Typhoon Impacts on the Chemical Weathering Regime and Atmospheric Carbon Consumption of a High Standing Island Watershed, Taiwan

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Kevin J. Meyer

Graduate Program in Earth Sciences

The Ohio State University

2016

Master's Examination Committee:

Professor Anne E. Carey, Advisor

Professor W. Berry Lyons

Professor Matthew R. Saltzman

Kevin J. Meyer

2016

Abstract

The impacts of extreme weather on watershed dynamics and chemical weathering are poorly understood and rarely documented. This study addresses the impacts of

Typhoon Mindulle (2004) on the physical hydrology, chemical weathering sources, and

CO2 consumption of the Choshui River, a High Standing Island watershed in Taiwan.

Stormflow runoff was determined to be largely controlled by total precipitation and precipitation intensity. Watershed slope steepness is much less important in determining runoff during periods of extremely high precipitation. Weathering sources were determined to be silicate and secondary disseminated carbonate minerals at the surface and silicate contributions from deep thermal waters. Loss on ignition analysis of collected rock samples indicate disseminated carbonate may compose a greater fraction of the surface minerology than previously identified. Strontium isotope and major ion geochemistry indicate that high precipitation causes surface minerals to control the weathering profile. These data also suggest purging of silicate solute rich soil waters during storm events, creating a greater relative contribution of silicate weathering to the solute load during periods of increased precipitation and runoff. However, this leads to depletion of this solute reservoir and carbonate weathering becomes more important to the weathering regime as the storm continues. Major ion data indicate the possibility that mica weathering (muscovite, illite, biotite, chlorite) may represent an important silicate weathering pathway in the watershed, but this determination was beyond the scope of

ii data available. Deep thermal water represents an important contribution to river solutes during lower flow conditions.

Sulfuric acid creation through oxidation of pyrite was determined to be a major contributor to total weathering and represents ~40%–77% of weathering. The lowest contributions from sulfuric acid occur during peak flows. Carbonate weathering is dominant in the watershed (66%-86% of total weathering) and increases in contribution during the typhoon. The remaining chemical weathering comes from silicate materials.

Carbonate weathering represents the majority of CO2 flux from the atmosphere during storm flow but silicate weathering is the majority during lower flow conditions. The carbonate contribution to CO2 flux is 36%–69% (31%–64% for silicates). Silicate

-2 weathering CO2 consumption for the 72 hours of storm sampling is 0.84 ton km , with a

-2 daily average of 0.28 ton km . CO2 flux from the atmosphere including carbonate weathering is substantially higher (2.51 ton km-2 for 72 hours storm measurements and

-2 0.84 ton km daily storm average). Chemical weathering mediated CO2 export rates from the atmosphere increased to a maximum of over 140 times the pre-storm rate for silicate weathering and 250 times the pre-storm rate for total weathering including carbonate minerals. The 72 hour sampling period contained only a portion of the total storm-flow and did not include the majority of the peak flow which occurred following sampling.

Therefore, these consumption values are likely underestimated. Daily silicate consumption estimates during the stormflow may contribute 0.03%–0.1% of average daily global silicate CO2 consumption from an area comprising ~0.001% of total global landmass for the days of storm activity. Therefore, extreme storm impacts on High

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Standing Islands globally may provide a significant but not represented contribution to the global carbon budget.

Acknowledgments

I would like to thank Dr. Anne Carey not just for agreeing to take me on as a graduate student, but for providing insights and suggestions into chemical processes and her consistent positive reinforcement among a myriad of other reasons to be grateful.

Additionally, I wish to thank Anne for providing the suggestion to apply for an NSF grant program I had never heard of about two weeks before its deadline. This application and subsequent funding set the trajectory for the entirety of my graduate research. I’d also like to thank Dr. Berry Lyons and Dr. Matt Saltzman for agreeing to serve on my committee. Discussions with Dr. Lyons always ended with excitement over the possibilities in my data, and multiple geochemistry courses with him provided sound interpretation footing needed for the geochemical data. Several additional members of the

Carey-Lyons research family were also helpful in interpretation and instrumentation guidance. Talks with Sue Welch provided clarity in all aspects of wet geochemistry.

Laura Miller and Dan Ardrey were helpful in learning the preparation and analysis methods for XRF analysis, even if uncooperative machinery meant no bulk chemistry results have been made at this point.

An immense amount of gratitude to my Taiwanese colleagues is warranted. I particularly want to thank Dr. Chen-Feng You of National Cheng Kung University for agreeing to support my summer research in Taiwan within about a week of being v contacted by a stranger. This research would be nothing without his insights into the hydrological and strontium regimes in Taiwan, and the analytical support he provided.

Dr. Hou-Chun (Bear) Liu provided a wealth of knowledge of factors affecting the strontium isotope system in Taiwan. My fellow graduate students at NCKU were also instrumental in the success of this project. Particularly, I wish to thank Tzu-Hao (David)

Wang for MC-ICPMS training and emergency 2 AM support, Pei-Hsuan (Lucy) Tsai for strontium column chemistry training, and Yen-Hsin (May) Chen for doing the ICP-OES concentration analysis of strontium and calcium concentrations prior to my arrival, and for assistance in field sampling. Importantly, every student in Dr. You’s group went out of their way to make me feel welcome in Tainan, both in and outside the office. I would also like to thank Dr. Huang for driving Yen-Hsin and me all over central Taiwan for field sampling, and getting us river access in many remote locations.

Finally I would like to thank The National Science Foundation East Asia and the

Pacific Summer Institute program (NSF EAPSI) and the Taiwan Ministry of Science and

Technology for support, and Friends of Orton Hall for additional funding for shipping and presenting at the Geological Society of America annual meeting. This material is based upon work supported by the National Science Foundation under Grant No.

1515319. Additional data from prior work conducted under NSF Grants No. OISE-

0413475 and EAR-0309564 were also used. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

Vita

May 2007 ...... Smithson Valley High School

Dec. 2011 ...... B.S. Earth Sciences, Jackson School of Geosciences, The University of Texas at Austin

Oct. 2012 – Mar. 2013 ...... Geothermal Energy Researcher, The Bureau of Economic Geology, Austin, Texas

Mar. 2014 – Aug. 2014 ...... Environmental Scientist I, ACI Consulting, Austin Texas.

Aug. 2014 to present ...... University Graduate Fellow, School of Earth Sciences, The Ohio State University

Aug. 2015 to present ...... Graduate Teaching Associate, School of Earth Sciences, The Ohio State University

Publications

Feng, W., Hardt, B.F., Banner, J.L., Meyer, K.J., James, E.M., Musgrove, M., Edwards, R.L., and Cheng, H., 2014. Changing amounts and sources of moisture to the U.S. southwest since the Last Glacial Maximum in response to global climate change. Earth and Planetary Science Letters 401, 47-56.

Meyer, K., 2011. Southwest U.S. paleoclimate over the past 30 ky: Insights from speleothem δ18O and growth rate time series. Undergraduate Honors Thesis, Jackson School of Geosciences, Adviser: Jay Banner.

Fields of Study

Major Field: Earth Sciences Area of Emphasis: Geochemistry

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Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vii

List of Tables ...... xi

List of Figures ...... xii

Chapter 1: Introduction ...... 1

1.1. Background ...... 1

1.2 Goals and Objectives ...... 3

Chapter 2: Physical Hydrology of the Choshui River and Response to ...... 6

Typhoon Mindulle ...... 6

2.1 Introduction ...... 6

2.1.1 Runoff Ratio...... 7

2.1.2 Watershed Physical Properties ...... 7

2.1.3 Typhoon Mindulle ...... 8

2.2 Methods ...... 9

2.3 Results ...... 12

2.4 Discussion and Conclusions ...... 13

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2.4.1 Slope Effects on Runoff ...... 13

3.4.2 Precipitation Effects on Runoff ...... 14

2.4.3 Sources of Error and Their Effect ...... 15

Chapter 3: Source Mixing Response to Typhoon Mindulle ...... 24

3.1 Introduction ...... 24

3.1.1 Background ...... 24

3.1.2 Geological Setting ...... 25

3.1.3 Strontium Isotope Geochemistry ...... 26

3.2 Methodology ...... 27

3.2.1 Sample Collection ...... 27

3.2.2 Sample Preparation and Analysis ...... 27

3.4 Results ...... 31

3.4.1 Disseminated Carbonate ...... 31

3.4.2 Strontium Isotopes ...... 32

3.4.3 Major Ions and Molar Ratios ...... 34

3.5 Discussion ...... 38

3.5.1 Disseminated Carbonate ...... 38

3.5.2 Strontium Isotopes ...... 38

3.5.3 Major Ions and Molar Ratios ...... 43

3.6 Conclusions ...... 51

Chapter 4: Typhoon Induced Atmospheric Carbon Consumption ...... 64

4.1 Introduction ...... 64

4.2 Methodology ...... 66

4.3 Results ...... 67

4.3.1 Pyrite Oxidation Weathering Contribution ...... 67

4.3.2 Silicate Weathering Yields and CO2 Consumption ...... 70

4.4 Discussion ...... 72

4.4.1 Pyrite Oxidation Contribution to Weathering Fluxes ...... 72

4.4.2 Carbonate Weathering Influence on CO2 Export...... 82

4.4.3 Dissolved Silica Flux ...... 84

4.4.4 CO2 Consumption from Weathering Yields ...... 85

4.5 Conclusions ...... 89

Final Conclusions...... 100

References ...... 104

Appendix A: Sub-Watershed Slope Maps ...... 112

Appendix B: Discharge and Precipitation Measurements ...... 117

List of Tables

Table 2.1. Discharge gauge selection reasoning and criteria …………………………...17

Table 2.2. Choshui River sub-watershed delineated areas ...... 18

Table 2.3. Precipitation totals in the Choshui River Watershed ...... 18

Table 2.4. Summary physical and hydrological data for the five Choshui River

sub-watersheds ...... 19

Table 3.1. Bulk rock Loss-on- Ignition analysis of carbonate minerals ...... 53

Table 3.2. Strontium Isotope, Ion Molar Ratio, and Select Concentration Data ...... 54

Table 4.1. Contributions of pyrite oxidation, carbonate weathering, and

silicate weathering to total weathering yields ...... 91

Table 4.2. CO2 consumption estimates ...... 93

List of Figures

Figure 2.1. Map of the Choshui River watershed, sub-watersheds, and

associated gauges ...... 20

Figure 2.2. Slope map of the Choshui River Watershed ...... 21

Figure 2.3. Map of Theissen Polygon precipitation estimates ...... 22

Figure 2.4. Choshui River sub-watershed discharge time-Series ...... 23

Figure 3.1. Map of water and rock sampling locations...... 56

Figure 3.2. 87/86 Sr time-series during Typhoon Mindulle ...... 57

Figure 3.3 Strontium isotope source mixing diagram for the Typhoon Mindulle related sampling period ...... 58

Figure 3.4 Molar ratio mixing diagrams for Mg/Na vs Ca/Na and Mg/K vs Ca/K during Typhoon Mindulle sampling period ...... 59

Figure 3.5 Molar ratio mixing diagrams for Ca/Na vs Sr/Na and Ca/K vs Sr/K during Typhoon Mindulle sampling period ...... 60

Figure 3.6 Molar ratio mixing diagrams for HCO3/Na vs Ca/Na during Typhoon Mindulle sampling period ...... 61

Figure 3.7 Ca/Mg and Ca/Sr time-series during the Typhoon Mindulle sampling period ...... 62

Figure 3.8 K+ and F- concentration time-series during the Typhoon Mindulle sampling period ...... 63

Figure 4.1 Plots of Ca+Mg charge equivalence vs HCO3 and Ca+Mg charge equivalence vs HCO3 + SO4 charge equivelance ...... 94

Figure 4.2 Plot of total cation concentration vs SO4 concentration ...... 95

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Figure 4.3 Plots of total cation concentration vs HCO3 concentration and XSO4 adjusted cation concentration vs CO2 mediate HCO3 concentration ...... 96

Figure 4.4 Time-series of the percent contribution of sulfuric acid to total weathering, carbonates to total weathering, and carbonate

weathering to CO2 flux from the atmosphere ...... 97

Figure 4.5 Time-series of the dissolved silica flux in response to Typhoon Mindulle ...... 98

Figure 4.6 Time-series of the silicate and total CO2 consumption rates in response to Typhoon Mindulle ...... 99

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Chapter 1: Introduction

1.1. Background

High standing Islands (HSIs) represent a major source of both physically weathered particulate and chemically weathered solutes to the world’s oceans (Jacobson and Blum, 2003; Lyons et al., 2005; Carey et al., 2005; Hilton et al., 2012). Chemical weathering of silicate minerals is a primary control on the global carbon budget and carbon export from the atmosphere over geological time (Berner et al., 1983; Raymo and

Ruddiman 1992; Berner, 1999; Kump et al., 2000). Weathering of carbonate minerals is beginning to be understood as a possible important sink for atmospheric carbon over geological time-scales as well through exporting DIC to the ocean that can be consumed through primary production (Archer et al., 1998; Liu and Zhao, 2000; Lenton and Britton,

2006; Liu et al., 2011). HSIs are defined as islands whose stream headwaters are at an elevation of 1000 meters or more above sea level (Milliman and Syvitski, 1992). Because of the direct exposure of many HSIs to open ocean, they are commonly exposed to the full force of intense storm events that may dramatically affect the weathering regimes of the mountainous watersheds. Taiwan experiences an average of four typhoons each summer (Wu and Kuo, 1999).

