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Chemical Geology

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Silicate weathering rate and its controlling factors: A study from smallgranitic watersheds in the Jiuhua Mountains

Mingzhao Suna, Weihua Wua,⁎, Xiang Jib, Xiangli Wangc,d, Shuyi Qua

a Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, Chinab Zhejiang Provincial Institute of Cultural Relics and Archeology, Hangzhou 310014, Chinac Department of Marine Sciences, University of South Alabama, Mobile 36609, USAdDauphin Island Sea Lab, Dauphin Island 36528, USA

A R T I C L E I N F O

Editor: G. Jerome

Keywords:Subtropical regionGranitic watershedsChemical weathering ratesWeathering regimeControlling factors

A B S T R A C T

Silicate weathering is intimately linked to global climate. To investigate the controlling factors of silicateweathering rate, we collected monthly or half-monthly river water samples from ten small granitic watersheds inthe Jiuhua Mountains. Mass balance calculation shows that agricultural activity, atmospheric input, silicateweathering, carbonate weathering and evaporite dissolution contribute 2.0%, 16.9%, 58.7%, 13.5% and 8.9%cations to the river water, respectively. The disproportionate contribution of carbonate rocks with an exposedarea of< 5% demonstrates the importance of trace carbonate minerals in these small granitic watersheds. Theaverage silicate weathering and CO2 consumption rates at our sites are 7.0 t km−2 y−1 and3.4× 105mol km−2 y−1, respectively.

There is no correlation between silicate weathering rates (SWR) and the suspended solid concentrations (anindex of physical erosion rate), but there is a strong positive correlation between SWR and temperature, con-sistent with the kinetic-limited chemical weathering regime in alpine areas. A compilation of literature data fromglobal small granitic watersheds in different climatic zones shows positive correlations between temperature,runoff and SWR. A multiple linear regression between SWR, temperature and runoff yielded an adjusted R-squared of 0.61, showing that climatic factors can account for 61% of the variation in SWR. In addition, forcatchments where the elevation difference between the highest point of the headwater and sampling point (anapproximate topographical parameter) exceeds 400m, the R-squared value obtained from the temperature andSWR regression analysis is significantly increased, indicating that temperature is likely the most important factorcontrolling silicate weathering in the alpine region where a kinetic-limited regime dominates.

1. Introduction

Rock weathering plays a key role in the evolution of the Earthsurface. It is important to quantitatively understand the feedback be-tween climate and chemical weathering. Atmospheric CO2 concentra-tion is mainly controlled by the balance between input rate degassedfrom metamorphism and magmatism (carbon source) and consumptionrate by chemical weathering (carbon sink) on geological time scales(e.g., Chamberlin, 1899; Walker et al., 1981; Berner et al., 1983;Berner, 1995; Raymo and Ruddiman, 1992; White and Brantley, 1995;Berner and Berner, 1997; Kump et al., 2000). Several models have beenproposed to support that silicate weathering regulates global climatechange, including “the BLAG model” (Berner et al., 1983), “uplift-weathering” model (Chamberlin, 1899; Raymo et al., 1988; Raymo andRuddiman, 1992; Edmond, 1992), and “seesaw balance” model (Li and

Elderfield, 2013).Although silicate weathering plays important roles in regulating

long-term climate change, the factors and mechanisms controlling theweathering intensity and rate have not yet been well understood. Theconnection between silicate weathering, the global carbon cycle andclimate change is extremely complex, and many factors can affectweathering rates, including climatic (mainly temperature and runoff),tectonic (exposed lithology, elevation and physical erosion rates) andbiological influences (e.g., Velbel, 1992, 1993; Bluth and Kump, 1994;Drever and Clow, 1995; White and Blum, 1995; Finley and Drever,1997; Bowser and Jones, 2002; Bricker et al., 2003; Oliva et al., 2003;West et al., 2005; Price et al., 2005, 2012; Li et al., 2014). There aredifferent interpretations about the relative importance of these con-trolling factors. For instance, Bluth and Kump (1994) advocated thatwarm and humid climate or abundant rainfall may lead to high

https://doi.org/10.1016/j.chemgeo.2018.11.019Received 4 July 2018; Received in revised form 30 October 2018; Accepted 25 November 2018

⁎ Corresponding author.E-mail address: wuwh@nju.edu.cn (W. Wu).

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T

chemical weathering rates only with strong physical denudation rate.White and Blum (1995) proposed that the effects of temperature andprecipitation must be considered simultaneously when modeling thefeedback between weathering and climate. Meanwhile, they empha-sized the contribution of the tropical region to the global silicateweathering fluxes. Oliva et al. (2003) compiled the hydrochemical dataof 99 small granitic watersheds and obtained the highest silicateweathering rate in tropical watersheds. Recently, Li et al. (2014)modeled how temperature, precipitation and tectonics influence graniteweathering. They concluded that tropical region plays a major role inthe long-term global carbon cycle and that the CO2 consumption rate inthe tectonically active region is not dramatically higher than the tec-tonically stable region.

Two common methods were used to elucidate the controlling factorsof silicate weathering. One approach is leaching experiments in la-boratory that can observe the relationship between silicate mineral(quartz, feldspar, biotite, hornblende, and pyroxene) weathering andsolution chemistry (e.g., White et al., 1994; White and Brantley, 1995,2003; Blum and Stillings, 1995; Brantley and Chen, 1995; Nagy, 1995).Another way is to study small monolithological watersheds. Given theimportance of lithology in controlling the chemical weathering rate (forexample, the weatherability of mafic/ultramafic minerals is far higherthan that of felsic minerals), it is essential to focus on monolithologicalwatersheds. In addition, temperature and rainfall do not vary sub-stantially in a small watershed with an area of only several or tens ofsquare kilometers. Therefore, the study of small monolithological wa-tersheds can circumvent the multiplicity of factors controlling weath-ering. The comparison of small watersheds with the same lithology indifferent climatic zones can further identify the role of climate factorsin controlling chemical weathering.

To investigate the influence of different factors on granite weath-ering rate, we completed time series sampling during a hydrologicalyear in the Jiuhua Mountains to (1) estimate the contribution of an-thropogenic activity, atmospheric input, silicate weathering, carbonateweathering and evaporite dissolution to the dissolved load in thesestreams; (2) calculate silicate and carbonate weathering and CO2 con-sumption rates; and (3) evaluate the impact of climate factors (tem-perature and runoff), anthropogenic activity and physical erosion onsilicate weathering rate.

2. Study areas

The Jiuhua Mountains are located in Qingyang County, ChizhouCity, eastern China. The main peak has an elevation of 1344m. TheJiuhua Mountains are in subtropical monsoon zone with mild climateand abundant rainfall. The multi-year average temperature and pre-cipitation (snowfall in winter and rainfall throughout the year) are13.4 °C and 2437.5 mm, respectively (Qingyang County MeteorologicalAdministration). Vegetation coverage is 30%–60%, including farmland(1027 ha, 11%), garden land (3%) and forestland (86%), respectively(Jiuhua Mountains Management Committee).

The Jiuhua Mountains-Qingyang complex with an exposed area of750 km2 is situated in the NE Yangzte Block. The complex, crystallizedat 139–127Ma, consists of a main body (the Qingyang granite) ofgranodiorite and monzogranite and a central body (the JiuhuaMountains granite) mainly composed of alkali-feldspar granite (Xuet al., 2010). The complex intruded into sandstones, shales and lime-stones deposited in the late Precambrian to Early Paleozoic. The JiuhuaMountains alkali-feldspar granite has high Na2O+K2O contents (Xuet al., 2010). Moreover, the granite is enriched in Rb, Th, U, Nb, Ta andHf, and depleted in Ba, Sr, Nd, Sm, Eu, Gd and Ti relative to the pri-mitive mantle (Xu et al., 2010).

