podzol podzol podzol thin. acidic thin. acidic
Owens and Watson, 1979 Owens and Watson, 1979
gr
laterite
gr
Britton, 1991
Area ha
Elevation (m) min.
max.
41
200
300
340 189 330 2500
180 295 438 200
650 695 806 1375
479 181 75 963 432 60
516 516 516 900 900 580
720 720 720
Precip. Runoff T (mm) (mm) (jC)
Notes
Anthropogenic effect
1637
1310
5.8 7.5 7.5 7.5 5.2
acid rain acid rain
2252
2560 3712 3305 1899 576 576 576 875 875 923
3.1 3.1 3.1
acid rain acid rain acid rain
690
876 876 876 984 984 1192
91 1800
2000
1220
385
13.8
laterite
733 1500
1600
922
88
17.1
gr
laterite
373
320
1200
2920
1215
Avila and Roda`, 1988
sch
ranker
4
700
1035
870
404
Drever and Zobrist, 1992
gn
ranker + podzol
360
2400
2138
1797
calcite
Bergstro¨m and Gustafson, 1985 Calles, 1983 Calles, 1983
gn
31
610
115
0.3 G. deposits
suspected
1288 2700
623 621
203 348
2.0 G. deposits 0.3 G. deposits
suspected suspected
gn gn an gn/qtz
gr/gn gr/gn
7.1
16.0 growing forest
9.0
suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
35 36 37 38
Reference
(continued on next page)
247
248
Appendix A (continued) No. Watershed
53
Storbergsba¨cken
54
Vuoddasbacken
57 58
Glendye Green Burn
59
White Laggan
United States 60 Albany. Wyoming 61 Andrews Creek. WA 62 63
Bear Brook. ME Brier Creek. GA
64
Cadwell Creek. MA
65
67 68 69
Caribou P. Creek LoP. AL Caribou P. Creek HiP. AL Como creek. CO Coweeta 2. NC Coweeta 18. NC
70 71
Coweeta 34. NC Dry. VA
72
Emerald lake. CA
66
Land and Ohlander, 2000 Calles, 1983
Cooper et al., 1987 Farley and Werritty, 1989 Creasey et al., 1986 Farley and Werritty, 1989 Farley and Werritty, 1989
Rock
gr
Soils
podzol
gr/gn
Area ha
Elevation (m)
940
min.
max.
110
375
621
383
0.3 G. deposits
suspected
1250 716
2000 2861
1735 2464
5.0 6.0 G. deposits
suspected acid rain
6.4 G. deposits 6.0 G. deposits. growing forest 6.0 G. deposits. growing forest
suspected acid rain
podzol podzol
gr/gn/sch gr/gn/metam
podzol podzol
4125 250
300 225
500 716
1375 2707
1178 2135
gr/gn/metam
podzol
570
225
716
2822
2185
gr mon/qzdio
acid soils podzol
1750 5700 1311
3050 2647
720 1650
430 480
gr/gn gn/sch/metam
acid soils
msch
MacLean et al., 1999 Lewis and Grant, 1979 White and Blum, 1995 Johnson and Swank, 1973 White and Blum, 1995 Stauffer and Wittchen, 1991 Williams et al., 1993
0.2
Anthropogenic effect
247
gr gr/gn/metam
Knight et al., 1985 Mast and Clow, 2000 Norton et al., 1994 Buell and Peters, 1988 Yuretich et al., 1989, 1993 MacLean et al., 1999
Notes
542
4200
37 1030 210 225
Precip. Runoff T (mm) (mm) (jC)
suspected
acid rain
7.0 G. deposits
15 440
213 646
446 1269
1400 1140
881 557
73
165
344
1108
793
permafrost
520
300
450
480
167
3.5
suspected
msch
permafrost
570
300
450
465
152
3.5
suspected
gr gn/sch gr/gn/sch
podzol
664 2908 12 709 12 721
3559 1004 1006
770 1771 1813
123 854 955
11.7 11.0
acid rain suspected suspected
34 243
670 488
1585
2009 1450
955 400
10.6
suspected suspected
120 2800
3416
1830
1410
6.0
suspected