Extreme weather induced mechanical weathering has been shown to move mass quantities of sediment and particulate organic matter to the oceans for both monsoon storms (Goodbred and Kuehl, 2000) and typhoon impact (Dadson et al., 2003; Milliman

1 and Kao, 2005; Goldsmith et al., 2008; Hilton et al., 2008). Some estimates indicate that up to 33% of total sediment transport to the oceans is derived from HSIs (Lyons et al.,

2002; Liu et al., 2008), and that the majority of HSI sediment and terrestrial particulate organic carbon is transported during extreme weather events (Goldsmith et al., 2008). An estimated 217 million tons of sediment and organic carbon were delivered to the ocean in

1996 from nine Taiwanese rivers through landslides and flooding induced by Typhoon

Herb (Milliman and Kao, 2005). It is thought that mechanical weathering rates may act as a primary control on chemical weathering rates on HSIs (Lyons et al., 2005), but only one known previous study has provided data for chemical weathering rates during a typhoon

(Goldsmith et al., 2008). Previous studies on silicate weathering of HSIs have shown that the weathering rates are some of the highest globally (Li, 1976; Carey et al., 2005; Lyons et at., 2005) and in Taiwan are largely controlled by relief, lithology, and seasonality

(Dadson et al., 2003; Goldsmith, 2009; Chao et al., 2015). No known studies have attempted to identify the changes in relative chemical weathering inputs from different lithologies or minerals within a watershed during a typhoon event. This may be because of the sampling difficulty and assumptions that chemical weathering during these events is a minimal component of the annual weathering yields.

The major controls on the rate of chemical weathering and the geochemical composition of stream waters globally are temperature, precipitation amount, flow path lithology, and “resetting events” such as rapid uplifting, erosion, and volcanism (Brady and Carroll, 1994; Gaillardet et al., 1999). These resetting events prevent the diminishing of chemical weathering rates over time by renewing denuded material and providing fresh mineral surfaces (Vitousek et al., 1997; Dixon et al., 2012). Taiwan has among the

2 highest uplift rates in the world (~5 ± 0.7 mm/yr; Peng et al., 1977). This allows for continuously produced solute from bedrock. This uplift keeps relief high allowing for rapid physical erosion, which in turn produces fresh mineral surfaces for chemical weathering processes. Annual precipitation totals are also extremely high in Taiwan, with an average of 2.5 m yearly (Dadson et al., 2003). Summer monsoon and typhoon precipitation commonly contribute nearly 90% of annual rainfall (Chung et al., 2009).

Because of these reasons, the lack of research on the impacts of extreme weather on weathering yields and sources represent a gap in our understanding of the global carbon cycle over geological timescales.

1.2 Goals and Objectives

The primary goal of this research is to determine the effects that extreme weather may have on both the physical and chemical hydrology of High Standing Island (HSI) rivers, specifically the Choshui River in West-Central Taiwan. This goal is accomplished through the pursuit of three primary objectives

1) Identify the dominant factors controlling runoff in the Choshui River watershed

during and following Typhoon Mindulle (2004).

2) Determine the river solute sources and changes in the relative weathering

contributions of these sources in response to Typhoon Mindulle.

3) Estimate typhoon induced CO2 flux from the atmosphere from silicate and

carbonate weathering.

The first objective is addressed by identifying the relative importance of multiple factors such as topography, localized rainfall intensity, and total rainfall on runoff ratio

(the ratio of runoff to precipitation) within five sub-watersheds of the Choshui River basin. Stream discharge, precipitation totals, and elevation data were analyzed for each sub-watershed. These were used to create a range of possible and likely runoff ratio estimates associated with storm flow, and to identify any relation this ratio has with precipitation totals, intensity, or slope gradient.

The second objective was addressed through the use of strontium isotope and major ion data. These data were used to create source mixing diagrams using 87Sr/86Sr and commonly used major ion ratios. Time-series were created for strontium isotope data, major ion ratios, and select total ion concentrations to determine how weathering source contributions change through time in relation to precipitation and discharge associated with Typhoon Mindulle.

The final objective was accomplished primarily by using dissolved silica, sulfate, and bicarbonate data. These data were used to account for sulfuric acid induced weathering through the oxidation of pyrite, provide a narrow range of possible silicate weathering contributions to total CO2 consumption, and to estimate of total CO2 flux from the atmosphere to the ocean through weathering of both silicates and carbonates.

Sulfuric acid induced weathering is commonly overlooked and does not consume CO2, but may be an important weathering pathway in many watersheds. These data were also used to create a quantitative estimate of the relative contributions of silicate and carbonate weathering, and how these change through time in response to Typhoon

Mindulle. Major ion data were used to give an indication of the reliability of these estimates.

This study provides novel insight into the role typhoons have in changing the weathering dynamics of watersheds they affect, such as changes in the relative contributions of weathering sources and their total inputs into the river system. It also constrains previously published estimates of the role that typhoons play in atmospheric carbon sequestration. In sum, this study applies many geochemical techniques in conjunction with an understanding of physical hydrology to elucidate the dynamics of typhoon induced chemical weathering in high relief terrains and their importance to the global carbon system.

Chapter 2: Physical Hydrology of the Choshui River and Response to

Typhoon Mindulle

2.1 Introduction

This study uses a sub-basin analysis of rainfall, discharge, and topography to analyze the impacts of Typhoon Mindulle on the hydrological regime of various watersheds within the Choshui River Basin. Precipitation, discharge, and watershed area data were used to create multiple runoff ratio estimates for the Choshui River watershed and for four distinct sub-watersheds of different relief within the larger basin.

Determination of the physical hydrological inputs and water fluxes of a watershed are important for understanding the origins and transport of any physical or chemical material being carried within that water flux. The flux associated with any storm and watershed is dependent on a variety of factors. These include total precipitation, precipitation intensity, rainfall distribution, watershed area, and surface runoff ratio.

Since total rainfall and its distribution and intensity can be estimated from gauge data, and watershed area can be easily delineated using mapping software, estimates of runoff ratio can be calculated to understand the water flux through a watershed. This ratio is generally calculated over long time intervals to control for changes in storage (typically

≥1 year), so this study’s approach to runoff ratio is novel for its limited time period.

2.1.1 Runoff Ratio

Runoff ratio is the ratio between average runoff of a watershed to the average precipitation of the watershed, where runoff is determined by the discharge volume at a point outlet normalized to the watershed area above that point. Therefore, three quantities are needed to determine runoff ratio (R): Precipitation (P), discharge (Q), and watershed area (A) (Eq. 2.1), where Q is in units of [length3 x time-1], A is in [length2], and P is in

[length x time-1]. The time unit of runoff and precipitation is typically not included since these values are usually reported and understood as yearly quantities. The annual timescale is used so change in storage can be assumed to be zero. However, this study uses a much smaller time interval of 11 days to capture only precipitation and discharge resulting from a single strong storm event. Base-flow is considered to be negligible compared to the storm flow. Since this shorter time interval is still shared between both P and Q, it is treated as a hidden variable similar to the “yearly” hidden time variable that is commonly reported. All runoff and precipitation values will be reported in units of (mm) as is typical in runoff ratio calculations

푄⁄ Eq. 2.1 푅 = 퐴 푃

2.1.2 Watershed Physical Properties

The Choshui River is the longest river in Taiwan at 187 km, and has the second largest drainage area at 3157 km2 (ROC, 2015). It is located in the west-central portion of the island. Total relief of the watershed is 3,952 m and runs from sea-level to the peak of

Yushan Mountain, the highest elevation on the island. Slope gradients are typically high, especially in the eastern half of the watershed in the Central Mountain Range (CMR).

While the westernmost portion of the watershed in the coastal plains is highly urbanized, it represents a small fraction of the watershed area and is excluded in this study. The vast majority of the watershed is largely natural.

2.1.3 Typhoon Mindulle

Typhoon Mindulle caused intense rainfall over much of Taiwan, both directly from the storm and indirectly induced by the typhoon. The storm center made landfall over Taiwan at UTC 1500 (2300 local time) on July 1, 2004 (Lee et al., 2008). The storm center left the island late in the morning local time. Nearly all direct typhoon precipitation over the island occurred during this time window, primarily east of the

CMR. Total precipitation east of the CMR was relatively low for a typhoon impact with highs of ~200–400 mm (Chien et al., 2008).

Extremely severe storm convection occurred over much of western Taiwan directly following the departure of Typhoon Mindulle (Wang and Liao, 2006). As the typhoon made landfall, orogenic effects induced a secondary low over the Strait of

Taiwan (Lee et al., 2008). As Mindulle left the island, southwesterly winds brought moisture from the South China Sea creating extreme convection and precipitation over central and southern Taiwan west of the CMR. Associated precipitation totals were in excess of 700 mm in many areas (Chien et al., 2008). For this study, precipitation both directly and indirectly associated with Typhoon Mindulle is included in calculations of runoff ratio.

2.2 Methods

The Choshui River Watershed was separated into five sub-watersheds that were defined as all land area upstream from their discharge gauge stations. These recorded hourly data during Typhoon Mindulle in July 2004. Figure 2.1 shows a map of the full

Choshui River basin, all delineated watersheds, and all precipitation and discharge gauges within the Choshui River Basin with data available during July 2004. All gauge data were recorded, and are plotted, in local time (China Standard Time, UTC +8). The largest sub-watershed is referred to as the “Primary” watershed, and is delineated from the furthest downstream discharge gauge that is not adjacent to the ocean. This gauge was selected to avoid any tidal influences on measured discharge. The primary watershed contains more than 94% of the total Choshui River watershed area including the four other sub-watersheds in their entirety.

The remaining four sub-watersheds were chosen based on the rainfall and hydrograph gauge distribution and data availability, relief, and size. Rationale for the inclusion or exclusion of the 10 available hydrographs for analysis is given in Table 2.1.

The four sub-watersheds are referred to by their relative reliefs of low, medium, high, and extreme. However, it should be noted that even in the “low” relief area, slope gradients are generally high (>20 degrees average), so the watershed descriptors are only with respect to each other. Average slope gradients for each watershed were calculated using

Shuttle Radar Tomography Mission (SRTM) elevation data collected by the National

Aeronautics and Space Administration and released by the United States Geological

Survey. Digital Elevation Models (DEMs) with 3 arc-second resolution were used for slope angle calculations. A map of the degree slope of the Choshui River watershed is

9 shown in Figure 2.2. Additional slope maps zoomed to the individual sub-watersheds are available in Appendix A.

Runoff for each basin was calculated using watershed area and the total discharge summed over an 11 day period. Watershed areas were taken from the Taiwan Water

Resources Agency listing for each hydrograph with the exception of the “Extreme

Topography” watershed, which did not have a listed area. Its area is based on a hand delineated boundary instead. All other watershed areas were also hand delineated to ensure data quality by comparing to the listed areas (Table 2.2). The percent difference between given areas and hand delineated areas are all less than 10%. All but one is ~2% or lower. Hourly discharge data (m3s-1) was multiplied by 3600 seconds to get total hourly discharge (m3). These data were summed over the 11 day observation period to get the total discharge for each watershed. This 11 day period was chosen because all hydrographs displayed base flow at the beginning of the period, and most had returned to near base flow conditions by the end of the period. This period was also chosen to exclude rainfall not associated with Typhoon Mindulle that began occurring after the 11 days. The entirety of rainfall associated with the typhoon occurred during this period, typically within the first five days. Hourly rainfall and discharge measurements are given in Appendix B.

Runoff ratios were calculated following Eq. 2.1 using five separate estimates of precipitation. Four estimates were created using the summed hourly precipitation over the study period for gauges within or directly adjacent to the watershed. One estimate was calculated using the Thiessen Polygon method for average rainfall (Thiessen, 1911). The rainfall sums for individual gauges are listed in Table 2.3. Gauges are listed in order of

10 increasing associated watershed size. All gauges were used for the largest “Primary” watershed. Minimum and maximum precipitation values were taken from the minimum and maximum hourly summed values for the gauges assigned to each watershed. Three

“likely” values were also calculated, with the first being a mean for each watershed’s assigned gauges. The second “likely” value is an areally weighted mean where each gauge or group of gauges is considered to impact only an area between it and the next gauge(s). This area was estimated by drawing polygons in Google Earth, and assigning a percentage of the watershed based on the polygon and watershed areas. Each area’s contribution summed to total to 100% coverage.