This study selected ten small granitic watersheds across the JiuhuaMountains granite, including six in the Qingtong River basin (originatefrom the Jiuhua Mountains and flow northward into the Yangtze River)and four in the Lingyang River basin (originate from the Jiuhua

Mountains and flow southward through the Taiping Lake, Qingyi Riverand eventually into the Yangtze River). In the Qingtong River samplinglocations, QTH1, QTH3 and QTH4 are located deep in the valley and farfrom the main road. QTH2 is located next to a village, while QTH5 andQTH6 are close to the road. In contrast, the Lingyang River watersheds(LYH7–LYH10) flow across primarily farmland. There is a simple calcitepowder-processing plant at approximately 200m upstream of LYH9.During the sampling, we found that the stream was usually milky afterheavy rain. The sampling locations are shown in Fig. 1. The multi-yearaverage runoff (2003–2015) in the Qingtong River watershed is1021mm (data from Hydrological Yearbook of China, Ministry ofWater Resources of China). The change in monthly water discharge ofthe Qingtong River is shown in Fig. 2. The geomorphological, hydro-logical and ecological characteristics of the Qingtong River and Lin-gyang River catchments are described in Appendix B.

3. Sampling and analysis

In a hydrological year from July 2014 to June 2015, stream sampleswere collected 1–2 times per month. During this period, 23 rainwatersamples were collected using a clean polyethylene wash basin near thesampling site QTH2 and then stored in bottle. Of six farmland watersamples, five were collected at the same location in the Qingtong Riverwatershed but at different times, and the remaining one was collectedat another site. A portable multi-parameter water quality analyzer(WTW 340i) was used to measure temperature, pH, conductivity andtotal dissolved solids in situ. Flow rate was measured using a propeller-type flow meter. Riverbed sediments were collected in June 2015.Water samples were collected from the river bank and stored in pre-cleaned polyethylene bottle, with no headspace.

The alkalinity of the unfiltered water samples was measured by adigital titrator (Hach 16,900) when returned to laboratory (singleequivalence point titration). Phenolphthalein alkalinities of all samplesare zero, so the bicarbonate alkalinity is equal to the total titrated al-kalinity. Samples of stream water, rain water and farmland water werefiltered through 0.45-μm Millipore filters. An aliquot of the filteredwater was acidified to pH < 2 with ultrapure grade 1:1 nitric acid.Ca2+, Mg2+, Na+, K+ and Si were measured in filtered and acidifiedwater with an inductively coupled plasma-optical emission spectro-meter (ICP-OES 6300) in the Key Laboratory of Surficial Geochemistry,Ministry of Education, School of Earth Sciences and Engineering,Nanjing University. The anions (F−, Cl−, NO3

− and SO42−) in the fil-

tered and unacidified water samples were measured using an ionchromatograph (ICS-1100). The measurement reproducibility was de-termined by repeated analysis of samples and standards, showing±5%precision for the cations and the anions.

Major element concentration of the riverbed sediments and bed-rocks was measured using XRF (ARL-9900) in the State Key Laboratoryfor Mineral Deposits Research, School of Earth Sciences andEngineering, Nanjing University. Absolute errors of Si and Al are±0.5% and±0.2%, respectively, and relative errors of other elementsare below 10%.

4. Results

Sampling information and hydrochemical data of river water,farmland water and rain water in the Qingtong River and LingyangRiver watersheds are listed in Tables 1–3 and Appendix A. Except forLYH9, the pH of other river water is 6.11–7.75 with an average of 7.20,being basically neutral. LYH9 has a pH of 6.88–8.61, which is relativelyalkaline and should be related to the upstream calcite processing plant.The temperature of river water is 5.7–27.2 °C. The pH of farmland wateris 6.45–7.38 with an average of 6.94, which indicates that fertilizationdoes not significantly change the pH. The pH of rain water is 3.81–5.81with an average of 4.76, showing the important influence of industrialactivities in surrounding cities.

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The total dissolved solids (TDS) concentration of river water in theQingtong River and the Lingyang River watersheds are34.3–94.5 mg L−1 (average 48.8 mg L−1) and 37.1–324mg L−1

(average 69.3 mg L−1), respectively, more than twice the average of theglobal granitic watersheds (22.7mg L−1, Oliva et al., 2003). The totalcation (TZ+) value of the river water is 375–1683 μeq L−1 and the totalanion (TZ−) value is 326–4683 μeq L−1. The normalized inorganiccharge balance (NICB= (TZ+− TZ−) / (TZ++TZ−)) is used to esti-mate overall analytical uncertainty and should ideally be near zero.NICB is within±10% for most of the samples. For the samples whereNICB was outside of 10%, we used the HCO3 concentration calculatedfrom the charge balance, rather than the titration, because for un-filtered samples, the titration acid will react with sediment particles,biasing the calculated alkalinity.

We used Na-normalized molar ratios (Ca/Na, Mg/Na and HCO3/Na)to remove the changes in absolute concentration due to dilution andevaporation processes. As shown in Fig. 3a and b, there are positivecorrelations between Ca/Na and Mg/Na (r2= 0.62), Ca/Na and HCO3/

Na (r2= 0.47), and most of the samples cluster around the silicate endmember (data from Gaillardet et al., 1999). Moreover, LYH8 and LYH9are the closest to evaporite and carbonate end members, respectively.

The TDS concentration of rain water is 5.7–36.4 mg L−1 with anaverage of 16.2mg l−1. The TDS concentration of farmland waterranges widely from 26.9 to 105mg L−1, which may be attributed todifferences in fertilization activities during these sampling periods.

The major element contents of riverbed sediments and bedrocks arelisted in Table 4. Both sediments and bedrocks have high K2O contents(4.26–5.59% and 4.82–4.94%, respectively). The CaO content in mostsamples is< 1% except LYH9 and LYH10.

5. Discussion

5.1. Spatial and temporal variations of major ion concentration

The temporal variation of river dissolved load content is closelyrelated to the hydrological processes in the catchment. The headwatersof Qingtong River and Lingyang River are very small mountain streamand there is no authoritative hydrological station. In addition, the flowrate, water depth and width of stream sections are quite different due tothe steep terrain and abundant distribution of pebbles and boulders inthe streams, which results in a large error in the calculated water dis-charge. Therefore, in order to discuss the influence of discharge changeson the dissolved load in the stream, we use the data of the QingtongRiver at Qingyang Hydrological Station, which is< 20 km downstreamof the sampling area. Although the absolute water discharge of theQingyang Hydrological Station is definitely higher than our samplingarea, the change trend in a year between them should be very similar inthe distance of ~20 km. The temporal variations of TDS in the tenwatersheds and water discharge at Qingyang Hydrological Station areshown in Fig. 4. Samples are divided temporally into four seasons:spring (March to May), summer (water-rich period, June to August),autumn (September to November) and winter (water-lean months,December to February). Overall, there was a negative correlation

Fig. 1. The geological map and sam-pling locations for small granitic wa-tersheds in the Jiuhua Mountains(Modified from 1:200000 GeologicalMap, China Geological Survey). ξπ:Yanshanian orthophyre, γ: Yanshanianfine-grain granite, γδ: Yanshaniangranodiorite, JJH R.: the JiangjunhuReservoir. The arrow indicates the di-rection of the water flow. The filleddiamonds and stars are the samplingpoints of farmland water and rainfallstation, respectively.

Fig. 2. The monthly runoff data from multi-year average and sampling year ofthe Qingtong River at Qingyang Hydrological Station (the data are from theYearbook of Hydrology, the Ministry of Water Resources of China).