gn/Qzt/metam
7m
gn/sch grdio gr/grdio
35 cm
5.0 13.1 7.0 G. deposits
suspected acid rain suspected
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
United Kingdom 55 Ciste Mhearad 56 Dargall
Reference
No. Watershed
73 74
Reference
Soils
1220 680
300 270
1052
46
380
1080
507
8.4 G. deposits
suspected
6100 2969 2200 144 3076 213
4399 280 1101
1000 1050 1300
400 350 800
3.0 G. deposits 14.0 5.0
suspected acid rain acid rain
860 3110
4010
1104
604
3300 50 2067
3300 2397
1587 876
1127 373
8 3420 46,900 1224
3560 3997 300
1295 1400 1428
1507 665 562
0 12.0 7.0
suspected
1462 1060
2400 1147
2400 395
1.5 9.0
acid rain suspected
1450
400
9.0
suspected
279 727 182 3035
1149 1170 933 1132
338 720 169 617
616
800
4300
3680
186 1395 1616
2079 2518
1120 2040
437 2040
4.2
14,600 573 13 2067
2 2255
1820 876
1160 219
12.0 7.2
gr
3 2941
3658
946
639
5.0
gr
164 2804
3444
1149
829
2.5
gn
207
728
1230
760
5.0 G. deposits
thin soils acid soils
82 83 84
Martinell. CO Merced river. CA Mundberry Brook. MA Mt Moosilauke. NH Old Rag Mountain. VA Old Rag Mountain. VA Panola. GA Panther Lake. NY Pond Branch. ML Rabbit Ears. CO
gr gr/grdio gn/sch
podzol
88 89 90 91 92 93 94 95 96 97 98
gn/qzt
gn/sch sch/gn mon/gn
acid soils podzol
gr/gn
gn/sch gr
271
1250 530
gr
282
439
gr/sch gn/charn sch mon/grdio/qzdio
41 224 124 557 38 121 200 2910
qzdio
podzol
laterite
mon mig gr/gn/sch grdio
acid soils acid soils
326
606
16.0 3.5
suspected
0
G. deposits. calcite 9.0 calcite 7.2
suspected suspected
suspected
15.3 calcite 5.0 G. deposits 12.3 0.7 G. deposits. eolian inputs 22.0 calcite
suspected suspected suspected suspected
G. deposits. calcite suspected
suspected
249
99
acid soils
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
gr/gn grdio
87
Anthropogenic effect
244
ALoch Vale. CO Log Creek. CA
Cronan, 1980 White and Blum, 1995 Stauffer and Wittchen, 1991 Peters, 1994 April et al., 1986 Cleaves et al., 1970 Peters and Leavesley, 1995 Rio Icacos. PR McDowell and Asbury, 1994 Silver Creek. ID Clayton, 1986, 1998 South Cascade. WA Drever and Hurcomb, 1986 Tallulah river. GA Mast and Turk, 1999 Tarps Creek. CA Williams and Melack, 1997 Tesque Aspin. NM White and Blum, 1995 Tesuque Conifer. NM White and Blum, 1995 Woods Lake. NY April et al., 1986
Notes
113
80 81
85 86
Precip. Runoff T (mm) (mm) (jC)
18,700 2520
79
Clow and Drever, 1996 Williams and Melack, 1997 Reddy, 1988 Mast and Clow, 2000 Yuretich et al., 1993
Elevation (m) max.
gn/gb gr/mon/gb
76 77 78
Area ha
min.
Mast and Turk, 1999 Siegel and Pfannkuch, 1984 Fort River. MA Yuretich and Bachelder, 1988; Yuretich et al., 1993 Halfmoon Creek. GA Clark et al., 2000 Holiday Creek. VA Mast and Turk, 1999 Hubbard Brook. NH Likens et al., 1977; Johnson et al., 1969 Loch Vale. CO Mast et al., 1990
75
Falling Creek. GA Filson Creek. MN
Rock
250
Appendix B Major element concentration values in precipitation and surface waters for the selected watersheds expressed in Amol/l.