For example: The “Extreme” watershed has three gauges along its southwest border. These were averaged to get the “typical” rainfall coming into the watershed across this boundary. The area between these and 1510P116 in the center of the watershed is ~50% of the watershed so the combined influence of the three boundary gauges would be 25% of the total. 1510P116 would contribute 50% of the total because it is also contributing to the other half of the watershed along with 1510P105 at the northern edge which in turn gives 25%.

The third method uses Thiessen Polygons (Thiessen, 1911). The Thiessen

Polygon method assigns a polygon to each precipitation gauge, where the entire polygon area is assumed to have the rainfall total of that gauge. Polygon edges are created using mid-points between adjacent gauges, where the boundary is drawn as a line perpendicular to the line connecting the gauges at its midpoint. These boundary lines are continued until they intersect the next nearest boundary. Each polygon’s percent coverage of a watershed is the weight given to the precipitation associated with that polygon. A Thiessen Polygon

11 map overlying all analyzed watersheds is shown in Figure 2.3. Associated rainfall amounts are displayed for all polygons.

2.3 Results

The Primary Choshui River watershed has an area of 2975 km2 and a total storm discharge of 1.11x109 m3. Runoff is 373 mm. Precipitation ranges from 483 to 1033 mm using 11 gauges. The arithmetic mean rainfall is 758 mm, and an area weighted average

(AWA) is 717 mm. Rainfall is estimated at 721 mm using the Thiessen Polygon estimation. Runoff Ratio may range from 0.36 to 0.77, with a mean value of 0.50, AWA of 0.52, and Thiessen Polygon estimation of 0.52.

The “Extreme” topography watershed (1510H075) has an area of 1583 km2 and a total storm discharge of 6.90x108 m3. Runoff is 436 mm. Precipitation ranges from 483 to

883 mm using 5 gauges, with an average of 747 mm and an AWA of 660 mm. Rainfall is estimated at 648 mm using the Thiessen Polygon estimation. Runoff Ratio may range from 0.494 to 0.903, with a mean of 0.584, AWA of 0.661, and Thiessen Polygon estimate of 0.673.

The “High” topography watershed (1510H049) has an area of 367 km2 and a total storm discharge of 1.77x108 m3. Runoff is 482 mm. Precipitation ranges from 714 to 851 mm using 3 gauges, with an average of 790 mm and an AWA of 794 mm. Thiessen

Polygon rainfall is estimated at 798 mm. Runoff Ratio may range from 0.567 to 0.675, with an average of 0.610, AWA of 0.608, and Thiessen Polygon estimate of 0.604.

The “Medium” topography watershed (1510H024) has an area of 259 km2 and a total discharge of 2.25x108 m3. Runoff is 868 mm. Precipitation ranges from 733 to 1033

12 mm using 2 gauges, with an average of 883 mm, AWA of 895 mm, and Thiessen

Polygon estimate of 993 mm. Runoff Ratio may range from 0.840 to approaching 1, with an average of 0.983, AWA of 0.969 and Thiessen Polygon estimate of 0.874. This is the highest range and average values of all watersheds.

The “Low” topography watershed (1510H050) has an area of 87 km2 and a total discharge of 3.93x107 m3. Runoff is 454 mm. Precipitation ranges from 669 to 910 mm using 3 gauges, with an average of 757 mm, AWA of 750 mm and Thiessen Polygon estimate of 701 mm. Runoff Ratio may range from 0.499 to 0.679, with an average of

0.600, AWA of 0.606, and Thiessen Polygon estimate of 0.67.

These values are summarized in Table 2.4. Discharge for each watershed is plotted in Figure 2.4. The eastern most “Extreme” watershed has the earliest response reflecting its proximity to the initial typhoon landfall. However, the more western discharge gauges have earlier responses in their peak flows beginning on July 2. This reflects the convection associated with moisture coming from the South China Sea in the southwest reaching the western portion of the Choshui River first.

2.4 Discussion and Conclusions

2.4.1 Slope Effects on Runoff

Slope did not seem to have a strong impact on the runoff ratio. This may be because the slope in all watersheds is comparatively high in relation to most global locations. The generally high slope combined with the sheer volume of water released may result in high runoff, with the slope variation between watersheds not substantially affecting total runoff. The most ideal watershed to assess extreme slope impacts on storm

13 runoff is the “High” watershed. Its possible precipitation range is narrow compared to the other sub-watersheds (137 mm), it has the highest mean slope, comparatively high gauge density, and consistent runoff ratio values for all three average calculation methods

(0.60–0.61). However, of the four non-primary watersheds, it has the lowest runoff ratio, suggesting minimal control of slope steepness over runoff.

3.4.2 Precipitation Effects on Runoff

Precipitation totals for Typhoon Mindulle related rainfall were extremely high at all gauges, with localized rainfall in excess of 1 m. Even the precipitation gauge with the lowest rainfall had nearly 0.5 m of rain. Nearly all gauges recorded precipitation in excess of 700 mm. The localized intensity of this rainfall appears to be the primary driver of variability in the runoff ratio averages among sub-watersheds. The three smallest

(more western) watersheds had the highest precipitation values of the four non-primary watersheds. This matches with the idea of a strong southwesterly flow induced by

Typhoon Mindulle creating the vast majority of rainfall in the watershed suggested by

Chien et al. (2008) and Lee et al. (2008). However, they suggested extreme high local precipitation is primarily driven by the orographic effect. Data for this study indicate that the highest rainfall amounts are associated with one of the least mountainous watersheds.

The “Medium” relief watershed has rainfall estimates that are ~100–200 mm higher than any other watershed depending on which averaging metric is used.

The three small watersheds (Low, Medium, and High) had higher precipitation averages using every metric, and also the three highest average Runoff Ratios. This does not hold true for the AWA and Theissen Polygon methods where the Extreme topography watershed ranks second. However this may be caused by the poor spatial resolution of the

14 precipitation gauges in this watershed. Very large areas in this watershed had to be assigned a weight based on single gauges, and in higher gauge concentration areas of other watersheds it is obvious that precipitation amounts change considerably over small distances. The “Medium” topography watershed received an exceptional amount of precipitation by every measure, and is even underweighted when looking at the minimum rainfall since this causes the ratio to exceed 1. This would suggest more water is leaving the system than precipitation entering, which should not be possible. Even using maximum precipitation values for this watershed yields an extremely high runoff ratio, and suggests that in localized areas, runoff can approach nearly 100% of rainfall. This watershed demonstrates the importance of rainfall amount and intensity in controlling runoff during extreme weather. However, most calculated runoff ratio values do not approach 1, and since evapotranspiration should be minimal due to the short time interval, storm cloud cover, and high humidity, this water is missing.

2.4.3 Sources of Error and Their Effect

The three averaging metrics suggest that ~2%–50% of rainfall is unaccounted for depending on the watershed analyzed. For nearly all watersheds (excluding the high precipitation “Medium” relief watershed), this range narrows to 39%–48%. There are several possible reasons for this depending on which watershed is being scrutinized.

Some tributaries lie along major faults and may be losing some water to the subsurface.

The Extreme topography watershed has a small reservoir with unknown storage change during the storm. Accurate precipitation measurements during high wind and strong rain are difficult due to eddy effects at gauge openings and raindrop splatter. Stream gauge rating curves were unavailable to assess the accuracy of the hydrographs during these

15 extremely high flows. Finally, while the hydrographs had returned to near stable conditions, they were not all reflecting pre-storm flow. Some amount of storm water had simply not moved through the system yet. This is small for the smaller watersheds, but may be a substantial amount of water for the larger ones (Figure 2.4). This may be why the “Primary” watershed unexpectedly had much lower runoff ratio values than any of the watersheds within it. It may be that even in small watersheds, short term storage is still an important part of the hydrological balance. In general, the three smallest watersheds have the densest gauge coverage, and therefore, the highest data quality. They indicate that total precipitation amount is the driving factor in increasing runoff. Nearly all water may runoff into the river once a certain threshold rainfall rate or amount is passed. This is likely due to one of two causes. 1) Rainfall amounts create full soil saturation quickly, or 2) Extreme rainfall overwhelms the infiltration rate of the soils.

This allows the remaining rainfall to rapidly travel as overland flow.

Table 2.1. All available discharge gauges with the rationale for including or excluding each in the study. Gauge Name (Type) Used? Reasoning

1510H076 (Main Branch) NO It is too close to the ocean outlet. It may be tidally affected.

It is ~20 km upstream of ocean outlet, but still contains 1510H071 (Main Branch) YES >94% of the total watershed.

It captures almost same extent as 1510H071. So it’s 1510H057 (Main Branch) NO redundant. Also it is very close to a large tributary outlet, and may not report accurate data.

It lies at a tributary outlet and is barely downstream of 1510H063 (Main Branch) NO 1510H075

It drains the entirety of the “Extreme” relief eastern portion of the watershed. The two main branches in this area are 1510H075 (Main Branch) YES in the Central Range of Taiwan, and no additional useable discharge gauges are upstream.

Location drains very small low lying watershed with 1510H050 (Tributary) YES relatively low relief for comparison.

Location drains upper reaches of the most western, 1510H024 (Tributary) YES relatively moderate relief watershed. Precipitation gauges offer moderate coverage for rainfall estimates.

No precipitation gauges exist within watershed, and it has 1510H064 (Tributary) NO several reservoirs that may skew storm response.

Location drains a watershed which reaches “High” relief terrain between that of the “Medium” and “Extreme” 1510H049 (Tributary) YES watersheds. It also has good precipitation gauge coverage within.

Hydrograph data for 2004 only contains daily values, not 1510H077 NO hourly. Determination can’t be made if these are mean (Main Branch/Tributary) values or singular measured values. —Gauges are listed with increasing distance from the ocean along the main branch, followed by tributary outlet distance from the ocean.

Table 2.2. Listed and hand delineated watershed areas along with the percent difference between them. Watershed Name 1510H071 1510H075 1510H049 1510H024 1510H050

Topography (Primary) Extreme High Medium Low

Listed Area (km^2) 2974.7 Not Given 367.4 259.2 86.5

Hand Delineated Area (km^2) 3024 1582.5 375 261 92.8

Percent Difference (%) 1.64 N/A 2.05 0.69 7.03

Table 2.3. Precipitation totals over the 11 day period for each precipitation gauge. Total Gauge Assigned Precipitation Theissen Watershed Watershed (mm) 1510P046 669 Low Low 1510P075 910 Low Extreme 1510P079 735 Low, Medium Low, Medium 1510P104 1033 Low, Medium Medium 1510P125 714 High Low, Medium, High 1510P087 851 High, Extreme High, Extreme 1510P030 805 High, Extreme Medium, High, Extreme 1510P088 714 Extreme High, Extreme 1510P116 483 Extreme Extreme 1510P105 883 Extreme Extreme 1510P080 545 Primary Only Primary Only —Gauges are listed by watershed(s) to which they are assigned for mean and “Area Weighted Mean” calculations in order from “Low” to “Extreme” relief. All gauges are used in the “Primary” watershed calculations.

Table 2.4. Summary of all calculated physical and hydrological data for the five Choshui River sub-watersheds. Watershed Primary Extreme High Medium Low Area (km^2) 2974.7 1582.5 367.4 259.2 86.5 Total Discharge (m^3) 1,109,815,200 690,092,712 177,170,400 224,874,000 39,310,200 Runoff (mm) 373 436 482 868 454

Min 483 483 714 733 669

Precipitation Max 1033 883 851 1033 910 (mm) "Average" 758 747 790 883 757 Area Weighted Avg. 717 660 794 895 750 Thiessen Polygons 721 648 798 993 701 Max 0.772 0.903 0.675 1.184 0.679 Min 0.361 0.494 0.567 0.840 0.499 Runoff Ratio "Average" 0.492 0.584 0.610 0.983 0.600 Area Weighted Avg. 0.520 0.661 0.608 0.969 0.606 Thiessen Polygons 0.517 0.673 0.604 0.874 0.648 Precipitation Gauges Used 11 5 3 2 4

Figure 2.1. Map of the full Choshui River Watershed including all five analyzed sub-watersheds and all precipitation and discharge gauges with data available from July 2004.

Figure 2.2. Map of the degree slope within the Choshui River Watershed.

Figure 2.3. Thiessen Polygon precipitation map of the Choshui River Watershed.

Figure 2.4. Eleven day storm hydrographs for the five Choshui River sub-watersheds.

Chapter 3: Source Mixing Response to Typhoon Mindulle

3.1 Introduction

3.1.1 Background

This study uses geochemical techniques to identify the sources of weathering solutes to the Choshui River, and to determine how these sources’ fractional contributions change in response to typhoon intensity. Strontium isotope and major ion chemistry analysis were used to accomplish this goal.