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between water discharge and TDS concentrations. The variation rangeof TDS in the Qingtong River catchment is much smaller than that of thedischarge, showing chemostatic behavior over the year. Since there isbasically no farmland distribution and industrial activities in theQingtong River catchment, the dissolved load in the stream is almostentirely derived from rock weathering. Considering the very slowweathering dynamics, it can adapt to seasonal variations of discharge,which results in relatively small change of TDS concentrations. Com-pared with the Qingtong River, the TDS of the Lingyang River catch-ment with some agricultural and industrial activities has a much largerchange, probably because the input of human activities is more sus-ceptible to some occasional factors. Although the distance betweenLYH7 and LYH8 is only a few hundred meters apart, their TDS con-centrations exhibit very different characteristics. LYH7 has TDS con-centration similar to QTH1, but LYH8 is 1.5–2.5 times higher thanQTH1. Compared with LYH7, LYH8 is a tiny stream with a widthof< 1m and a depth of about 10 cm, and thus it is extremely suscep-tible to the contribution of agricultural activities or trace carbonate/evaporite minerals. This effect is even more pronounced in autumn andwinter due to low water discharge. LYH9 shows the important influencefrom nearby calcite powder-processing plant, especially in the dryseason. Therefore, when weathering and CO2 consumption rates arediscussed below, LYH9 will be removed. LYH10 flowing across re-sidential areas has high major ion concentrations, which may be at-tributed to the contribution of agricultural activities and domestic

sewage.Similar to TDS, most of the major ions in the Qingtong River wa-

tersheds exhibit chemostatic behavior in a hydrological year (Fig. 5).NO3

− concentrations in May–October (late spring, summer and earlyautumn) are significantly lower than other periods, probably becausethe agricultural fertilization is mainly concentrated in spring and au-tumn. SO4

2− concentrations are higher in summer with abundantrainfall except LYH8, reflecting the contribution of acid rain in thisarea. Spatially, several small watersheds show special major ion char-acteristics. LYH8 has high Ca2+ and the highest Na+ and SO4

2− con-centrations in all samples. Na2SO4 is mainly used in industrial pro-duction, but there is no relevant factory in this region. Therefore, themost reasonable explanation is the contribution of evaporite mineral,such as mirabilite (Na2SO4·10H2O) and gypsum. Geological data showthat the Jiuhua Mountains region was an evaporite platform duringTriassic period, and that gypsum was intercalated in dolomite (Bureauof Geology and Mineral Exploration of Anhui Provincial, 1987). If thisinference is correct, then why LYH7, which is only a few hundreds ofmeters away from LYH8, is not significantly affected by evaporite dis-solution? The discharge areas of LYH7 and LYH8 are 4.5 km2 and0.4 km2, respectively, both of which are predominantly covered bygranite. Considering that the dissolution rate of evaporite is nearly twoorders of magnitude higher than that of granite, if sparse evaporite isexposed in the two watersheds, its effect on LYH8 must far exceedLYH7. LYH9 exhibits high Ca2+, Mg2+ and HCO3

– concentrations,

Table 1Sampling information and water chemistry data of streams, farmland water and rainwater in the Qingtong River and Lingyang River watersheds.

Num. Areaa Runoffb T pH Ca Mg K Na Cl HCO3 SO4 NO3 Si

km2 mm °C μmol L−1

QTH1 3.58 55–1044 8.9–25.2 6.34–7.53 96–199 26.3–44.3 102–166 6.8–16.9 21.1–58.3 55.7–108 81.5–132 78.4–241 160–251(0) 321 16.8 7.03 127 33.7 128 10.9 37.2 83.7 100 150 197

QTH2 7.15 20.2–575 7.9–26.4 6.77–7.75 133–213 37.2–65 134–235 12.9–25.1 29.2–55.8 123–328 81.1–133 77.3–179 53.5–293(0.274) 205 17.2 7.30 164 49.2 182 17.3 42.6 222 108 119 247

QTH3 2.94 17.1–440 5.7–24.6 6.61–7.51 86.5–172 26.6–46.2 141–287 11.0–22.1 21.9–44.3 91.8–279 73.0–102 73.5–225 199–357(0) 112 15.9 7.20 111 35 201 14.4 32.8 179 86 124 297

QTH4 1.58 35.2–778 6.4–26.2 6.57–7.45 74.3–111 23.5–35.4 162–225 10.9–22.0 22.5–41.1 103–220 60.4–104 65.6–124 222–367(0) 200 15.8 7.20 90.9 28.5 194 15.5 30.4 174 74 92.4 319

QTH5 4.31 34.8–546 6.6–25.2 6.96–9.69 145–410 32.2–63.9 161–332 12.8–24.2 28.7–46.6 159–713 92.5–126 66.1–172 244–367(0) 193 16.2 7.50 187 50.6 195 16.8 36.0 302 107 103 301

QTH6 1.64 54.6–1194 7.4–23.4 6.93–7.54 141–213 32.3–45.5 183–261 9.5–17.2 29.3–47.4 118–226 63.1–145 140–274 217–339(0) 288 15.9 7.20 168 36.8 224 12.2 38.3 183 114 182 296

LYH7 4.16 35.6–990 7.6–27.2 6.65–7.59 96.1–155 26.2–40.8 139–192 9.9–19.4 27.0–53.8 113–238 67.8–102 54.6–204 181–295(0.073) 241 17.3 7.20 118 31.5 164 13.8 36.7 169 83.4 104 257

LYH8 0.514 31.7–982 8.5–23.8 6.75–7.53 135–327 29.0–43.9 246–649 10.9–21.2 31.1–43.9 131–493 134–284 38.0–130 244–481(0.009) 311 16.5 7.20 205 34.7 429 14.8 38.5 336 201 73.2 384

LYH9 1.47 62.4–3239 7.1–23.3 6.88–8.61 130–649 36.8–107 98–248 12.4–30.0 25.2–65.6 212–4426 51.2–85 7.9–108 146–329(0.106) 702 15.9 7.80 300 66.9 191 16.9 41.7 838 66 41.0 278

LYH10 6.16 10.3–945 7.8–24.0 6.61–7.4 141–409 55.4–123 115–273 19.7–51.6 40.4–87.2 243–697 94.6–134 65.9–238 144–334(0.497) 230 17.3 7.10 243 80.5 197 29.2 56.7 418 110 122 258

b.l.; below the detection line.a The top row is the drainage area, and the lower row is farmland area.b The top row is the range of value, and the lower row is an average value, same as other columns.

Table 2Sampling information and water chemistry data of farmland water in the Qingtong River watersheds.

Num.a Date Runoff T pH Ca Mg K Na Cl HCO3 SO4 NO3 Si NICB

mm °C μmol L−1

FW1 2014/7/8 483 29.7 6.82 197 60.6 367 93.2 597 95.5 101 30.5 61.6 0.03FW2 2014/9/23 483 15.7 7.22 290 114 323 24.5 712 68.4 101 15.3 149 0.07FW3 2015/4/11 483 22.2 6.83 285 114 313 25.6 738 70.5 117 70.0 344 0.01FW4 2015/5/22 483 35.4 6.93 98.6 32.2 180 20.5 203 13.4 94.8 0.00 270 0.06FW5 2015/6/29 483 33.5 7.38 256 47.0 128 15.6 562 29.5 70.9 9.40 151 0.00FW6 2015/6/29 483 34.2 6.45 87.8 29.8 97.7 18.6 207 15.8 43.7 6.70 68.7 0.05

a The sampling site of FW1–FW5 is at E 117°52′55″, N 30°28′00″, near the site of QTH1, The sampling site of FW6 is at E117°53′06″, E30°28′11″, near the site ofQTH2.

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which are clearly affected by the upstream calcite powder processingplant.

Therefore, the temporal variations of the major ions in the smallgranitic watersheds are related to the water discharge, and the spatialvariations are more susceptible to human activities and the slight

lithological differences in different sampling points.