No.
Watershed
Cameroun 1 Nsimi
380
T (jC)
pH
24.0 5.4
Napp (Amol/l)
Na (Amol/l)
Kpp (Amol/l)
K (Amol/l)
Capp (Amol/l)
Ca (Amol/l)
Mgpp (Amol/l)
Mg (Amol/l)
1.3
47.4
3.6
7.9
3.5
33.9
1.1
25.3
21.8 54.2 49.7 50.6 30.3 67.5 41.2 44.7 70.1 35.9
1.5 0.9 1.1 1.1 0.5 3.5 2.7 3.5
5.6 3.8 5.2 3.9 1.8 13.9 14.4 6.9 24.8 17.1
11.0 4.0 4.4 4.5 4.0 11.7 9.6 11.7
19.0 40.7 44.7 52.0 28.3 65.3 31.1 40.4 142.1 46.1
3.6 1.5 1.7 1.7 2.0 4.8 4.8 4.7
15.9 13.4 16.5 15.0 9.9 41.2 23.2 30.3 85.9 20.6
252 1010 1240 1040 3668 278 383 287 117 556
2.4 9.2 9.2 9.2 3.4 2.4 2.4 2.4 4.0 4.5
4.5 6.7 6.7 6.7 6.4 6.0 6.0 6.0 6.9 5.9
5.0 8.3 9.1 9.3 12.6 8.7 7.4 8.7
Czechoslovakia 12 Hartviko 13 Lysina 14 Salacova Lhota 15 Vocadlo
108 406 128 171
6.0 5.0 6.5 6.5
6.8 4.0 7.1 6.2
6.8 11.1 5.8 4.9
173.1 89.7 139.3 241.6
3.3 1.7 3.0 2.5
22.7 24.5 22.0 47.9
17.8 7.4 14.9 12.9
69.3 86.0 115.0 347.3
4.1 2.2 3.7 3.7
45.7 28.3 86.7 187.6
Finland 16 Liuhapiro 17 Yli-Knuutila
480 198
6.1 6.1
9.5 28.8
40.2 118.2
3.4 5.5
8.8 29.8
9.1 24.7
33.3 136.4
2.3 6.8
16.3 52.5
Strengbach
1125 1246 705 952
5.0 7.6 6.1 11.0 9.0 6.1
17.6 28.5 14.1 12.5
37.0 54.1 116.3 83.5
9.2 4.1 10.2 2.9
3.4 7.5 20.4 23.3
36.4 16.6 9.4 8.4
117.8 33.9 53.3 93.0
4.5 5.1 2.5 2.5
4.0 21.4 53.5 30.0
Germany 22 Barhalde 23 Schluchsee
1400 1974
3.9 5.0
3.7 3.2
52.8 58.6
2.6 2.3
9.9 14.4
5.0 5.9
30.3 26.3
1.4 1.8
8.9 14.4
2019 1013 822 902 2291
27.0 27.0 27.0 27.0 27.0
4 5 6 7 8 9 10
Haney B.B.C. Haney C.B.C. Jamieson Ck. B.C. Rawson lake E. Ont Rawson lake NE. Ont Rawson lake NW. Ont Slave Province
11
Greenville Province
France 18 Estibe`re 19 Latte 20 Margeride 21
India 24 Ilambalari 25 26 27 28
Cherakkobbanmala Anamalai Hills Sivagiri Tarangamakanam
7.1 7.2 6.9 7.1 6.3
127.2 140.1 202.8 122.4 157
17.9 20.2 18.2 23.1 23.8
127.9 113.9 208.4 137.3 126.9
102.4 92.3 163.1 108.7 98.6
Sipp (Amol/l)
Si (Amol/l)
Clpp (Amol/l)
Cl (Amol/l)
2.6
9.7