The dissolved chemistry of rivers is commonly used to determine the weathering sources and source compositions (Gaillardet et al., 1999; Kump et al., 2000; Dalai et al.,

2002; Chung et al., 2009). Solute chemistry has been used repeatedly to provide estimates of atmospheric carbon consumption through weathering of watershed rock (Gaillardet et al.,1999; Carey et al., 2005; Lyons et al., 2005; Goldsmith et al., 2008). Understanding the lithology and minerology of weathering materials is important for estimating atmospheric carbon consumption. This is because silicate mineral weathering is commonly understood to be a primary control on CO2 sequestration over geological time

(Berner, 1983), while carbonate weathering is historically thought of as having a net zero impact because of rapid recrystallization of carbonate minerals within the ocean environment over geologic time (Gaillardet, 1999; Hartmann et al., 2009). In addition,

24 certain silicate minerals chemically weather more easily than others. CO2 consumption per reaction is dependent on the exact mineral that is dissociating in the reaction. These links of weathering source to climate regulation create a need to understand the contribution of sources to total weathering yields. A multitude of geochemical applications have been used to address this, including major ion analysis (Gaillardet et al., 1999; Kump et al., 2000; Carey et al., 2005; Hilley and Porder, 2008) and strontium isotope analysis (Aberg, 1995; Banner, 1995; Capo et al., 1998; Banner, 2004; Chao et al., 2015)

3.1.2 Geological Setting

The watershed analyzed in this chapter is the upper half of the Choshui River watershed. This is equivalent to the “Extreme” relief watershed from Chapter 2 (Figure

2.3; Table 2.4). This area is not referred to as the “Extreme” sub-watershed from this point forward. The lithology of the watershed is dominated by Eocene to Miocene aged sub-metamorphic (showing some signs of metamorphism) to low-grade metamorphic rocks (Ho, 1975; Cammani et al., 2014). The rocks are predominately weakly metamorphosed sediments consisting primarily of slates and quartzites/meta-sandstones

(Hovius et al., 2000). The regional metamorphism is responsible for the creation of a small amount of secondary disseminated carbonate minerals within the bulk silicate material through hydrothermal alteration. This type of alteration is common in active collisional environments (Chamberlain et al., 2005). No pure carbonate lithologies have been identified in the watershed. Urbanization is limited to a few small willages in the

Choshui River watershed east of the western foothills region. The sampling location for this study drains the Central Mountain Range and Hsueshan Range regions of the

25 watershed that make up the eastern half of the Choshui River Basin (Figures 2.3; 3.1).

The area is nearly entirely natural, with a few small towns near the main river channel.

Hot-springs are present in the far reaches of the upper watershed near Lushan (Chen,

1985; Jang et al., 2012), and are a major constituent of the headwaters. The soils of the study area are composed of entisols and inceptisols (Chen et al., 2015). Entisols are largely defined as having poor development and minimal mineral alteration from underlying bedrock, while inceptisols typically begin developing a weathering depleted horizon (USDA, 1999). In the western foothills downstream of the study area, the geology is primarily upper Tertiary clastic sedimentary rocks (Ho, 1975; Camanni et al.,

2014; Goldsmith, 2009).

3.1.3 Strontium Isotope Geochemistry

Strontium (Sr) isotope ratios have been proven as highly effective tracers of water source inputs in a variety of environments (Banner et al., 1989; Chung et al., 2009;

Christian et al., 2011). The main radiogenic Sr isotope (87Sr) undergoes nearly no fractionation from low temperature or biogenic reactions compared to stable 86Sr. The ratio of these two isotopes in water samples is thus entirely dependent on source material composition and mixing in surface environments (Capo et al., 1998; Chung et al., 2009).

This makes strontium isotopic ratios ideal tracers of weathering inputs from silicate minerals and from disseminated carbonate minerals in Taiwan since their strontium isotopic compositions are substantially different (Chao et al., 2015). Silicate mineral ratios of 87Sr/86Sr are typically driven by rock age and the ratio of rubidium to strontium at formation. This is because 87Sr is the decay product of 87Rb, which has a half-life of 48 billion years, while 86Sr is a stable isotope. This changes strontium isotope compositions

26 over long geological time-scales, but the long half-life (~3.5 times the age of the universe) creates near zero change in the ratio over shorter time-scales.

3.2 Methodology

3.2.1 Sample Collection

Water samples for this study were collected during the summer of 2004, before, during, and following Typhoon Mindulle. Two samples were collected prior to Typhoon

Mindulle on 28 June, 2004 and 30 June, 2004. Samples referred to as the “storm samples” were collected in three hour intervals following the start of Typhoon Mindulle related rainfall from 1 July, 2004 to 4 July, 2004. A total of 25 storm samples were collected. An additional 20 samples were collected intermittently from 5 July, 2004 to 16

August, 2004. All water samples were collected at Renlun Bridge, and storm hydrograph data for the sampling period was collected ~4 km upstream at Bao-Shih Bridge. All samples were filtered using 0.45 µm pore-size cellulose nitrate filters to remove particulate matter into acid washed 250 mL LDPE bottles for storage. Rock samples were collected on 19 August, 2015 at eight locations upstream of the Typhoon Mindulle sampling location. All samples were either bedrock or stream cobbles. Rock samples were brought to The Ohio State University for analysis. Sampling and gauge locations for this study and a related study (Goldsmith et al., 2008), can be found in Figure 3.1.

3.2.2 Sample Preparation and Analysis

Strontium Isotope Geochemistry

Aliquots of the water samples were analyzed by Inductively Coupled Plasma

Optical Emission Spectrometry (ICP-OES) to provide high precision strontium

27 concentration data. This analysis was completed by Yen-Hsin Chin at National Cheng

Kung University, Taiwan in early summer 2015. Aliquots of the samples were then taken into a Class 1000 clean lab, and using the concentration data, were pipetted into HNO3 cleaned plastic beakers to contain 150 ng Sr. These samples were then evaporated to precipitate all dissolved salts. Following evaporation, 0.5 mL of 1 N HNO3 was added, and the precipitate was allowed to redissolve fully.

After re-dissolution, Eichrom Sr-Spec Resin loaded columns were conditioned for ion-exchange chemistry. This was accomplished by allowing Milli-Q water used for column preservation to drain fully, and then were added sequentially 3 mL of 3N HNO3, followed by 3 mL of Milli-Q water, 5 mL of 6N HCL, and 0.8 mL of HNO3. The columns were allowed to drain fully after each step. The 0.5 mL samples were then loaded into the columns. The samples were eluted with 8 mL of 3N HNO3 to remove unwanted ions which may interfere with analysis, primarily rubidium. The refined Sr samples were then collected from the column by adding 4 mL Milli-Q water, and evaporated to obtain a high purity strontium fraction. Finally, the ~150 ng Sr fractions were dissolved in 1 mL of 0.3N HNO3 containing 300 ppb Zr and transferred to sampling vials for MC-ICP-MS analysis.

Samples were analyzed using a Neptune MC-ICP-MS on three separate days.

Prior to analysis each day, the instrument was tuned for highest stability scanning of the

88Sr, 87Sr, and 90Zr isotopes to adapt to changes in ambient air conditions (temperature, pressure, and humidity). To ensure data quality, two separate standard runs were employed before sample analysis. The first analyzed a blank, followed by seven 150 ppb

NIST SRM 987 Sr standards, and finally another blank. The seven NIST SRM 987

28 measurements were used to calculate five adjusted standard values for the middle five measurements by averaging the measurements before and following each, subtracting these values from the standard value of 0.710245, then adjusting the five middle measurements by the differences. Mean and 2 sigma values were calculated for these five measurements for each day of analysis. The difference from the mean from 0.710245 for each day was 0.000001, 0.000000, and 0.000005, while the 2 Sigma uncertainty was

0.000012, 0.000009, and 0.000031, indicating high precision measurements. The second set of standards was then run.

This set consisted of alternating four NIST SRM 987 Sr standards with IAPSO seawater standards that were given the same column chemistry treatment as the river samples. The NIST SRM 987 values were used to correct the IAPSO seawater values in the same manner as the first standard run, which were then compared to the standard value of 0.709168. Mean and 2 sigma values for the adjusted IAPSO measurements were calculated each of the three days that samples were run. Mean differences from 0.709168 for each day were 0.000013, 0.000012, and 0.000000, while the 2 sigma uncertainties were 0.000038, 0.000031, and 0.000015.

Following standard runs, the samples would then be run. In the sample run, the

NIST SRM 987 Sr standard was analyzed before and after each sample, which were corrected in the same manner as the standard runs. Additional IAPSO standards were run as samples at a minimum of every five river samples and compared to the accepted value to ensure data quality. Of these, only two IAPSO samples had calculated 87Sr/86Sr ratios greater than 0.00002 different from 0.709168 standard value (0.000023 and 0.000035) while the remaining 10 had ratios less than 0.00002 different from the accepted value of

0.709168. The 2 sigma uncertainty for the 12 IAPSO samples measured over the three days was 0.000027.

Major Ion Geochemistry

All major cation (Na+, K+, Ca2+, and Mg2+) and dissolved silica concentrations

- 2- - were determined through ICP-OES analysis. Major anion species (Cl , SO4 , NO3 ,

NO2-, and F-) were determined using an Alltech ion chromatograph. These analyses were done shortly after the sampling period in 2004, and provided to me after my arrival in

Taiwan in 2015. All dissolved species were adjusted to account for rainfall contribution.

This was accomplished by assuming all chloride in the system comes from rainfall and that rainfall ion ratios are identical to seawater (Lyons et al., 2005). Seawater ion ratios were calculated from concentration data provided by Bruland (1983). Total HCO3 concentrations were calculated following Lyons et al. (1992). This method assumes all

- charge comes from the measured ions and bicarbonate. HCO3 concentration is then calculated as the difference between total positive and negative equivalency. The rainfall

- adjusted HCO3 follows this same calculation, but uses the rainfall adjusted ionic concentrations with a Cl- concentration of zero.

Disseminated Carbonate Weight Percent

To determine the percentage of carbonate by weight for collected rock samples,

Loss on Ignition (LOI) analysis was conducted. ~5g–10g portions of collected rock samples were pulverized in a SPEX CertiPrep 8515 Shatterbox swing mill for three minutes until a homogenous ultrafine powder. Approximately 3.5g of powder was partitioned into a pre-weighed, clean, 3.5cm ceramic crucible for each sample. The

30 samples were then dried for 24–48 hours at 250°C, allowed to cool, then weighed. Dry sample weight (mdry) is taken as this weight minus the crucible weight. Samples were then heated to 550°C in a muffle furnace for 4 hours to combust all organic matter, allowed to cool, then weighed (m550). Samples were the heated to 950°C for 2 hours to combust carbonate minerals, allowed to cool, then weighed (m950). All mass loss during this period is assumed to be from CO2 loss from CaCO3, leaving residual CaO behind.

Thus, percent carbonate by weight of a sample is calculated as follows, where 2.274 is equal to the ratio of the molecular weight of CaCO3 (100.086 g/mol) to the molecular weight of CO2 (44.009 g/mol). This follows Goldsmith et al. (in prep).

2.247 × (푚550 − 푚950) % 푤푒𝑖𝑔ℎ푡퐶푎푟푏표푛푎푡푒 = 푚550

3.4 Results

3.4.1 Disseminated Carbonate

Loss on Ignition analysis was used to determine the percent carbonate by weight of rock samples collected from the upper Choshui River. All collected rocks were meta- sandstone to quartzite or slate to phyllite. Sampling locations can be found in Figure 3.1.

Total disseminated carbonate weight by percent ranges 0.19%–25.64%. The median value of all analyzed samples is 2.84%, while the mean is 5.20%. These averages may be biased away from the “typical” rock values since diversity of hand samples was actively sought.

3.4.2 Strontium Isotopes

Time-series for discharge and the 87Sr/86Sr ratio of Choshui River water samples were created to analyze co-variability between 87Sr/86Sr and the physical hydrology of the watershed (Figure 3.2). Discharge shows a large variation of several orders of magnitude from ~20 m3 s-1 to peak discharge at ~2600 m3 s-1, which is common in this watershed during typhoons. More detailed physical hydrology for the watershed can be found in

Chapter 2.

During base-flow conditions prior to the onset of Typhoon Mindulle related rainfall, two samples were collected. 87Sr/86Sr ratios for these samples show little variation, and have a range of 0.71494–0.71496, which is identical within the previously established uncertainties. Following the initial onset of rainfall and slight increase in discharge, the 87Sr/86Sr ratio of the stream water increases rapidly toward the silicate endmember (0.72047–0.72729 using 4 suspended sediment and 6 bedload samples; Chao et al., 2015) to a maximum of 0.71559. Directly after this peak value, the 87Sr/86Sr ratio of storm samples gradually decreases over the course of the typhoon toward the carbonate endmember (0.70954–0.71043 using the same 10 samples as the silicate) and an overall minimum of ~0.71423. During the following week, 87Sr/86Sr fluctuates between 0.71424 and 0.71480. This fluctuation coincides with a small increase in discharge after the main storm precipitation. Over the following month (until 17 August, 2004), 87Sr/86Sr values slowly trend toward pre-storm values before leveling off slightly lower than before the storm. The storm samples reveal additional variability when viewed on a smaller time-scale. This variability in the 87Sr/86Sr record occurs during the negative trend associated with the storm flow. After expanding the time-series to focus on the declining

87Sr/86Sr ratio during storm flow, three intervals of increasing 87Sr/86Sr values are seen

(Figure 3.2(B)). These peaks were concurrent with the three peaks in discharge visible in the storm hydrograph over this same period and are significant within ±2 sigma uncertainty.