5.2. Source of major ions in the river water

The budget equation of any dissolved element X in river water canbe written as follows (Galy and France-Lanord, 1999):

= + + + +X X X X X Xriver cyclic anthropogenic eva carb sil (1)

where the subscript ‘cyclic’ represents atmospheric input, ‘eva’ is theinput of evaporite dissolution, ‘carb’ and ‘sil’ represent the inputs ofcarbonate and silicate weathering, respectively, and ‘anthropogenic’ isanthropogenic input. In order to quantify the contribution of thesesources, a forward model was used (Galy and France-Lanord, 1999;Mortatti and Probst, 2003; Moon et al., 2007; Wu et al., 2008).

5.2.1. Anthropogenic inputThe impact of anthropogenic input mainly comes from agricultural

fertilization and domestic sewage, because there are no industrial ac-tivities in our sampling watersheds except for LYH9. There is almost nofarmland in the hinterland of the Jiuhua Mountains, and only a fewpaddy-fields are distributed in piedmont area. Major ion concentrationsof the farmland water change noticeably at different sampling times,with a TDS range of 26.9–105mg L−1 (Table 2). TDS concentrationdoes not show a seasonal change, suggesting that it is mainly controlledby fertilization activities and fertilizer types. The proportions of ni-trogen fertilizer, phosphate fertilizer, potash fertilizer and compoundfertilizer used in the farmland in 2013 are 44.6%, 11.1%, 12.2% and34.4%, respectively (data from the Qingyang County Agricultural Bu-reau). The annual water consumption for farmland irrigation is4830m3 ha−1 (the corresponding farmland runoff depth is 483mm)(2015 Chizhou City Water Resources Bulletin, Chizhou Water Au-thority). Agricultural input (Q(i)AGR, mol y−1) can be estimated by thefollowing modified equation (Pierson-Wickmann et al., 2009):

= × ×i iQ( ) C ( ) R SAGR AGR AGR AGR (2)

where CAGR(i) (μmol L−1) is the average concentration of solute i infarmland water and RAGR is the annual farmland runoff depth; SAGR (ha)represents farmland area in the watershed, which can be estimatedaccording to the on-site investigation combined with google map

Table 3Sampling information and water chemistry data of rainwater in the Qingtong River watersheds.

Num. Date pH Ca Mg K Na Cl HCO3 SO4 NO3 Si NICB

μmol L−1

RW1 2014/7/26 3.96 38.4 4.81 32.3 10.9 14.7 b.l. 165 109 3.94 −0.56RW2 2014/7/27 5.72 10.9 0.66 7.87 5.48 2.62 36.1 10.0 7.41 5.72 −0.29RW3 2014/7/31 5.56 17.2 1.36 70.8 19.7 31.3 37.7 30.4 22.3 2.76 −0.09RW4 2014/8/10 3.98 41.2 7.28 17.2 16.5 23.4 b.l. 141 97.2 3.50 −0.51RW5 2014/8/14 3.81 12.8 2.63 21.1 7.22 14.9 b.l. 114 114 2.80 −0.72RW6 2014/8/23 4.85 42.0 4.65 22.7 15.0 13.8 34.4 64.1 41.7 5.93 −0.25RW7 2014/8/28 5.24 38.0 3.70 25.5 6.57 14.2 41.0 63.4 54.7 5.24 −0.34RW8 2014/9/1 4.52 9.3 0.00 12.8 4.52 9.30 16.4 30.7 25.5 0.19 −0.52RW9 2014/9/11 4.48 19.2 1.81 31.2 8.17 12.7 b.l. 80.7 42.6 2.71 −0.45RW10 2014/9/18 4.21 27.4 3.62 21.0 8.83 20.5 b.l. 75.8 97.0 1.77 −0.49RW11 2014/9/30 4.46 29.7 4.57 12.1 23.2 47.3 21.3 131 132 6.15 −0.63RW12 2014/10/29 4.37 18.4 2.18 50.2 9.30 13.7 b.l. 62.5 41.4 2.61 −0.28RW13 2014/11/5 4.43 41.4 5.97 53.8 23.3 24.5 b.l. 95.2 81.9 6.53 −0.27RW14 2014/11/24 4.43 7.60 0.33 18.2 3.17 7.90 b.l. 33.6 18.2 2.07 −0.43RW15 2014/11/30 5.02 18.8 2.10 36.5 7.26 15.1 21.3 48.4 44.6 4.85 −0.35RW16 2015/1/13 5.36 10.4 0.53 8.26 7.74 11.4 32.8 22.1 28.9 4.35 −0.51RW17 2015/2/15 4.45 41.9 9.67 82.0 28.3 20.6 39.3 78.2 100 32.5 −0.19RW18 2015/2/27 4.71 6.10 0.41 8.33 2.17 8.72 18.0 28.9 30.6 0.87 −0.66RW19 2015/3/14 4.43 59.8 7.41 17.3 13.4 38.9 b.l. 105 145 3.84 −0.41RW20 2015/3/20 4.73 24.6 2.02 11.0 6.52 17.0 9.84 56.3 47.4 1.45 −0.45RW21 2015/3/26 5.81 61.5 7.41 15.8 36.2 52.3 62.3 64.4 70.5 5.30 −0.25RW22 2015/4/3 5.64 41.1 3.42 13.6 10.5 18.5 27.9 45.5 45.7 9.58 −0.24RW23 2015/6/3 5.46 33.8 4.90 18.1 17.2 15.1 57.4 25.3 36.3 13.3 −0.17

Fig. 3. Molar ratios of Mg/Na and Ca/Na (a), HCO3/Na and Ca/Na (b). Endmember compositions for carbonate, silicate and evaporite are from Gaillardetet al. (1999).

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(Table 1). The calculated contribution of agricultural input to totalcations is 0–10.8% with an average of 2.0%. In the Qingtong Riverwatersheds, only QTH2 has farmland distribution, and the contributionof agricultural input is 5.5%. In the Lingyang River watersheds, thecontribution is 1.6–7.9%, with an average of 4.2%.

5.2.2. Atmospheric inputThe sampling information and hydrochemical data of rainwater are

listed in Table 3. SO42− and NO3

− concentrations are relatively high,averaging 73.7 and 69.8 μmol L−1

, respectively. It shows that the effectof acid rain is obvious even in the mountain areas, consistent with thelow pH characteristics of rainwater. The Jiuhua Mountains are onlytens of kilometers away from some densely populated cities, indicatingthat industrial emissions from adjacent cities have a substantial influ-ence on the precipitation chemistry. Atmospheric input is important forthe dissolved load in the river water and must be corrected beforecalculating chemical weathering rate and flux. The contribution of theatmospheric input to the soluble element X in the river water is esti-mated as follows (Stallard and Edmond, 1981; Gupta et al., 2011):

= ×∗X X /Cl Clrain rain rain min (3)

where Xrain and Clrain represent the concentrations of element X Cl inrainwater, respectively; Clmin is the lowest concentration of Cl in allriver water samples (21.1 μmol L−). The calculated contribution of at-mospheric input to total cations ranges from 6.8% to 25.7% with an

average of 16.9%. It shows that under the influence of acid rain, thecontribution of atmospheric input is more important.

5.2.3. Rock weatheringBefore calculating the contribution of carbonic acid–based rock

weathering to the dissolved load in the streams, we first need to un-derstand the effects of sulfuric acid and nitric acid on chemicalweathering.