96.4 97.9 75.0 70.0 41.8 190.6 132.0 143.3 3.83 69.6
2.6 13.0 14.2 14.7 14.4 7.5 7.4 7.5
5.6 31.8 34.4 35.0 29.3 8.3 7.0 5.1 51.5 21.6
296.8 224.0 244.8 127.0
9.7 12.2 8.3 8.4
84.3 39.0 95.3 103.5
8.5 26.3
13.1 121.7
11.8 29.3 19.8 13.9
8.7 45.1 70.4 54.8
11.0
18.1
126
0.6
6.3 8.9 6.2
3.6 3.1 2.4
1.5
0.8
67.8 44.7 178.0 119.6
NO3 pp (Amol/l)
24.4
NO3 (Amol/l)
2.4
SO4 pp (Amol/l)
24.2 22.5 25.6 29.2
32.6
5.5 10.0 14.9 36.0
HCO3 (Amol/l)
2.4
1.3
12.9 8.9 9.6 9.9 1.5 3.9 4.0 3.9
33.7 20.5 24.8 23.7 2.6 12.5 13.4 8.8 24.5 25.1
18.1 31.7 15.9 9.0
194.4 211.0 97.7 42.1
26.6 60.9
41.0 292.4
27.0 61.4 22.4 21.5
11.0 82.7 39.1 105.5
20.0 262.3 44.5
18.6 23.2 37.6 16.3 23.4
265.1 229.1 414.7 207.9 187.2
0.8 4.4
42.3
SO4 (Amol/l)
16.2 3.3 3.6 376.5 69.1
85.0
191.9 126.7 168.4 156.4 138.6
77.8 114.5 145.7 107.6 165.7
18.6 13.9 57 42.3
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
Canada 2 Exper. Lake 3 Haney A.B.C.
Runoff (mm)
No.
Watershed
Runoff (mm)
29 30
Pulachimalai Pasukidamettu
1533 1000
27.0 7.1 25.0 6.9
936 721 1790
12.6 6.5 13.1 6.9 14.0
Japan 31 Matsuzawa 32 Tsukuba 33 Iu Norway 34 Birkenes 35 Botnane
T (jC)
pH
105.7 112.0 104.0 113.0 18.0 25.9 28.8 26.0 48.4 44.7 30.9
3.7 3.0 2.0 3.0 1.7 4.0 4.0 4.0 5.2 5.2 1.6
5.1 4.0 3.0 11.0 3.1 4.7 4.2 4.7 4.8 1.1 1.4
4.2 3.5 3.0 3.5 2.0 3.9 3.9 3.9 2.4 2.4 2.6
23.8 9.0 6.5 18.0 10.0 33.0 27.8 30.4 9.2 9.1 16.3
5.8 13.0 11.5 15.0 4.4 1.4 1.4 1.4 3.4 3.4 1.9
14.6 18.0 14.0 19.5 6.8 9.3 10.8 9.5 5.2 5.7 6.1
113.1 173.9
0.4 0.3
19.7 30.7
0.2 0.3
14.0 37.4
0.0 0.0
3.3 15.2
404
9.0 7.5
1797
6.2
115 203 348 247 383
0.3 6.4 2.0 0.3 0.2 0.3
United Kingdom 55 Ciste Mhearad 56 Dargall 57 Glendye
1735 2464 1178
230.9
33.5
8.9
14.9
Si (Amol/l)
Clpp (Amol/l)
131.6 168.3
3.6
259.9 357.8
16.2 20.2
4.1
10.1
54.5
118.6
8.5
76.3
40.9
6.6
11.4
14.8
50.3
3.9
15.6
6.1 13.8 19.8 2.6 19.8
209.6 71.5 72.3 33.3 76.7
0.8 4.1 4.4 2.6 4.4
33.0 12.1 16.5 9.2 10.6
3.1 15.0 15.4 1.0 15.4