The three hour sampling interval ended with the sample collected at 6:00 on 4

July, 2004. The next sample was not collected until 11:00 on 5 July, 2004 after rainfall had ceased. Because of this absence in data, a large portion of storm flow was not analyzed. This includes two additional discharge maxima. One of these maxima follows the highest intensity precipitation event in the watershed, while the other is associated with the absolute peak storm flow recorded at the gauge station. Only one sample was collected during the initial rapid decline of storm waters (Table 3.2).

A source mixing model was created using 87Sr/86Sr ratio and 1/[Sr] (to exaggerate changes in concentration) in parts per million (Figure 3.3). This type of model is commonly used to relate two or more water source endmembers with distinct radiogenic strontium signatures. Additional data from Wang (2003) collected during wet-season base-flow conditions were included to constrain the pre-storm signature. Pre-storm and upstream base-flow values show little variation with 87Sr/86Sr values ranging from

0.71471–0.71501 and 1/Sr values ranging from 2.28–2.82. At the onset of the storm,

87Sr/86Sr values shift more radiogenic, and total strontium becomes more dilute (shifts right). Following this initial shift, the storm flow samples have a general trend toward the bottom right of the plot (less radiogenic and more dilute). The 87Sr/86Sr ratio of these 17 samples range 0.71482–0.71560. The 1/Sr values have a range of 2.93–6.08. However, all but the first Typhoon Mindulle sample cluster more closely between 4.38 and 6.08.

The 11 samples collected and analyzed following the storm (following 4 July) are shifted toward less radiogenic and more concentrated than the storm samples, and have a general trend toward increased concentration and 87Sr/86Sr ratio. The 87Sr/86Sr ratio for these samples has a range of 0.71424–0.71481, and the 1/Sr values ranges 2.78–4.61. These samples approach pre-storm values but end slightly less radiogenic and more dilute than the defined base-flow. Arrows indicate the general trend through time of all samples. A few samples fall outside these general trends, and typically occur concurrent with sudden changes in discharge.

3.4.3 Major Ions and Molar Ratios

Plots of the molar ratios of major ions analyzed from Choshui River water samples provide clues to water source and weathering provenance, and how these sources change in relation to changes in storm related environmental factors.

Figure 3.4(A) shows a plot of magnesium-sodium ratios vs calcium-sodium ratios. The plotted ratios do not fall on a simple two endmember mixing line. The two samples taken prior to Typhoon Mindulle have little variation (Mg/Na ratios range 1.57–

1.60 and Ca/Na ratios range 2.18–2.33). Storm samples trend toward the top right of the plot, and molar ratios range 1.69–2.89 for Mg/Na and 2.57–7.28 for Ca/Mg. Following the storm, samples shift left before trending toward the pre-storm values, displaying an overall hysteretic pattern. Pre-storm values are not reached by the end of the sampling period in August. The post storm Mg/Na ratios range 2.10–2.94, while the Ca/Na ratios range 3.10–5.46. These same data are plotted following Gaillardet et al. (1999) to identify changes in carbonate-silicate mixing (Figure 3.4(B)).

A similar plot with potassium substituted for sodium can be seen in Figure

3.4(C). The two pre-storm samples show little variation with Ca/K ranging 27.33–27.81 and Mg/K ranging 19.08–19.75. During Typhoon Mindulle, there is a rapid shift down and left toward lower Mg/K and Ca/K ratios. Nearly all storm samples display very little variation, with the exception of two samples that lie between the pre-storm values and the close clustering of the other Mindulle data points. The full range of the Ca/K values for the storm samples is 6.48–18.80 (6.48–8.35 excluding the two initial transitional samples). The full Mg/K ratio ranges 2.89–12.32 (2.89–3.97 within the main cluster).

While little variation exists for the storm samples, there is a general trend back toward higher Mg/K and Ca/K ratios within the tight clustering as the storm progresses.

Following the storm, values gradually trend up and to the right toward pre-storm values, but do not fully return to these values. The Ca/K ratios for the post-storm period range

8.85–24.79 while the Mg/K ratio range 4.09–15.59.

Figure 3.5(A) shows a plot of calcium-sodium ratios vs. 1000 × (strontium- sodium ratio). The Sr/Na ratio was adjusted for ease of viewing by multiplying by 1000, but all values discussed will be referred to Sr/Na for simplicity. The plotted ratios do not fall on a simple two endmember mixing line. Pre-Typhoon Mindulle Ca/Na and Sr/Na ratios showed little variation. Ca/Na ranged 2.18–2.33, while Sr/Na was stable at ~8.16.

During the storm, both ratios increased up and to the right with some variation. Storm values had a Ca/Na range of 2.57–7.28, while Sr/Na varied from 7.54–13.85. After the

Mindulle storm flow, values gradually trended toward pre-storm levels, but never fully returned. The Ca/Na ratio ranged 3.10–5.46 while the Sr/Na ratio ranged 0.21–12.95.

Figure 3.5(B). is similar to Figure 3.5(A). but with potassium in place of sodium.

It shows similar trends to that of Figure 3.4(B). Pre-storm values show more variation than previous plots but are still distinct from typhoon and post-storm ion ratios. The Ca/K ratio ranges 27.33–27.81, and the Sr/K ratio ranges 97.2–102.4 prior to the storm. Nearly all Typhoon Mindulle samples group together tightly after a rapid negative shift in both ratios. Only two storm-flow samples fall outside this grouping, and these are the first two storm samples. The full range for the storm sample Ca/K is 6.48–18.80, while the tight grouping ranges 6.48–8.35. Sr/Na ranges 12.69–102.4 overall and 12.69–19.36 within the main clustering. Within the clustering, there is a slight trend back toward increased ratios as the storm progresses. Following the storm, values slowly trend more positive for both ratios. The Ca/K ratio for the post-storm samples varies 8.85–24.79, while Sr/K ratio ranges 19.2–81.3.

Figure 3.6 shows the relationship between HCO3/Na and Ca/Na. A generalized mixing line between global silicate and carbonate weathering is shown following

Gaillardet et al. (1999). All samples fall outside the idealized mixing line near the edge of possible mixing space. The overall trend of the samples show a general movement toward the carbonate endmember during the storm, followed by a gradual return to near pre- storm conditions, but with slightly elevated HCO3/Na values compared to those prior to

Mindulle. However, the direction of this change is not parallel to the mixing line. Pre- storm, storm flow, and post storm value ranges for Ca/Na are 2.18–2.33, 2.57–7.28, and

3.10–5.46 respectively. HCO3/Na values range 1.57–1.67 for pre-storm samples, 1.65–

6.58 for the storm samples, and 1.60–5.53 for post-storm samples.

Calcium magnesium and calcium-strontium molar ratios were calculated and plotted as a time-series to identify relative changes in the contribution of carbonate and silicate weathering in the system over the course of the typhoon (Figure 3.7). Both the

Ca/Mg and Ca/Sr ratios follow similar patterns through time. Pre-storm values were lower than those observed during the storm for both Ca/Mg and Ca/Sr ratios. Pre-storm

Ca/Mg ratios were ~1.4 while Ca/Sr ratios decreased slightly from ~130 to ~120. At the onset of Typhoon Mindulle, both ratios rise rapidly, with the Ca/Mg ratio reaching 2.47 and the Ca/Sr ratio reaching 237. Three peaks in each ratio occur largely concurrently with the three peaks of the storm hydrograph over this same period, similar to the

87Sr/86Sr time-series. However, the last peak in each time-series (and absolute maximum for both Ca/Mg and Ca/Sr at 2.62 and 258 respectively) does not persist throughout the same peak seen in the hydrograph and in the strontium isotope data. Following this maximum value, both records decrease gradually, and then stabilize following the storm slightly above pre-storm values (~1.5 for Ca/Mg and ~140 for Sr/Mg).

Total potassium and fluoride concentrations were plotted through time to address possible contributions from mica weathering (Figure 3.8). The overall pattern of [K+] is similar to that of 87Sr/86Sr. [K+] increases rapidly at the onset of the storm (from ~0.04 mM to ~0.15mM), and gradually declines over the course of the storm. This decline is much smaller compared to total change than that seen in the 87Sr/86Sr ratio. Small local maxima in [K+] occur during storm flow. However, these are not so pronounced, nor as concurrent with the discharge peaks as are the 87Sr/86Sr ratios. Following the storm, with the exception of the first data point, values of [K+] slowly decline toward pre-storm conditions but do not fully return. Concentrations stabilize near 0.05 mM. Fluoride

37 concentrations have high variability throughout the record. However, in Figure 3.8(a), there is a clear association between peaks in discharge and peaks in [F-].

A table summarizing all rainfall adjusted chemistry ratios used in this chapter is available in Table 3.2, along with concentration data for [K+] and [F-].

3.5 Discussion

3.5.1 Disseminated Carbonate

The median value of collected hand sample carbonate weight percent is similar to disseminated carbonate estimates from previous studies in Taiwan (Garzanti and

Resentini, 2016). However, there is much greater variability in total carbonate than previously established. The mean weight percent is skewed higher by a small number of high weight percent carbonate rocks (>10%; Table 3.1). These high values come from all rock types present in the upper Choshui River watershed, with both the highest and lowest weight percent originating from similar rock types. Both the quartzites and slates that represent the bulk of the lithology show considerable variability in the amount of disseminated carbonate they contain (0.19%–25.64%). While these values may not reflect the true bulk nature of the upper Choshui because of sampling bias for sample diversity, they do still suggest that the amount of carbonate material available for weathering may be substantially underestimated.

3.5.2 Strontium Isotopes

The two pre-storm values for the strontium isotope time-series (Figures 3.2) likely indicate normal base-flow conditions for the Choshui River at this location. This is supported by results from Wang (2003) that include wet-season base-flow measurements

38 for 87Sr/86Sr ratios at four slightly upstream locations compared to the Renlun Bridge typhoon sampling site. Previous work by Chao et al. (2015) provides values for the ratio of 87Sr/86Sr of carbonate leachates (leached from bulk material using sodium acetate) and residual silicate minerals in a separate, but similar Taiwanese watershed. Carbonate leachate values had a range 0.70954–0.71043, while residual silicate minerals showed a range of 0.72047–0.72729. The rapid increase in 87Sr/86Sr ratio toward the silicate endmember at the onset of Typhoon Mindulle is likely caused by an increase in the fractional contribution of silicate minerals at the surface compared to carbonates.

All samples analyzed for this study fall between these two endmembers.

Therefore, in a two endmember system, a positive 87Sr/86Sr shift corresponds to increased relative inputs of weathered silicate minerals into the river system. However, carbonate minerals are typically weathered more easily than silicate mineral, so this increased silicate input indicates that the source is likely coming from storage of previously weathered silicate materials rather than active weathering of bedrock. This study suggests this storage source is soil pore water. The soils of the watershed upstream of the sampling point are entisols and inceptisols (Chen et al., 2015). While entisols are defined by a minimal alteration from the bedrock material, inceptisols typically alter rapidly from the parent material (USDA, 1999). Because carbonate is more weatherable, it is likely that it is depleted within the soil environment relative to the bedrock in this rapidly denuded environment.

The above discussion suggests that the initial rise in 87Sr/86Sr ratios should represent a flux of soil water into the river system mediated by incoming rainfall. The gradual decline over the Typhoon Mindulle event would then represent dissolved silicate

39 rich soil waters being purged and replaced by fresher rainfall, diluting the relative silicate contribution. Increased active weathering of disseminated carbonate minerals by the storm may also contribute to this decline in 87Sr/86Sr, as carbonate becomes a larger fraction of the solute input. This would also help to drive the 87Sr/86Sr ratios toward the carbonate endmember. 87Sr/86Sr ratios are lower following Mindulle than pre-storm values. This is consistent with a combination of both soil water depletion and enhanced carbonate weathering during the storm. During dry base-flow conditions, soil water is not likely to be a major contributor to the system. Therefore, if direct rock weathering influence of both carbonates and silicates remained static throughout the storm, 87Sr/86Sr values would approach pre storm levels as the influence of soil pore water waned.