Studies have shown that sulfuric acid also reacts with silicate andcarbonate minerals, and produces cations and HCO3

– into the ocean, aprocess that does not consume atmospheric CO2 (e.g., Spence andTelmer, 2005; Calmels et al., 2007; Torres et al., 2016). Therefore, thispart of the cation needs to be subtracted when calculating the chemicalweathering and atmospheric CO2 consumption rate. Both sulfate dis-solution and sulfide oxidation are the source of SO4

2− in river water,however, it is difficult to identify their respective contributions.Weathering of carbonates or Ca-Mg silicates by carbonic acid generatesequivalent charge units of Ca2++Mg2+ and HCO3

−. In the smallgranitic watersheds of the Jiuhua Mountains, Ca2++Mg2+ cannot bebalanced by HCO3

– (the slope= 0.68, r2= 0.69), but are basicallybalanced by HCO3

–+SO42− (the slope=1.1, r2= 0.76), indicating

relatively important contribution from gypsum dissolution and/orpyrite oxidation besides the carbonic acid–based weathering. In the twoprocesses, gypsum dissolution produces equivalent Ca2+ and SO4

2−,and this process has no acid involvement. Pyrite oxidation forms sul-furic acid, which reacts with silicate minerals and carbonate minerals toproduce two and one moles of Ca2+ for one mole of SO4

2−, respectively(Spence and Telmer, 2005). Considering that the exposure proportionof carbonate rock in the small granitic watersheds does not exceed 5%,we assume that the Ca2+ from gypsum dissolution and sulfuric acid-based weathering is equal to SO4

2− in the stream after correction byatmospheric input. This portion of Ca2+ will be deducted in the cal-culation of the chemical weathering and CO2 consumption rate below.

Agricultural activities also have an impact on the dissolved load inriver water. For example, in two upland granitic catchments in France,fertilization causes soil acidification, and then the cations are releasedinto the river water by ion exchange leaching in the soil, therebyoverestimating the CO2 consumption by carbonation-based silicateweathering (Pierson-Wickmann et al., 2009; Fortner et al., 2011).Moreover, nitric acid produced by nitrification of N-fertilizers sub-stitutes carbonic acid and weathers rock, which reduces the consump-tion of atmospheric CO2 (Perrin et al., 2008; Pacheco et al., 2013; Liand Ji, 2016). The small granitic watersheds of the Jiuhua Mountainscontain a certain number of nitrate ions with an average concentrationof 112 μmol L−1. The NO3

− in river is mainly from atmospheric inputand agricultural activities. The average NO3

− concentrations in thefarmland water and rain water in the Jiuhua Mountains are22 μmol L−1 and 62 μmol L−1, respectively. In addition, the proportionof farmland in each Jiuhua Mountains watershed is very small (0–8%,mean 2.3%). Therefore, the agricultural activities have relatively small

Table 4Major element compositions of riverbed sediment in the Jiuhua Mountains watersheds (wt%).

Num. SiO2 TiO2 Al2O3 Fe2O3 MnO MgO CaO Na2O K2O P2O5 LOI Sum

QYH1 80.3 0.185 10.1 1.03 0.042 0.31 0.66 2.08 4.40 0.025 0.59 99.7QYH2 76.8 0.139 12.1 0.74 0.019 0.25 0.94 2.44 5.59 0.037 0.59 99.6QYH3 79.0 0.144 10.8 0.72 0.022 0.22 0.67 2.19 5.05 0.014 0.40 99.3QYH4 83.7 0.095 8.47 0.29 0.018 0.12 0.54 1.6 4.42 0.010 0.60 99.8QYH5 77.5 0.293 11.2 1.25 0.027 0.36 0.96 2.31 4.65 0.029 0.20 98.8QYH6 75.6 0.299 12.1 2.07 0.052 0.27 0.82 2.59 5.15 0.018 0.20 99.2LYH7 81.3 0.137 9.96 0.67 0.031 0.21 0.62 1.88 4.89 0.021 0.19 99.9LYH8 79.4 0.132 10.9 0.53 0.028 0.19 0.54 1.93 5.50 0.008 0.20 99.4LYH9 74.7 0.284 12.7 1.97 0.034 0.51 1.52 2.85 4.26 0.063 0.39 99.2LYH10 67.3 0.515 15.7 3.03 0.057 0.68 1.47 3.57 4.34 0.075 2.39 99.2R1 72.8 0.206 14.5 0.97 0.072 0.56 1.32 3.85 4.94 0.20 99.5R2 75.5 0.076 12.8 0.62 0.057 0.12 0.54 3.93 4.82 0.99 99.5

Fig. 4. Temporal variations of TDS and water discharge in the JiuhuaMountains watersheds. The water discharge at Qingyang Hydrological Stationis from the Yearbook of Hydrology (Ministry of Water Resources of China).

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contribution to the nitrate ions in the streams. The influence of nitricacid produced by agricultural activities to chemical weathering is ne-glected in this paper.

After deducting the contributions of atmospheric input and an-thropogenic activities (the corrected concentration of major ion X isshown below as X*), we simplify the forward model in the followingthree aspects. First, the Cl⁎ comes only from the halite dissolution.Second, carbonate weathering does not affect dissolved Na and K.Finally, Ca2+ from gypsum dissolution and sulfuric acid weathering isequal to SO4

2− in the stream except for LYH8. Considering very highCa, Na and SO4 concentrations of LYH8, we assume that gypsum andmirabilite each account for 50% of the evaporite mineral in this wa-tershed.

With these assumptions, Eq. (1) can be simplified as follows (Galyand France-Lanord, 1999):

=∗Cl Cleva (4)

= −∗Na Na Clsil eva (5)

= ∗K Ksil (6)

=+∗Ca SOsulfide sulfate 4 (7)

= + +∗Ca Ca Ca Casil carb eva (8)

= +∗Mg Mg Mgsil carb (9)

In river water, Ca and Mg derived from silicate weathering (Casil

and Mgsil) can be written as (Galy and France-Lanord, 1999; Wu et al.,2008):

= ×Ca Na (Ca/Na)sil sil sol (10)

= ×Mg Na (Mg/Na)sil sil sol (11)

Based on the major element data of riverbed sediment in thesewatersheds (Table 4), (Ca/Na)sol and (Mg/Na)sol molar ratios can beobtained (0.31–0.59 and 0.116–0.295, respectively).

According to the above calculation results, the contribution of sili-cate weathering, carbonate weathering and evaporite dissolution tototal cations can be expressed as:

∑ = + + +

+ + +

( Cat) (Na K 2Ca 2Mg )

/(Na K 2Mg 2Ca )sil sil sil sil sil

riv riv riv riv (12)

∑ = + + + +( Cat) (2Ca 2Mg )/(Na K 2Mg 2Ca )carb carb carb riv riv riv riv

(13)

∑ = + + +( Cat) (2Ca )/(Na K 2Mg 2Ca )eva eva riv riv riv riv (14)

In the Qingtong River and Lingyang River watersheds, the average(∑Cat)sil, (∑Cat)carb and (∑Cat)eva of 172 samples (except LYH9) are60.6%, 12.4%, 7.9% and 54.5%, 16.1%, 10.9%, respectively. Thecontributions of different sources are illustrated in Fig. 6.

Many studies have pointed out that trace calcite in silicate wa-tershed can greatly affect the dissolved load concentration in river

Fig. 5. Temporal variations of major ion concentrations in the Jiuhua Mountains watersheds.

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water. For example, studies of silicate watershed in the High HimalayanCrystalline Series show that> 80% of the Ca2+, Mg2+ and HCO3

–

fluxes are derived from the weathering of carbonate minerals (Blumet al., 1998; Harris et al., 1998; White et al., 1999b; Jacobson et al.,2002; Bickle et al., 2015). Moreover, this phenomenon was also foundin the Southern Alps of New Zealand, the Australian Victorian Alps, theDeccan basalt and the Iceland basalt (Dessert et al., 2001; Jacobsonet al., 2003, 2015; Oliva et al., 2004; Hagedorn and Cartwright, 2009;Gupta et al., 2011; Moore et al., 2013). By using a reactive-transportmodel, Li et al. (2014) pointed out that trace calcite contributes dis-proportionately to the dissolved Ca flux in the silicate watershed, andthat the relative contents of silicate- and carbonate-derived Ca are ne-gatively correlated with uplift and erosion rates. In these small graniticwatersheds of the Jiuhua Mountains, the average contribution of car-bonate weathering is 13.5%, which is close to our earlier study of theXishui River, a tributary of the Yangtze River that flows across high-grade metamorphic rocks (mainly amphibolites and gneisses) (Wuet al., 2013). There is no carbonate outcrop in the 1:200000 geologicalmaps, and only one calcite particle was identified in the ten slices ofriverbed sediments under the microscope. However, we did find twocarbonate rock quarries in the region. Taken together, we speculate thatthe proportion of exposed carbonate rocks should be<5%. Given thehigher dissolution rate of carbonate minerals relative to silicate mi-nerals, it is not surprising that< 5% of carbonate rocks in these graniticwatersheds contribute 13.5% of the dissolved load.