170.4 144.8 118.2 41.1 71.3
1.1 4.4 5.0 0.8 5.0
164.3 65.6 48.8 19.3 36.8
5.0 5.2 45.0 6.0 5.2 111.3 6.4 6.5 87.0
79.0 170.9 203.1
2.0 4.3 4.5
4.0 8.7 12.3
1.5 3.5 11.7
5.5 17.9 55.1
5.0 11.9 9.3
9.5 22.6 37.4
NO3 pp (Amol/l)
69.5 114.3
NO3 (Amol/l)
SO4 pp (Amol/l)
14.6 17.7
SO4 (Amol/l)
HCO3 (Amol/l)
15.8 20.2
154.4 262.7
100.0 43.8
169.1
57.7 102.0 110.0 143.0 43.9 8.7 8.7 8.7 36.0 36.0 17.2
113.9 127.0 115.0 141.0 55.1 18.4 19.3 18.4 45.9 43.0 32.0
5.1
28.4
12.0 18.0 12.0
6.0 5.0 5.0
28.9 28.9 28.9 7.2 7.2
1.9 1.6 2.4 1.4 0.5
15.3
37.1
29.9 18.0 24.5 23.0 5.0 28.4 28.4 28.4 10.2 10.2 21.4
52.7 21.0 23.5 22.5 8.0 42.5 43.4 40.8 12.1 11.5 35.3
11.0 4.2
244.8 251.4
18.9
263.7
Cl (Amol/l)
204.7
42.5
90.0
103.3
0.1
5.2
7.4
0.4
82.3
10.3
41.8
33.2
68.5
39.1
58.4
8.0 11.1 11.5
167.0 30.3 24.0
5.2 4.1
1.5 0.7
7.9 38.7 39.3
81.7 60.7 61.3
11.5
21.6
4.1
0.9
39.3
26.3
3.4 130.0 87.0
68.0 197.0 177.4
7.0 3.7
9.0 7.1
11.5 25.4 15.8
19.5 40.7 25.2
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
4.6 41.7 4.7 87.0 4.7 95.0 5.3 123.0 7.0 5.0 4.7 7.6 4.6 7.6 4.5 7.6 5.8 34.3 5.7 34.3 7.1 4.5 13.7
0.4 0.5
Sipp (Amol/l)
68.2 97.7
43.5 71.7 36.2
Spain 48 Montseny Mtns
Storbergsba¨cken Vuoddasbacken
73.8 109
Mg (Amol/l)
6.3 11.1
16.0
53 54
Mgpp (Amol/l)
62.0 90.5 37.4
1215
Switzerland 49 Ticino lake Sweden 50 Dantersta 51 Lilla Tivjon 52 Solmyren
14.3 21.3
Ca (Amol/l)
15.9 19.0
South Africa 47 Swartboskloof
Rhodesia 45 Juiadale 46 Rusape
Capp (Amol/l)
63.6 23.4 25.6
13.8 17.1
Sogndal 1 Sogndal 3 Storgama
93.5 148.3
K (Amol/l)
3.9 7.3
385 88
42 43 44
Kpp (Amol/l)
282.5 245.6 217.4
5.8 7.5 7.5 7.5 5.2 3.1 3.1 3.1
Breidvikdalen Dyrdalen Kaarvatn Langtjern E1 Langtjern E2 Langtjern E3
Na (Amol/l)
14.0 25.4
1310 2560 3712 3305 1899 576 576 576 875 875 923
36 37 38 39 40 41
Napp (Amol/l)
81.4
1.7 2.3
42.1 60.4 119.1
29.6 69.2
(continued on next page)
251
252
Appendix B (continued) No.