Enhanced direct weathering of the carbonate minerals is the likely driver towards

87Sr/86Sr values lower than the pre-storm levels. This is consistent with previous studies on wet-dry season river chemistry in regions with disseminated carbonates (Goldsmith,

2009; Chao et al., 2015; Tipper et al., 2006). Three peaks in the 87Sr/86Sr data that are concurrent with statistically distinct peaks in the discharge lend further credence to the soil water purging model (Figure 3.2(B)). During periods of more intense precipitation associated with hydrograph peaks, more water would be pulsed from the soils than during the in-between low-to-no precipitation intervals. This leads to brief increases in the fractional contribution of silicate weathering. However, the overall trend is still toward the carbonate end member due to the overall purging of soil water and enhanced carbonate weathering rates.

The gradual return to near base-flow conditions reflects the diminishing importance of carbonate weathering in the system as storm related waters recede. A

40 period of high variability in 87Sr/86Sr occurs directly following the storm, with more radiogenic values associated with a small, but distinct, increase in discharge. This may be caused by pulsing of soil pore waters by the rainfall event that led to the small hydrograph peak. This soil water may have had time to replenish its solute concentrations to some degree following the main storm, and these soils would likely still be much closer to saturation than they were before the storm, and thus would be likely to expel a large amount of water even with a relatively small rainfall event. This expulsion would move the 87Sr/86Sr ratio of the river water toward the silicate end member.

Strontium isotope mixing diagrams can elucidate changes in the contribution of two or more weathering source endmembers (Palmer and Edmund, 1992; Chu and You,

2007; Banner, 2004). Direct mixing between two endmembers results in samples that fall on a mixing line between the two (Faure, 1991) A mixing model was created for the samples collected before, during, and following Typhoon Mindulle 87Sr/86Sr, and suggests mixing among at least three end members (Figure 3.3). The headwaters of the

Choshui River lie in the Lushan hot spring province, and these waters may provide the third endmember. Major ion geochemistry data from these hot springs analyzed by Chen

(1985) show very depleted concentrations of calcium and magnesium with respect to other silicate cations, suggesting almost no carbonate influence. These deep silicate minerals are likely to have higher 87Sr/86Sr values than the surface carbonate minerals, and their signal should be highest during base-flow conditions because of minimal surface weathering input.

This type of mixing diagram is typically scrutinized as a static plot showing general mixing relationships. However, when viewed through the lens of mixing through

41 time, additional insight may be gained about mixing endmembers and why their contributions change. Arrows indicate the general direction of time across the sampling period. At the onset of the storm, 87Sr/86Sr values move toward the silicate endmember

(high 87Sr/86Sr, dilute compared to hot springs) before gradually moving toward the carbonate endmember over the course of the storm. This change in the relative contribution of surface silicates and carbonates is consistent with the purging of soil pore water containing higher concentrations of silicate weathered strontium. The downward movement is toward the carbonate while the rightward movement is indicative of general dilution from heavy precipitation. The rapid shift toward less radiogenic and more concentrated Sr is likely caused by continued increased fractional weathering of carbonate minerals, along with less dilution because of a cessation of rainfall as the storm passed. The slow return to more radiogenic and less dilute Sr following the storm is linked to an increased importance of the deep thermal waters to total flow volume as the river returns to near base-flow conditions. Overall this mixing model suggests four major events: 1) An abrupt transition to a surface weathering dominated system from one with a significant amount of deep thermal groundwater contribution at the start of Typhoon

Mindulle. 2) An initial increase in the fractional weathering input of surface silicates at the onset of the storm in relation to surface carbonates. 3) Increasing importance of active carbonate weathering to the total weathering inputs as the storm progresses. 4) Deep thermal waters slowly becoming important to the strontium inputs again as the discharge approaches base-flow conditions.

3.5.3 Major Ions and Molar Ratios

Multiple mixing diagrams were created using molar ratios of major ions in

Choshui River water samples to identify the possible endmembers involved in solute mixing. Many of these models are typically applied to analyze trends in bulk silicate vs carbonate weathering within a watershed. However, this study also uses distinct end member compositions and knowledge of the specific lithology of the Choshui River watershed to constrain the type and origin of silicate and carbonate weathering.

Typical interpretation of Mg/Na vs Ca/Na follows mixing endmembers established by Gaillardet et al. (1999) and has recently been used in a another Taiwan watershed (Chao et al., 2015). These endmembers (carbonate and silicate weathering sources) can be seen in log-log space (Figure 3.4(B)) along with same data plotted in

Figure 3.4(A). The values from Figure 3.4(A) were initially plotted in linear space to enable better visualization of the changes within the dataset outside of the general carbonate-silicate mixing line. This was done because the Gaillardet et al. (1999) end members are averages for rivers worldwide that drain distinct lithologies dissimilar to the

Choshui River watershed. The Gaillardet et al. (1999) silicate endmember includes all types of silicate rock but does not contain information on strictly low grade metamorphic rocks, which are the primary constituents of the upper Choshui River watershed. Because of this, sodium values are likely to be exaggerated compared to the Choshui River.

Sodium rich minerals such as plagioclase feldspar are common in many silicate rocks, but the area upstream of the Typhoon Mindulle sampling location is primarily composed of slate and low grade quartzites/metasandstones. Because of this, it is likely mica rich but sodium silicate poor. Slates are typically primarily composed of quartz, muscovite

(KAl2(AlSi3O10)(F,OH)2), and illite ((K,H3O)(Al,Mg,Fe)2(Si,Al)4O10[(OH)2,(H2O)]).

Illite has been shown to be the primary clay mineral assemblage of Choshui River sediments (Li et al., 2012). These mica minerals contribute K+, but no Na+ when weathered.

According to the traditional interpretation of the Mg/Na vs Ca/Na ratios, the beginning of storm flow is associated with an immediate increase in the relative contribution of carbonate weathered material that slowly becomes more and more dominant throughout the storm. However, this initially appears counter to the interpretation suggested by the strontium isotope data. Within the Choshui River it is more likely that this shift toward higher Ca/Na and Mg/Na values is actually caused by shift in weathering origin from a high Na+ and low Ca2+ water source to the surface weathering environment. Chen (1985) provides geochemical data from the Choshui River source waters. These waters originate from the Lushan hot springs and are associated with deep thermal waters. Na+ concentrations were ~25.8 mM while Ca2+ concentrations were only ~0.01 mM for samples collected at the spring outflow. Mg2+ was below the detection limit of the study. By adding this endmember to the mixing interpretation, the increasing Mg/Na and Ca/Na ratios associated with storm flow become consistent not only with an increase in the fractional carbonate input as a whole but also with the initial increase in surface silicate input suggested by the strontium data. This is because Sr2+ acts similarly to Ca2+ and is therefore likely depleted in the thermal waters and controlled by surface weathering. The initial shift toward the carbonate end member in the ion data is related to a weathering regime change away from a carbonate poor deep water signal to a signal completely dominated by surface weathering. This regime change effectively

44 makes the samples taken during the storm flow a two end member mixing problem between surface silicates and carbonates consistent with the 87Sr/86Sr data. This makes intuitive sense when one considers the massive volume change of the Choshui River from base-flow to storm-flow conditions where nearly all water is from the surface environment (~150X to 200X volume change).

To constrain and support this model of two weathering regimes with three end members, Mg/K and Ca/K values were analyzed similarly to the Mg/Na and Ca/Na values (Figure 3.4(c)). The Na+ concentration from the deep thermal water is exceptionally high, and surface slates and quartzites are not expected to contribute heavily to the Na+ reservoir, so the deep water signature contributes to the values seen in

Figure 3.4(a) as a third endmember. However, the mica rich slates should contribute to the K+ reservoir when weathered. Chen (1985) measured K+ concentrations at Lushan hot springs and these concentrations are ~13.8 times higher than the pre-storm base-flow of the Choshui (0.58 mM compared to 0.042 mM). Since this contribution should be diluted downstream and large amount of slate/micas are present at the surface, the vast majority of K+ at the sampling location likely comes from surface silicate weathering. Since the deep thermal water should not contribute significantly to Ca2+, Mg2+, or K+, the ratios of

Mg/K and Ca/K show linear mixing between the two surface endmembers.

The large rapid decrease in Mg/K and Ca/K at the start of Typhoon Mindulle is consistent with an increase in the fractional weathering of silicate material at the surface.

However, the small amount of variability during the bulk of storm flow (following the first two storm samples) suggests that depletion of soil pore water and enhancement of carbonate weathering do not affect the surface weathering profile as profoundly as the

87Sr/86Sr data appeared. This discrepancy is likely attributable to the strontium isotope system being influenced by three end members, with two of them being silicate minerals with more radiogenic ratios. Pre-storm Sr isotope values would already be elevated compared to what would be expected for just surface weathering because of the input of the deep thermal waters into the system. Therefore, the initial rise in the 87Sr/86Sr was likely dampened, and the soil water depletion and enhanced carbonate weathering were able to dominate the time-series profile since the storm samples were effectively only a two endmember system. While relatively minor, this movement toward carbonate weathering input is visible within the storm sample data of the Mg/K and Ca/K ratios.

Early in the storm, ratios reach minimum values then slowly move ~8% back toward pre- storm values. This was calculated following the series of equations in Eq. 3.1(a-c) which calculate the fraction of change in the storm samples compared to the overall change in ratios. These molar ratios further suggest that following the storm, carbonate weathering slowly increased its fractional contribution to the system as discharge approached base- flow conditions.

Eq. 3.1

퐶ℎ푎푛𝑔푒푆푡표푟푚 (a) % 퐶ℎ푎푛𝑔푒 = 푋 100 퐶ℎ푎푛𝑔푒푇표푡푎푙

⁄ 2 ⁄ 2 (b) 퐶ℎ푎푛𝑔푒푇표푡푎푙 = √[∆ (퐶푎 퐾)] 푇표푡푎푙 + [∆ (푀𝑔 퐾)] 푇표푡푎푙

⁄ 2 ⁄ 2 (c) 퐶ℎ푎푛𝑔푒푆푡표푟푚 = √[∆ (퐶푎 퐾)] 푆푡표푟푚 + [∆ (푀𝑔 퐾)] 푆푡표푟푚

Figure 3.5(a) shows the relationship among calcium, strontium, and sodium before, during, and following Typhoon Mindulle. It is similar to Figure 3.4(A) in that it incorporates sodium, and thus should be affected by mixing from the deep thermal water end member. Sr2+ commonly substitutes for Ca2+ and therefore is typically present in much higher concentrations in carbonate minerals than silicate minerals (Faure, 1991).

This plot clearly shows the hysteretic pattern of three endmember mixing more so than the other molar ratio comparisons do. This is associated with the lower Sr2+ concentrations in silicate rocks, so carbonate dissolution and the switching of weathering regimes to the surface dominates changes in the Sr/Na ratio. Overall trends are similar to those of Figure 3.4(a), and show storm values that move toward a more carbonate composition, consistent with the switch to surface weathering control of the system. It also shows the gradual return to base-flow conditions and the re-introduction of deep thermal water as an important solute source. However, this return displays much more variability than that of the Mg/Na vs Ca/Na comparison. Since the Ca/Na ratio is involved in both comparisons, this variability can likely be contributed to changes in Sr2+ concentrations. This variability occurs because very small changes in weathering source can cause large perturbations in the strontium system since its concentrations are so low.

Changes in the Sr/Na ratio may then reflect very small precipitation events that cause a small release of soil water into the non-storm flow conditions. They could also reflect small anthropogenic additions from villages upstream of the sampling location. However these villages are very small and number very few amongst a much larger and largely pristine watershed.

Trends in the comparison of Ca/K and Sr/K (Figure 3.5(B)) follow very closely to those of Mg/K vs Ca/K. The plot follows a distinct two endmember mixing curve that does not reflect inputs of the deep thermal waters. This is because Sr2+ from the hot spring water, like Ca2+, Mg2+, and K+, is likely not very prevalent in the system at the sampling location due to dilution. Since Sr2+ is a common substitution for Ca2+, it would be expected that a Ca poor system would also be poor in Sr, even if it was not measured in Chen (1985). All trends reflect those previously discussed for Figure 3.4(B). However, the decline in silicate influence over the course of Typhoon Mindulle is more readily visible in the Sr data. Following Eq. 3.1(a-c), these data suggest a return of ~8% toward pre-storm values over the course of the storm, consistent with the earlier estimate.

Gaillardet et al. (1999) also introduce a source mixing model focused on bicarbonate, calcium, and sodium in a log-log environment. Choshui River samples were plotted in this environment (Figure 3.6), but fall away from the silicate-carbonate mixing line. Additionally, the trend of change in the samples is not parallel to the mixing line.

The beginning of the storm shows a deviation in the general direction of the carbonate end member, consistent with previous mixing diagrams. A return to more silicate contribution slowly occurs following the storm. However, this return does not trend toward pre-storm values. This mixing diagram was developed without consideration of additional weathering agents beyond carbonic acid. Because of this, the offset from the mixing line and direction likely indicates weathering of carbonates and silicates not associated with atmospheric CO2. Pyrite has been shown to occur throughout many, if not all, watersheds in Taiwan (Chung et al., 2009; Das et al., 2011; Kao et al., 2004), and

2- SO4 is a major anion constituent of all waters analyzed in this study. This indicates the

48 presence of sulfuric acid source weathering. Constraints on this portion of the weathering regime are discussed in detail in Chapter 4.