5.3. Chemical weathering and atmospheric CO2 consumption rates

Based on the silicate- and carbonate-derived cations and runoffdata, the chemical weathering and atmospheric CO2 consumption rates

of silicate and carbonate in the Jiuhua Mountains can be calculatedusing the following equations (Négrel et al., 1993; Roy et al., 1999; Wuet al., 2008):

= + + + ×SWR (Ca Mg Na K ) runoffsil sil sil sil (15)

= + ×CWR (Ca Mg ) runoffcarb carb (16)

= = + + + ×+[ΦCO ] [ΦTZ ] (2Ca 2Mg Na K ) runoff2 sil sil sil sil sil sil (17)

and

= = + ×+[ΦCO ] [ΦTZ ] (Ca Mg ) runoff2 carb carb carb carb (18)

In Eqs. (15) and (16), concentrations of cation are in mg L−1, and inEqs. (17) and (18), they are in μmol L−1. As the water discharge datacalculated from flow rate, stream depth and width measured in situhave relatively large error, authoritative multi-year average runoff dataof the Qingtong River at Qingyang Hydrological Station is used (Year-book of Hydrology, the Ministry of Water Resources of China). Thishydrological station is only about 20 km away from our Qingtong Riversampling points and reflects the average runoff of the upstream114 km2 drainage area. In addition, the 7-year average rainfall differ-ence between the three rainfall stations near the six sampling points inthe Qingtong River (data from the authoritative Hydrographic Bureau,Li, 2016) is 2%–12%, indicating that the rainfall in the sampling area isrelatively consistent. The Rainfall Station near Qingyang HydrologicalStation has a 7-year average rainfall of 13–25% lower than the otherthree stations in the sampling area. Since 98% of the runoff in theQingtong River catchment comes from surface water, the difference inrainfall between Qingyang County and the sampling locations basicallyreflects the error of runoff. Therefore, the uncertainty in the calculationof chemical weathering and CO2 consumption rate is about 13–25%.

Eq. (17) represents a short-term CO2 consumption rate from silicateweathering. Over the longer term, Na and K are involved in ‘reverseweathering’ reactions that convert HCO3

– to CO2 and returns to theatmosphere (Gaillardet et al., 1999). Therefore, the CO2 consumptionrates from carbonate weathering, Ca and Mg silicate weathering andtotal silicate weathering are given separately in Table 5.

5.4. Factors controlling chemical weathering rates

Chemical weathering rate is controlled by multiple factors, whichcan be grouped into inter-related climatic (temperature and runoff) andtectonic factors (rock type and physical erosion). Research on a smallmonolithological watershed effectively eliminates the influence of rocktype, but it has proved hard to distinguish climate factors from tectonicfactors. Chemical weathering has two modes: supply-limited and ki-netic-limited (Riebe et al., 2004, 2017; West et al., 2005; Gabet and

Fig. 6. Percentage of contribution of different sources to major cations in theJiuhua Mountains watersheds.

Table 5Chemical weathering rate and CO2 consumption fluxes in the Jiuhua Mountains.

Date Runoff SWR CWR ΦCO2sil ΦCO2sil (Ca+Mg) ΦCO2carb

mm y−1 t km−2 y−1 t km−2 y−1 105mol km−2 y−1 105 mol km−2 y−1 105 mol km−2 y−1

Jan. 388 3.59 0.93 1.72 0.86 0.51Feb. 770 6.45 2.42 3.10 1.55 1.31Mar. 1236 8.82 3.62 4.26 2.18 1.95Apr. 1011 6.99 2.09 3.39 1.76 1.14May. 1165 7.98 1.68 3.82 1.94 0.91Jun. 1720 11.2 3.43 5.32 2.66 1.77Jul. 2037 13.3 1.84 6.46 3.28 1.06Aug. 1568 11.4 1.65 5.50 2.74 0.92Sep. 997 7.26 1.24 3.49 1.71 0.68Oct. 445 3.65 0.67 1.76 0.85 0.36Nov. 518 3.57 1.01 1.71 0.84 0.56Dec. 399 2.79 0.54 1.35 0.67 0.30Averagea 1021 7.0 1.5 3.4 1.6 0.80

a The average values are calculated from all 171 samples except for LYH9 in a year.

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Mudd, 2009; Dixon et al., 2012; Ferrier et al., 2016). When erosion rate(mainly physical erosion) is low, chemical weathering is a supply-lim-ited, and is limited by the supply of fresh rock and is proportional to theerosion rate. When erosion rate is high, chemical weathering is kinetic-limited. Rocks are fully exposed under high erosion conditions, and theresidence time of weathering fluid is short, resulting in weak weath-ering intensity. The chemical weathering flux is no longer limited bythe supply of fresh rock, but by kinetic factors such as temperature andprecipitation. The kinetic-limited regime forms a negative feedbackbetween chemical weathering and atmospheric CO2 concentration,which is the key to maintain the balance of the global carbon cycle(Walker et al., 1981; Berner et al., 1983). Therefore, it is particularlyimportant to distinguish the role of temperature from other factors.

5.4.1. Relation between chemical weathering and physical erosionRiver total suspended solids (TSS) are classically used to provide

estimate of physical erosion. In general, there are two main limitationswith this estimate: one is human impact, including agricultural activ-ities and dams. Another is natural impact, that is, sediments erodedfrom highlands are deposited in lowlands, floodplains or reservoirs(Milliman and Meade, 1983; Gaillardet et al., 1999). However, the twolimitations have little impact on the small stream in the JiuhuaMountains due to very few farmlands, and only one reservoir is locateddownstream of the sampling points.

We plotted the silicates weathering rates (SWR) and TSS (Fig. 7a)for the 172 samples in a hydrological year in the Jiuhua Mountains.There is no correlation between the TSS and SWR (r2= 0.003). In orderto minimize the impact of storm events and anthropogenic activities,the annual average TSS and SWR values for each watershed are used.The results show that there is also no correlation between TSS and SWR(r2= 0.009, Fig. 7b), indicating that the physical erosion rate is not themain factor controlling the chemical weathering rate in the alpine areas

with a kinetic-limited regime.

5.4.2. Climatic factorsWater is essential for chemical weathering and transportation of

weathering product to the ocean. Therefore, humid areas may undergomore intense chemical weathering and consume more atmospheric CO2

when other factors are similar. In general, there are high weatheringrates in warm and wet watersheds (White and Blum, 1995). Gaillardetet al. (1999) compiled data of the 60 largest rivers in the world andfound that runoff has a strong control over chemical weathering rate,which was also suggested in studies of some smaller watersheds(Gislason et al., 1996; Louvat and Allegre, 1997, 1998; Dessert et al.,2001; Stefansson and Gislason, 2001; Das et al., 2005; Goldsmith et al.,2010; Balagizi et al., 2015). There is a strong positive correlation be-tween monthly average SWR and runoff in the Jiuhua Mountains(r2= 0.98) (Fig. 8). However, as proposed by Gaillardet et al. (1999),weathering rate is obtained by multiplying the cation concentrations bythe runoff, and thus the correlation between them is inevitable. Themain implication is that with increasing runoff, the chemical weath-ering rate increases and counterbalances dilution.