Watershed
Runoff (mm)
58 59
Green Burn White Laggan
2135 2185
United States 60 Albany. Wyoming 61 Andrews Creek. WA
69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
pH
Napp (Amol/l)
Na (Amol/l)
Kpp (Amol/l)
K (Amol/l)
Capp (Amol/l)
Ca (Amol/l)
Mgpp (Amol/l)
Mg (Amol/l)
6.0 5.8 111.3 6.0 5.8 111.3
172.0 172.4
4.3 4.3
8.7 7.8
3.5 3.5
24.7 28.3
11.9 11.9
29.5 29.9
5.8 6.0 13.2 3.3 30.3 1.2 1.2 13.2 7.7 13.0 9.0 4.0 2.1 7.3 12.1 9.4 4.4 4.9 5.2 2.5 1.9 5.1 2.3 3.8 10.0 4.0 6.7 4.0 5.2 9.8 12.1 1.4 70.8
52.8 96.0 41.9 43.4 82.6 41.7 42.9 56.0 58.2 44.4 52.7 56.7 9.6 330.0 64.4 45.7 65.0 120.0 38.3 16.4 15.7 116.5 15.9 78.0 79.4 13.0 58.2 43.7 94.7 44.3 133.7 40.8 190.2
3.2 0.3 0.5 0.7 9.5 0.2 0.2 6.3 2.5 4.5 2.4 0.8 0.9 1.2 9.0 22.3 1.3 0.7 1.8 0.5 0.6 3.3 3.6 0.3 6.9 3.0 2.2 0.8 1.0 5.6 4.6 1.2 2.5
17.5 13.0 7.7 12.1 9.1 13.8 15.1 7.0 13.9 13.8 13.3 8.0 2.5 33.0 17.1 14.1 15.0 15.0 5.9 4.0 8.5 22.2 5.2 10.0 14.9 10.0 10.6 10.0
8.6 0.8 1.5 1.9 16.4 0.3 0.3 15.2 5.1 8.5 4.8 4.1 1.3 2.0 16.9 6.9 6.0 1.9 4.2 3.2 3.2 4.2 5.9 1.2 5.8 4.5 3.3 4.1 1.6 19.7 8.6 5.1 8.7
2.3 1.0 1.0 0.6 6.9 0.1 0.1 2.9 1.8 2.9 1.8 2.1 0.4 2.0 10.9 3.2 1.6 0.9 2.1 0.9 0.7 3.9 0.6 0.6 2.4 1.5 1.0 2.1 0.8 4.8 4.4 1.0 9.1
9.6 5.4 5.1 3.4 2.3 8.5
8.7 52.0 89.9 106.3 71.7 17.0
1.9 0.5 3.0 2.2 1.1 5.3
55.7 170.0 43.4 15.5 56.9 225.6 223.2 46.0 15.9 18.1 23.0 33.5 10.0 250.0 82.3 74.0 250.0 60.0 41.2 27.5 45.7 87.1 27.6 55.0 83.4 13.0 23.3 17.5 35.8 111.9 66.3 39.5 67.8 27.2 36.0 30.0 65.9 16.7 60.0 32.8
26.1 41.0 15.7 9.9 23.6 98.5 56.8 22.0 14.8 13.3 19.0 20.0 1.8 250.0 85.3 29.4 150.0 45.5 15.6 7.6 13.0 20.2 4.1 8.0 26.0 9.5 11.6 10.0 24.3 28.3 65.7 20.3 41.8 22.4 8.2 17.0 17.5 36.2 23.0 8.2
7.0 5.0 13.1 7.0 3.5 3.5
7.6 5.8 6.7 5.8
6.8 11.7 6.9 11.0 6.9 10.6 6.9 6.0 16.0 3.5 8.4 3.0 14.0 5.0
9.0 7.2
12.0 7.0 1.5 9.0
6.0 7.4 5.0 6.1 7.5 6.8 4.9 6.3 6.9 6.9 6.4 6.8 6.7 4.7 6.8
15.3 5.0 6.2 12.3 0.7 22.0 6.8 4.2 6.6 12.0 6.9 7.2 6.9 5.0 2.5 5.0 4.7
13.6 60.4 9.4 11.8 11.2 15.0 13.0 20.7 14.9 12.5 5.8
1.9 1.8 4.2 10.6 8.8 13.4
1.1 0.8 3.9 1.5 1.3 3.9
Sipp (Amol/l)
Clpp (Amol/l)
Cl (Amol/l)
72.1 45.9
130.0 130.0
197.3 201.7
170.0 55.1 4.3 139.2 124.3 131.0
7.0 46.4 16.4 20.9
8.5 63.7 13.0 37.5
0.5
150.7
7.3
18.5
0.5
139.3 167.0 27.8 320.0 204.4 165.1 95.0 200.0 73.7 25.5 45.7 130.5 26.6 100.0 148.3
8.4 4.6 1.1 7.6 18.2 10.3 2.9 6.1 14.4 1.9 1.6 9.1 5.0 4.1 10.1 7.0 24.2 4.6 7.2 4.7 19.1 2.3 88.0
19.7
37.3 10.0 105.3 4.4 165.0
5.6 9.1 4.8 4.2 4.5
7.2 22.0 22.4 11.6 7.8 10.9
5.1
Si (Amol/l)
155.7 158.0 200.0 1.8
242.0 106.8 219.2 290.6 8.4 140.0 106.4
*10. 11. 18. 20: average of punctual values. 49: punctual values. 24 – 30: high flow and low flow discharge weighted punctual value averages.