Time-series of Ca/Mg, Ca/Sr, K+, and F- concentration were created to identify changes in total contribution of carbonate and silicate minerals associated with the

Typhoon Mindulle event (Figures 3.7 and 3.8).

Ca/Mg and Ca/Sr are commonly used to identify changes in the carbonate to silicate weathering signal. This holds true for the Choshui River watershed as well, but since the deep thermal water endmember is highly depleted in Ca, Mg, and likely Sr, and the surface silicates are likely relatively depleted in these elements also, these ratios should reflect general changes in the total carbonate contribution to weathered solutes. As would be expected, these ratios are correlated with discharge (and therefore rainfall), and suggest increasing total carbonate weathering yields during increasing flow. Following the storm, both Ca/Mg and Ca/Sr decrease gradually before stabilizing above pre-storm values. This suggests, along with the K+ concentration data, that wet-season weathering rates are elevated for both carbonates and silicates compared to the dry season. Overall, higher precipitation increases carbonate weathering to a greater degree than silicate weathering, even though weathering rates for both likely increase substantially. This is entirely consistent with wet and dry season weathering analyses conducted in the Choshui watershed by Goldsmith (2009).

K+ is the only cation to increase in concentration during the storm flow compared to pre-and-post storm values. This represents a shift to increased weathering of mica rich surface silicates and an absolute increase in surface silicate weathering. Dilution pressure

49 from the extremely high rainfall and flow volumes would suppress the total K+ concentration, but [K+] remains elevated well above base-flow conditions for the entirety of the storm suggesting enhanced mica weathering. The time-series also displays the general decreasing trend across the storm similar to the 87Sr/86Sr data. This trend is consistent with slow purging of the silicate sourced solute rich soil pore waters. However, this trend is much smaller than it is in the strontium data. This is likely because K+ had little contribution from the deep thermal waters prior to the storm, and is reflecting the minor change to less silicate influence discussed for Figures 3.4(b) and 3.5(b). The three peaks seen in the strontium data are not present in the K+ concentration data. However, since this time series is focused on absolute concentration without reference to another constituent, it is subject to dilution effects on its totals. These three peaks correspond with extremely high flow conditions, and thus are likely suppressed in the K+ concentration record. Following Typhoon Mindulle, K+ values slowly return to conditions associated with less surface mica weathering and more input from the geothermal waters. However, total concentrations level out slightly higher than pre-storm base-flow, similar to the discharge, suggesting slightly higher weathering rates of surface silicate during wet season base-flow compared to the dry season. F- concentrations are more variable, but increased F- should correspond with increased weathering of specific mica minerals where it is sometimes substituted for (OH)-. Three clear peaks in the fluoride data occur during increased discharge further suggesting enhanced total weathering of mica minerals during extreme weather.

3.6 Conclusions

Strontium isotope geochemistry used in conjunction with major ion analysis of

Choshui River water has provided clues to water and weathering source provenance in the watershed, and how this provenance changes in response to extreme rainfall. The data in this chapter indicate four major changes in the dynamics of weathering sources and contributions within the Choshui River watershed in response to Typhoon Mindulle.

These are as follows:

1) A switch from a system where deep thermal waters are important to the solute

flux to one dominated by surface weathering.

2) Purging of silicate sourced solute rich soil pore waters during periods of extreme

rainfall and discharge.

3) Gradually increasing importance of carbonate weathering at the surface during the

typhoon.

4) Increased total weathering rates for both carbonate and silicate minerals during

storm flow.

The time-series for 87Sr/86Sr indicates changes in the relative contribution of surface carbonate and silicate minerals in response to Typhoon Mindulle. An initial increase in the ratio along with increases that coincide with peaks in discharge suggest increased input of soil water containing a higher percentage of silicate weathered mineral solutes compared to direct rock weathering. However, soil water becomes depleted, and extreme rainfall increases the relative contribution of surface carbonates overall. The strontium isotope mixing diagram indicates a third endmember outside of these surface silicates and carbonates. Molar ratio mixing diagrams suggest this third endmember is

51 deep thermal water that forms the Choshui River headwaters near Lushan. These diagrams indicate a regime change in weathering source from a thermal water influenced regime to one completely dominated by weathering of surface minerals. Additionally, they suggest that weathering of complex mica minerals like illite and muscovite may be important contributors to the surface silicate weathering system in the watershed. Time- series for Ca/Mg and Ca/Sr indicate increased weathering rates of carbonate minerals during the storm, while [K+] and [F-] concentrations indicate increased weathering rates of mica minerals during storm flow. [K+] data lend further credence to the soil purge model.

Table 3.1: Carbonate Loss on Ignition Analysis Carbonate § Ŧ Crucible & Sample % Mass Sample # Type Lithology Crucible M M CO Mass Dry Sample 550 950 Dry Weight 2 Carbonates Loss TW15-1A BR Slt 20.037 23.556 23.446 23.402 3.519 0.04 2.84 TW15-1B BR Qtz 19.841 23.382 23.350 23.312 3.541 0.04 2.44 TW15-2A SC Qtz 20.427 23.927 23.886 23.779 3.500 0.11 6.95 TW15-2B BR Slt 20.653 24.172 24.068 24.039 3.519 0.03 1.87 TW15-3A BR Slt 19.934 23.438 23.328 23.276 3.504 0.05 3.37 TW15-3B SC Slt 19.688 23.196 23.089 23.009 3.508 0.08 5.19 TW15-4A SC Mss/Qzt 20.599 24.100 24.054 23.981 3.501 0.073 4.74 53 TW15-4B SC Mss/Qzt 20.652 24.122 24.038 24.011 3.470 0.027 1.77

TW15-5A SC Slt 19.688 23.198 23.098 23.060 3.510 0.038 2.46 TW15-5B SC Mss/Qzt 19.933 23.437 23.321 22.926 3.504 0.395 25.64 TW15-5C SC Qtz 20.427 23.969 23.964 23.961 3.542 0.003 0.19 TW15-6 BR Slt 19.842 23.352 23.267 23.197 3.510 0.070 4.54 TW15-7 BR Phl 20.327 23.847 23.785 23.742 3.520 0.04 2.78 TW15-8B SC Qtz 19.946 23.463 23.441 23.402 3.517 0.04 2.52 TW15-8C SC Slt/Phl 19.700 23.283 23.213 23.044 3.583 0.17 10.73 — All masses and mass losses are given in grams. § - Sample types are listed as BR (Bedrock) or SC (Stream Cobble) Ŧ – Sample lithologies are given as Slt (slate), Qtz (quartzite), Mss (meta-sandstone), or Phl (phyllite).

Table 3.2: Strontium Isotope, Ion Molar Ratio, and Select Concentration Data 1/Sr 1000 X 1000 X HCO3/N HCO3/ Ca/M [K+] [F-] Date and Time 87/86Sr Ca/Na Mg/Na Ca/K Mg/K Ca/Sr (ppm) Sr/Na Sr/K a K g (mM) (mM) 6/28/04 11:20 0.71496 2.82 2.34 1.60 8.16 27.81 19.08 97.22 1.69 20.07 1.46 286.1 0.042 0.0095 6/30/04 9:48 0.71494 2.71 2.18 1.57 8.16 27.33 19.75 102.41 1.57 19.76 1.38 266.9 0.041 0.0105 7/1/04 6:05 – 2.88 2.57 1.69 8.91 18.80 12.32 65.10 1.65 12.06 1.53 288.9 0.061 0.0104 7/1/04 12:03 0.71544 2.93 3.58 1.90 10.66 15.38 8.16 45.77 2.00 8.58 1.89 336.0 0.085 0.0121 7/1/04 15:00 – 3.90 5.36 2.42 12.64 8.21 3.71 19.36 2.64 4.04 2.21 423.9 0.151 0.0095 7/1/04 18:00 – 5.11 6.04 2.50 12.20 7.36 3.05 14.88 4.57 5.57 2.42 494.8 0.150 0.0090 7/1/04 21:04 0.71559 5.40 6.36 2.58 12.28 7.45 3.02 14.38 5.95 6.96 2.47 518.3 0.147 0.0096 7/1/04 23:54 – 6.03 5.81 2.51 11.23 6.70 2.89 12.95 5.44 6.27 2.31 517.3 0.146 0.0079 7/2/04 0:00 – 6.13 5.54 2.49 10.86 6.48 2.91 12.69 5.33 6.23 2.23 510.4 0.147 0.0072 54 7/2/04 2:57 0.71534 4.38 6.04 2.64 13.85 7.82 3.41 17.93 5.18 6.70 2.29 436.1 0.145 0.0106

7/2/04 5:57 – 5.36 5.80 2.79 12.71 7.28 3.50 15.96 5.55 6.97 2.08 455.9 0.133 0.0086 7/2/04 8:59 0.71527 4.66 5.90 2.73 13.29 7.57 3.50 17.05 5.61 7.20 2.16 443.9 0.144 0.0118 7/2/04 11:57 0.71515 5.03 6.27 2.77 13.54 8.26 3.65 17.83 6.37 8.39 2.26 463.1 0.127 0.0074 7/2/04 15:20 – 5.60 6.22 2.82 12.69 7.90 3.58 16.10 5.52 7.01 2.21 490.7 0.127 0.0074 7/2/04 18:15 0.71529 5.43 6.61 2.81 13.20 8.35 3.56 16.67 6.57 8.29 2.35 500.7 0.126 0.0078 7/2/04 21:00 – 5.66 6.06 2.79 12.21 7.45 3.43 15.02 5.88 7.23 2.17 496.2 0.134 0.0086 7/3/04 0:00 0.71513 5.60 5.94 2.88 12.36 7.32 3.54 15.21 5.91 7.27 2.07 480.9 0.134 0.0091 7/3/04 3:00 0.71511 5.49 5.92 2.76 11.77 7.95 3.71 15.80 6.25 8.39 2.14 503.2 0.132 0.0096 7/3/04 6:00 0.71488 5.60 5.95 2.64 12.06 7.93 3.52 16.08 5.62 7.49 2.25 493.2 0.127 0.0087 7/3/04 8:58 0.71488 5.68 7.28 2.78 12.89 8.12 3.11 14.38 6.58 7.34 2.62 564.8 0.140 0.0090 7/3/04 12:00 0.71493 5.56 5.59 2.26 10.47 7.85 3.18 14.70 5.24 7.37 2.47 534.1 0.140 0.0086 7/3/04 15:11 0.71514 5.79 3.97 1.95 8.42 7.07 3.47 14.99 4.71 8.38 2.04 471.5 0.132 0.0091 7/3/04 17:55 0.71498 5.66 3.58 1.75 7.86 8.14 3.97 17.88 5.15 11.71 2.05 455.2 0.113 0.0100 7/3/04 21:05 0.71502 6.06 3.63 1.80 7.54 7.78 3.87 16.18 5.18 11.11 2.01 481.1 0.116 0.0089 7/4/04 0:00 0.71499 6.01 3.65 1.85 7.63 7.53 3.81 15.75 5.04 10.41 1.98 478.2 0.121 0.0057

Table 3.2: Strontium Isotope, Ion Molar Ratio, and Select Concentration Data (Continued) 1000 1/Sr Ca/N 1000 X HCO3/N [K+] [F-] Date and Time 87/86Sr Mg/Na X Ca/K Mg/K HCO3/K Ca/Mg Ca/Sr (ppm) a Sr/K a (mM) (mM) Sr/Na 7/4/04 3:00 0.71496 5.82 3.63 1.86 7.88 7.35 3.77 15.97 4.78 9.68 1.95 460.5 0.123 0.0054 7/4/04 6:00 0.71481 6.08 3.98 1.96 8.41 7.57 3.73 16.01 5.35 10.18 2.03 472.6 0.117 0.0068 7/5/04 11:05 0.71423 4.18 5.32 2.46 11.55 8.85 4.09 19.21 4.37 7.26 2.16 460.6 0.142 0.0086 7/8/04 14:18 0.71475 4.14 5.46 2.94 12.36 11.99 6.45 27.13 4.68 10.27 1.86 442.1 0.102 0.0065 7/9/04 12:53 0.71432 4.43 4.97 2.66 12.13 11.84 6.33 28.91 5.14 12.25 1.87 409.5 0.089 0.0059 7/9/04 17:38 0.71480 4.19 4.53 2.61 10.90 11.19 6.46 26.94 4.20 10.39 1.73 415.5 0.101 0.0056 7/12/04 9:40 0.71424 4.61 4.66 2.34 11.66 13.72 6.90 34.34 5.53 16.30 1.99 399.6 0.072 0.0087