During weathering, the dissolution rate of primary minerals mayincrease with temperature according to the Arrhenius equation (Whiteand Blum, 1995; Dessert et al., 2003). In addition, many laboratoryexperiments and field catchment-scale calculations also show that sili-cate weathering rates indeed increase with temperature (Velbel, 1993;Brady and Carroll, 1994; White and Blum, 1995; White et al., 1999a;Dalai et al., 2002; Gislason et al., 2009; Li et al., 2016). However,chemical weathering rates are not always low under cold climate con-ditions, such as Arctic Scandinavia (Rapp, 1960; Campbell et al., 2002),Colorado Front Range (Caine, 1992), the Canadian Rockies (Smith,1992), Banks Island (Canada) (Lewkowicz, 1988), eastern Siberia (Huhand Edmond, 1999) and polar desert Taylor Valley of Antarctica (Nezat

Fig. 7. Scatter plots of silicate weathering rate (SWR) vs. total suspended solid(TSS). (a) The data are for all 171 samples of nine small watersheds except forLYH9 in a hydrological year. (b) The data are for annual average value of everywatershed except for LYH9.

Fig. 8. Scatter plots of SWR vs. runoff (from Qingyang County MeteorologicalBureau), the data are for the monthly average of nine small watersheds exceptfor LYH9. There is a strong linear correlation between the runoff and SWR.

Fig. 9. Scatter plots of SWR vs. temperature (from Qingyang CountyMeteorological Bureau), the data are for the monthly average of nine smallwatersheds except for LYH9.

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Table 6Comparison of silicate weathering rates in the Jiuhua Mountains with other small granitoid watersheds worldwide.

Watershed Names Runoff(mm)

Temp.(°C)

ΔElem

SWR(t km−2 y−1)

Data sources

Jiuhua Mountains 1021 16.4 460 7.00 This studyExper. Lake 225 2.4 0.47 White and Blum, 1995Hanley A. 8. C. 1010 9.2 310 3.37 White and Blum, 1995Hanley B B.C. 1240 9.2 310 4.37 White and Blum, 1995Henley C. BC 1040 9.2 310 3.91 White and Blum, 1995Jamieson Ck. B. C. 3668 3.4 976 7.84 White and Blum, 1995Rawson Lake E. Ont. 277 2.4 80 0.85 White and Blum, 1995Rowson Lake NE, Ont 277 2.4 80 2.17 White and Blum, 1995Rawson Lake NW, Ont 277 2.4 80 1.91 White and Blum, 1995Hartviko 467 6.0 20 0.95 White and Blum, 1995Salacova Lhota 128 6.5 493 1.38 White and Blum, 1995Vocodlo 171 6.5 65 4.41 White and Blum, 1995Liuhapiro 480 50 1.43 White and Blum, 1995Yli-Knuutila 198 50 2.10 White and Blum, 1995Pont Donar 379 9.37 White and Blum, 1995Barholde 1395 2000 4.23 White and Blum, 1995Schluchsee 1974 5.0 100 6.53 White and Blum, 1995Kiryu 936 12.6 160 11.70 White and Blum, 1995Tsukuba 720 13.1 180 8.58 White and Blum, 1995Birkenes 1310 5.8 100 5.15 White and Blum, 1995Botnane 7.5 470 10.31 White and Blum, 1995Bteidvikdolen 3712 7.5 400 9.18 White and Blum, 1995Dyrdalen 3305 7.5 368 12.86 White and Blum, 1995Kaarvotn 1899 5.2 1175 2.08 White and Blum, 1995Longtjern 603 3.1 240 1.20 White and Blum, 1995Sogndol 1 875 1.52 White and Blum, 1995Sogndol2 870 1.47 White and Blum, 1995Storgoma 923 7.1 110 1.44 White and Blum, 1995Dante&a 115 0.3 1.94 White and Blum, 1995Lilla Tivjon 203 2.0 1.92 White and Blum, 1995Solmyren 348 0.3 2.85 White and Blum, 1995Vuoddosbacken 383 0.3 2.27 White and Blum, 1995Ciste Mhearod 5.0 4.81 White and Blum, 1995Dorgall 2464 6.0 491 13.62 White and Blum, 1995Glendye 817 6.4 200 7.48 White and Blum, 1995Green Burn 2135 6.0 491 12.79 White and Blum, 1995White Laggan 2185 6.0 491 13.37 White and Blum, 1995Bear Brook, ME 881 5.0 233 2.97 White and Blum, 1995Brair Creek. GA 990 13.1 623 1.57 White and Blum, 1995Cadwefll Creek, MA 793 7.0 147 2.35 White and Blum, 1995Como Creek, CO 247 659 0.49 White and Blum, 1995Coweeta 2, NC 854 11.7 295 2.45 White and Blum, 1995Coweeta 34. NC 955 10.6 915 2.97 White and Blum, 1995Emerald Lake, CA 1410 6.0 616 1.08 White and Blum, 1995Fort River. MA 507 8.4 334 2.78 White and Blum, 1995Hubbard Brook. NH 800 5.0 888 4.26 White and Blum, 1995Loch Vale. CO 604 0.0 900 1.10 White and Blum, 1995Log Creek, CA 363 7.2 330 2.76 White and Blum, 1995Martinell. CO 1507 0.0 140 2.67 White and Blum, 1995Mundberry Brook.MA 7.0 3.15 White and Blum, 1995Old Raa Montain.VA 395 9.0 530 1.17 White and Blum, 1995Ponolar GA Peters 338 15.3 55 1.42 White and Blum, 1995Panther Lake, NY 720 5.0 170 5.27 White and Blum, 1995Rabbit Ears, CO 617 0.7 125 2.08 White and Blum, 1995Sage Hen, CA 142 1.5 220 0.17 White and Blum, 1995South Cascade. WA 4090 695 1.67 White and Blum, 1995Tarps Creek, CA 187 7.2 188 0.65 White and Blum, 1995Tesque Aspin, NM 639 5.0 717 4.11 White and Blum, 1995Tesuque. Conifer. NM 829 2.5 640 4.22 White and Blum, 1995Woods Lake, NY 760 5.0 122 1.61 White and Blum, 1995Slave Provinceb 117 −4.0 1.21 Millot et al., 2002Greenville Provinceb 556 4.5 2.13 Millot et al., 2002Nsimi 380 24.0 200 1.15 Oliva et al., 2003Lysina 406 5.0 120 2.63 Oliva et al., 2003Estibe're 1125 5.0 700 3.91 Oliva et al., 2003Latte 1246 350 2.25 Oliva et al., 2003Margeride 705 11.0 600 4.04 Oliva et al., 2003Strengbach 952 9.0 263 6.16 Oliva et al., 2003Ilambalari 2019 27.0 22.61 Oliva et al., 2003Cherakkobbanmala 1013 27.0 10.92 Oliva et al., 2003Anamalai Hills 822 27.0 1643 14.49 Oliva et al., 2003Sivagiri 902 27.0 869 10.66 Oliva et al., 2003Tarangamakanam 2291 27.0 831 27.45 Oliva et al., 2003

(continued on next page)

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et al., 2001).There is a good positive correlation (r2= 0.61) between monthly

average SWR and temperature in the Jiuhua Mountains (Fig. 9). Itdemonstrates the dominant role of temperature in controlling thechemical weathering rates in the alpine regions where chemicalweathering is kinetic-limited.