NO3 pp (Amol/l)
3.6 3.6
1.8 7.5 0.3 0.3 4.4
NO3 (Amol/l)
5.0 5.7
15.9 1.8 2.0 2.0 0.1
14.8 1.7 90.0 42.8 20.9 14.0 51.0 15.2 4.6 5.7 14.8 4.1 68.0 27.4 7.0 27.1
26.3
45.0
23.1 8.9 10.3 8.5
31.1 15.1 8.0 0.1
9.7 21.0
2.7 15.0
14.8
8.5
3.7
SO4 pp (Amol/l)
SO4 (Amol/l)
HCO3 (Amol/l)
25.4 25.4
45.6 45.1
54.0 50.9
3.6 28.6 7.5 18.5 1.0 1.1 15.1 17.1
19.0 52.7 64.2 80.7 13.1 14.4 4.0 5.0
520.0
17.9 36.0 1.6 16.0 26.0 27.0 11.0 23.0 29.9 5.6 5.4 3.7 8.2 3.0 32.4 37.5 24.6 36.0 21.5 32.1 19.8 6.0 5.3 10.0 10.8 14.0 3.7 20.6 20.0 30.6
6.0 49.5 2.1 40.5 26.6 66.8 55.0 29.0 64.9 13.2 13.1 2.5 15.3 11.5 63.3 66.0 19.7 17.5 20.4 56.9 109.5 29.5 6.8 15.8 10.1 10.5 10.7 56.3 63.4 68.9
42.2 25.8
181.0
51.0 22.1 1040.0 119.6 50.6 760.0 260.0 34.1 79.4
140.0 121.9 15.0 61.0
140.0
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
62 63 64 65 66 67 68
430 480 Bear Brook. ME 881 Brier Creek. GA 557 Cadwell Creek. MA 793 Caribou P. Creek LoP. AL 167 Caribou P. Creek HiP. AL 152 Como creek. CO 123 Coweeta 2. NC 854 Coweeta 18. NC 955 Coweeta 34. NC 955 Dry. VA 400 Emerald lake. CA 1410 Falling Creek. GA 300 Filson Creek. MN 270 Fort River. MA 507 Halfmoon Creek. GA 400 Holiday Creek. VA 350 Hubbard Brook. NH 800 Loch Vale. CO 604 ALoch Vale. CO 1127 Log Creek. CA 373 Martinell. CO 1507 Merced river. CA 665 Mundberry Brook. MA 562 Mt Moosilauke. NH 2400 Old Rag Mountain. VA 395 Old Rag Mountain. VA 400 Panola. GA 338 Panther Lake. NY 720 Pond Branch. ML 169 Rabbit Ears. CO 617 Rio Icacos. PR 3680 Silver Creek. ID 437 South Cascade. WA 2040 Tallulah river. GA 1160 Tarps Creek. CA 219 Tesque Aspin. NM 639 Tesuque Conifer. NM 829 Woods Lake. NY 760
T (jC)
P. Oliva et al. / Chemical Geology 202 (2003) 225–256
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