5.4.3. Comparison with other granitic watersheds in different climatic zonesA database of global small granitic watersheds in different climatic

zones was compiled to further explore the relationship between che-mical weathering rates and climatic factors (temperature and runoff)(Table 6). Most of the global data comes from White and Blum (1995),Oliva et al. (2003), and West et al. (2005). The type of climatic zonevaries from frozen tundra to tropical, with temperature ranging from−4.6 °C to 29 °C, runoff ranging from 100mm to 7380mm, and ele-vation (can be approximated as a parameter of physical erosion in-tensity) ranging from 0m to 3000m. The following criteria were con-sidered during data compilation: (1) homogeneous lithology (mainlygranitoids and gneisses) to the greatest extent. Therefore, the con-tribution of carbonate minerals must be slight or can be corrected; (2)high-resolution hydrochemical data (long-term averages that are de-termined from time-series data, rather than a single spot sample,commonly reported on an annual basis), and high-quality runoff andtemperature data (from hydrological and meteorological departmentsof authority); and (3) similar methods of data processing and silicateweathering rate calculation. We can only get authoritative runoff datafor the Qingtong River, and the contribution of human activities,

carbonate and evaporite minerals in the Qingtong River is much lowerthan the Lingyang River. Therefore, according to the above-mentionedcriteria, only the data of the Qingtong River was used to compare withthe global small rivers. The compiled data show a large range of SWRfrom 0.17 to 31.5 t km−2 y−1.

According to White and Blum (1995), the effect of temperature onSWR can be described by an Arrhenius relationship:

= −SWR Ae( Ea/RT) (19)

where Ea is the activation energy (kJmol−1), T is temperature (K), R isthe gas constant, and A is a pre-exponential factor. Calculated Ea valueof the global granitic watersheds is 46.6 kJmol−1, which is similar tothe value of 48.7 kJ mol−1 from Oliva et al. (2003), but lower than thevalue of 62.5 kJmol−1 from White and Blum (1995).

In the global data set, 113 samples show positive correlations be-tween SWR and runoff (r2= 0.33), and between SWR and temperature(r2= 0.39) (Fig. 10). Scattered points below the trend line (high runoffand low SWR) are mostly from frigid zones or glacier-covered areas,while points above the line are mainly from tropical areas with hightemperatures and high runoff. Moreover, using the Excel data analysistool, we made a multiple linear regression of SWR (dependent variable)and temperature and runoff (independent variables). The adjusted r2

and p value are 0.61 and ≪0.05, respectively, which indicates that 61%of the SWR change is caused by the two parameters. The remaining39% may be attributed to other factors such as physical erosion andbiological effect. As a terrain parameter, the elevation difference be-tween the source area and the sampling location can be used to roughly

Table 6 (continued)

Watershed Names Runoff(mm)

Temp.(°C)

ΔElem

SWR(t km−2 y−1)

Data sources

Pulachimalai 1533 27.0 862 11.19 Oliva et al., 2003Pasukidamettu 1000 25.0 10.95 Oliva et al., 2003Storbergsba¨ckenc 247 −0.2 265 0.74 Oliva et al., 2003Andrews Creek.WA 480 7.0 1336 4.94 Oliva et al., 2003Caribou P. Creek LoP. Alc 167 −3.5 150 2.14 Oliva et al., 2003Caribou P. Creek HiP. Alc 152 −3.5 150 1.80 Oliva et al., 2003Falling Creek. GA 300 16.0 131 7.36 Oliva et al., 2003Halfmoon Creek. GA 400 3.0 1430 6.10 Oliva et al., 2003Holiday Creek. VA 350 14.0 136 2.31 Oliva et al., 2003μLoch Vale. CO 1127 9.0 0 2.95 Oliva et al., 2003Log Creek. CA 373 7.2 330 2.61 Oliva et al., 2003Merced river. CA 665 12.0 2773 2.94 Oliva et al., 2003Rabbit Ears. CO 1132 0.7 125 3.47 Oliva et al., 2003Tallulah river.GA 1820 12.0 571 5.60 Oliva et al., 2003Tarps Creek.CA 876 7.2 188 4.76 Oliva et al., 2003Idaho 390 4.5 475 2.46 West et al., 2005Africa 250 24.1 250 0.80 West et al., 2005Sabah Malaysia 1960 25.7 100 3.35 West et al., 2005Cote d'Ivoire 470 26.0 72 4.54 West et al., 2005East Southern Alps 1690 13.0 5.09 West et al., 2005Puerto Rico Long Term 3680 22.0 184 15.97 West et al., 2005West Southern Alps 7380 10.0 25.79 West et al., 2005Siberia 270 2.0 434 0.77 West et al., 2005British Columbia 3670 9.9 2944 2.49 West et al., 2005Lesser Himalaya 1290 14.5 938 9.69 West et al., 2005High Himalaya 2060 5.0 3000 5.78 West et al., 2005Colorado Rockies 580 5.8 658 0.65 West et al., 2005Svalbard 980 −4.6 806 0.99 West et al., 2005Swiss Alps 2840 0.5 1430 2.39 West et al., 2005Nethravati River 3300 29.0 1239 25.00 Gurumurthy et al., 2012Kumaradhara River 2594 28.0 1221 16.60 Gurumurthy et al., 2012Gurupur River 3425 28.0 1235 16.60 Gurumurthy et al., 2012Strengbach Catchment 814 13.0 131 4.76 Viville et al., 2012Xishui River 680 16.9 947 6.90 Wu et al., 2013Changhua River 810 23.0 894 24.30 Zhang et al., 2016Sorocaba River 776 19.7 255 4.60 Fernandes et al., 2016Anhui, Huangshan 989 16.3 760 9.20 UnpublishedFujian 1069 18.5 470 10.30 UnpublishedGuangdong 1001 21.9 497 15.10 UnpublishedHainan 1073 24.5 773 12.70 Unpublished

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represent physical erosion intensity. Alpine areas with large elevationdifferences would have high physical erosion rates, where the kinetic-limited regime is dominant. In comparison, more craton regions thatare subject to supply-limited usually have small elevation differencesand low erosion rates. When only the watersheds with elevation dif-ferences> 400m are considered, the adjusted R-squared value of linearregression of SWR and temperature shows a remarkable increase (from0.39 to 0.56) (Fig. 11). This indicates that the watersheds in the highphysical erosion areas exhibit stronger positive correlation betweenSWR and temperature. This increase in correlation does not occur insupply-limited craton watersheds. In summary, temperature is the mostcritical factor in controlling chemical weathering rate in the kinetic-limited areas with elevation difference exceeding 400m.

6. Conclusions

We conducted an annual sampling (monthly or half-monthly) in thesmall granitic watersheds of the Jiuhua Mountains. Through hydro-chemical analysis and forward-model calculation, we have gained thefollowing conclusions:

(1) Low pH rain water and average contribution of 16.9% of at-mospheric input to the dissolved load in stream indicate that the in-fluence of acid rain is significant even in the alpine area. In comparison,the influence of agricultural activities is weak with an average con-tribution of only 2.0% due to very low proportion of farmland in thewatersheds.

(2) The average contribution of silicate weathering, carbonateweathering and evaporite dissolution is 58.7%, 13.5% and 8.9%, re-spectively. Considering that the exposed area of carbonate rocks in thewatersheds is< 5%, the trace carbonate minerals do make an im-portant contribution to the dissolved load in the granitic watersheds,but the contribution is significantly lower than those of the Himalayasand Southern Alps of New Zealand.

(3) The small granitic watersheds in the Jiuhua Mountains with highphysical erosion rates show strong positive correlation between theSWR and temperature, but no correlation between the SWR and TSS. Acompilation of global small granitic watersheds further demonstratesthat temperature is the first-order controlling factor for silicateweathering rates in kinetic-limited alpine areas with an elevation dif-ference > 400m.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemgeo.2018.11.019.

Acknowledgments

This study was supported by the Natural Science Foundation ofChina (Grant No. 41373003). We are grateful to QingYang CountyMeteorological Bureau, which was keen to provide information re-garding temperature, precipitation and evaporation.

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