7/14/04 8:50 0.71455 3.67 4.25 2.78 12.28 12.69 8.28 36.65 3.38 10.09 1.53 346.2 0.085 0.0076 7/16/04 8:55 0.71447 3.62 3.92 2.62 11.54 13.16 8.80 38.69 3.23 10.84 1.50 340.2 0.081 0.0058 7/19/04 8:50 0.71438 3.18 4.30 2.54 12.69 17.55 10.35 51.74 3.61 14.71 1.70 339.2 0.069 0.0073 7/21/04 8:50 – 3.16 3.66 2.37 11.37 16.79 10.87 52.21 2.34 10.75 1.54 321.5 0.069 0.0071 7/23/04 8:50 – 3.08 3.84 2.47 12.41 17.60 11.33 56.93 2.71 12.45 1.55 309.2 0.065 0.0070 7/26/04 9:05 – 3.03 3.59 2.38 11.26 18.94 12.55 59.42 1.70 8.95 1.51 318.7 0.063 0.0071 7/28/04 9:15 0.71474 2.87 3.61 2.35 11.69 20.13 13.09 65.23 2.14 11.93 1.54 308.5 0.061 0.0083 7/30/04 9:10 – 3.12 3.59 2.37 11.26 19.62 12.93 61.48 1.88 10.26 1.52 319.2 0.060 0.0086 8/2/04 10:15 – 3.00 3.51 2.28 11.19 21.21 13.79 67.56 2.16 13.07 1.54 314.0 0.056 0.0263 8/4/04 10:53 – 2.84 3.15 2.18 10.80 22.54 15.60 77.44 1.65 11.81 1.45 291.1 0.052 0.0229 8/6/04 12:41 0.71473 3.00 3.60 2.22 10.78 20.79 12.80 62.28 2.50 14.44 1.62 333.8 0.061 0.0238 8/9/04 12:51 – 2.93 3.10 2.10 10.21 22.78 15.44 74.99 1.60 11.72 1.48 303.8 0.052 0.0266 8/12/04 11:20 – 3.77 4.66 2.32 12.95 23.02 11.47 63.97 14.51 71.65 2.01 359.8 0.047 0.0299 8/13/04 8:17 – 3.10 3.30 2.14 10.73 22.45 14.56 72.93 11.91 80.96 1.54 307.8 0.051 0.0247 8/16/04 13:19 0.71462 2.78 3.21 2.03 10.52 24.79 15.72 81.29 11.52 89.01 1.58 304.9 0.051 0.0184

Figure 3.1. Map of Typhoon Mindulle water sampling and Upper Choshui River rock sampling sites. Sites for this study are in yellow. Goldsmith et al. (2008) is in blue.

Figure 3.2. Time series for discharge and 87Sr/86Sr ratio at the Renlun Bridge sampling location for (A) the entire sampling period (28 June, 2004 – 17 August, 2004), and (B) the period before and during storm flow. Yellow bars indicate three peaks in discharge during the storm-sampling period.

Base Flow

Figure 3.3. Source mixing model for dissolved strontium before, during, and following Typhoon Mindulle. Black arrows indicate the general direction of change through time during and following the storm. Upstream base flow values (Red Triangles) are taken from Wang (2003). Typical storm flow and post-storm flow ranges are confined by blue and green ovals respectively.

A B

C Figure 3.4. Molar ratio mixing diagrams for (A) Mg/Na versus Ca/Na in linear space, where the general hysteretic trend is indicated by black arrows, (B) Mg/Na versus Ca/Na in log-log space with silicate and carbonate endmembers from Gaillardet et al. (1999) shown, and (C) Mg/K versus Ca/K. For all diagrams, pre-storm samples, Typhoon Mindulle storm samples, and post storm samples are plotted as red squares, blue circles, and green circles respectively.

Figure 3.5. Molar ratio mixing diagrams for samples collected before, during, and following Typhoon Mindulle. The diagrams are (A) Ca/Na versus 1000×Sr/Na, where the hysteretic change is indicated by black arrows, and (B) Ca/K versus 1000×Sr/K. For both diagrams, pre-storm samples, Typhoon Mindulle storm samples, and post storm samples are plotted as red squares, blue circles, and green circles respectively.

Figure 3.6. Molar ratio mixing diagram of HCO3/Na versus Ca/Na. Carbonate and silicate weathering endmember chemistry and their respective mixing line follows Gaillardet et al.

(1999). No samples fall near the expected mixing line for CO2 driven weathering of these endmembers.

Figure 3.7. Time-series plots showing changes in ratio of Ca/Mg and Ca/Sr alongside discharge for (a) the entire sampling period and (b) Typhoon Mindulle storm flow. Yellow bars indicate three peaks in discharge during the storm-sampling period.

Figure 3.8. Time-series plots showing changes in concentration of K+ and F- alongside discharge for (a) the entire sampling period and (b) Typhoon Mindulle storm flow. Yellow bars indicate three peaks in discharge during the storm-sampling period.

Chapter 4: Typhoon Induced Atmospheric Carbon Consumption

4.1 Introduction

This study estimates the total atmospheric carbon consumption or export from the atmosphere for silicate and carbonate minerals within the Choshui River watershed.

Stream chemical fluxes provide a powerful tool for estimating changes in weathering regimes. Dissolved silica is produced during chemical weathering of crustal silicate rocks through CO2 mediated hydrolysis. Therefore dissolved silica in natural waters serves as an indicator of silicate weathering rates and carbon uptake from the atmosphere (White and Blum, 1995; Kump et al., 2000). Dissolved silica fluxes have been used in many studies to estimate CO2 consumption associated with silicate rock weathering (Edmond and Huh, 1997; Lyons et al., 2005; Goldsmith et al., 2008). Previous studies of HSI watersheds suggest these terrains have some of the highest CO2 consumption rates globally (Carey et al., 2002; Jacobson and Blum, 2003; Lyons et al., 2005).

The uplift weathering hypothesis for CO2 consumption from Raymo and

Ruddiman (1992) suggests that active orogenic uplift is responsible for a large portion of active silicate weathering and carbon uptake from the atmosphere. Following up on this hypothesis, many studies have been done on Himalayan and New Zealand river chemistry and CO2 fluxes (Dalai et al., 2002; Galy and France-Lanord, 1999; West et al.,

2002). These studies established the importance of small amounts of secondary

disseminated carbonate in the total weathering of the system (Jacobson and Blum, 2000;

Galy and France-Lanord, 2001; Dalai et al., 2003; Jacobson et al., 2003; Quade et al.,

2003). Similar disseminated carbonates are widespread throughout the lightly metamorphosed rock of the western Central Mountain Range and Hsueshan Range of

Taiwan. Weathering of these carbonate minerals has been found to contribute up to 75% of total weathering yields (Goldsmith, 2009; Chung et al., 2009), and cannot be ignored when calculating CO2 flux from the atmosphere.

Pyrite oxidation can contribute dramatically to total weathering yields, but does not consume any CO2 in its associated reactions (Galy and France Lanord, 1999; Lerman et al., 2007). Sulfuric acid from pyrite oxidation has been implicated as a major source of weathering in many watersheds in Taiwan (Goldsmith, 2009; Calmels et al., 2011; Das et al., 2012). Sulfate is a major ion in the waters of the Choshui River watershed. Since no gypsum is assumed to be in the watershed (Goldsmith, 2009), pyrite oxidation is the likely cause of sulfate introduction. Because sulfuric acid weathering does not consume

CO2, it needs to be accounted for when determining CO2 drawdown from silicate weathering. Galy and France-Lanord (1999) provide methodology for estimating and removing weathering fluxes from pyrite induced weathering. Using this method, the fractional contribution of H2SO4 weathering compared to atmospheric CO2 derived weathering was calculated. A more comprehensive guide for this calculation is examined in the discussion section of this chapter.

Typhoon induced mechanical weathering has been shown to move mass quantities of sediment and particulate organic matter to the oceans (Dadson et al., 2003; Milliman and Kao, 2005; Goldsmith et al., 2008; Hilton et al., 2008), and physical weathering rates

are a major control on chemical weathering rates (Lyons et al., 2002). While many studies have been done on the chemical weathering rates of HSIs, particularly in Taiwan, only one known previous study has attempted to identify CO2 fluxes associated with typhoon impact (Goldsmith et al., 2008). However, this study only identified silicate fluxes and did not account for weathering of disseminated carbonate material or sulfuric acid mediated weathering. This study provides the first known estimates of total carbon flux from the atmosphere during a period of typhoon induced weathering for both silicate and carbonate minerals.

4.2 Methodology

All sample collection and analysis of major ion geochemistry follows the methods detailed in Chapter 3. Dissolved silica concentrations were analyzed using ICP-OES during the same time period that the cation data were analyzed using this method.

CO2 consumption rates over 72 hours were calculated using four distinct methods across the storm event:

1) Calculated from 2 × [Si]. This is for comparison with Goldsmith et al. (2009),

following Edmund and Huh (1997).

2) Adjusted from 2 × [Si] using a 40% approximation of H2SO4 contribution to

total weathering yields. This approximation was generated using Figure 4.3.

3) Adjusted from 2 × [Si] by multiplying by calculated XSO4 values (Eq. 4.2)

- 4) Calculated from summing the HCO3 flux determined to be from CO2 export

from the atmosphere by adjusting both silicate and carbonate weathering yields

using the XSO4 adjustment factor. This calculation is explained step-wise with

reasoning in the discussion section.

4.3 Results

4.3.1 Pyrite Oxidation Weathering Contribution

A plot of rainfall adjusted equivalency (in meq L-1) of Ca2+ and Mg2+ versus

- HCO3 was created to assess the maximum possible contribution of carbonate minerals to alkalinity (Figure 4.1(a)). A negative relationship was associated between (Ca2+ + Mg2+)

- - 2+ 2+ and HCO3 . A general shift to higher HCO3 concentrations compared to Ca + Mg was observed during storm flow. Following the storm, this trend reversed toward the pre- storm values, and eventually moved beyond the pre-storm conditions. While the negative trend is clear, the data have a fairly large spread around it. In particular, the “storm samples” typically plot below the trend line, while the post storm samples plot above it.

All samples plot well above the 1:1 equivalency line.

To determine whether pyrite produced H2SO4 weathering is an important contributor to the dissolved solute flux of the system, a plot of Ca2+ and Mg2+ versus

- 2- HCO3 and SO4 equivalency was created (Figure 4.1(b)). This assumes that H2SO4 does not preferentially weather silicate or carbonate minerals with respect to H2CO3, There is a strong linear relationship between the positive and negative charge equivalents. All values plot near, but below, the 1:1 equivalency line. The distance from the line is dependent on whether the sample came before, during, of following Typhoon Mindulle.

Pre-storm values plot the furthest from the line, followed by post-storm samples. Values move toward the 1:1 line during storm flow.

An initial approximation of the contribution of H2SO4 to the total chemical weathering yields was created using Figure 4.2. The figure plots total cation

+ concentration of water samples (TZ ) versus the concentration of SO4. Additionally, two idealized lines of pure silicate (1:1) and pure carbonate (2:1) weathering by H2SO4 are plotted. The silicate weathering line assumes all silicate weathering will release one cation per reaction. Both lines are referenced to a system where 100% of weathering is driven by H2SO4 and do not account for H2CO3 weathering. Lines are derived from the assumption that two H+ ions are required per silicate weathering reaction, but only one is needed to weather carbonate. All data fall near a single linear trend with a slope of 0.8.

This slope is lower than either of the 100% H2SO4 weathering bounds. This correlation

+ 2- has a positive relationship between TZ and SO4 . The approximation of the maximum

2- + H2SO4 contribution to silicate weathering is taken to be (0.5 × slope) because the H to silicate cation release ratio is assumed to be 2:1. Both concentrations decrease during storm flow and slowly return to near pre-storm conditions following storm flow.

The XSO4 coefficient was calculated following Galy and France-Lanord (1999) and Goldsmith (2009). It represents the fractional contribution of pyrite oxidation and sulfuric acid production to the chemical weathering of silicate and carbonate minerals.

After correction of the total weathering yields for H2SO4, the fractional contributions of

CO2 induced weathering of silicate and carbonate minerals were calculated. These

- contributions were used to calculate HCO3 input into the Choshui River system from

- CO2 uptake from the atmosphere. The HCO3 data are plotted against the total H2SO4

- corrected cation concentration in (Figure 4.3). After correction, the cation and HCO3

data share a close linear relationship. This relationship has a positive slope of 0.699. No clear trends between storm and post-storm samples are evident in the plot.

XSO4 changes through time and H2SO4 does not contribute to HCO3 at the same ratio as it contributes to total weathering. H2SO4 weathering as a percent of total weathering (calculated as XSO4 x 100%) is plotted in Figure 4.4 as a time series with

- discharge. With the XSO4 corrected HCO3 concentrations, total contributions of HCO3 from CO2 sourced silicate and carbonate weathering were calculated. These contributions were used to calculate the contribution of both mineral types to total weathering and the consumption of CO2. Subsequently, the fractional contribution of silicate versus carbonate mineral uptake of atmospheric carbon is calculated. The percent contributions to total weathering and CO2 export from the atmosphere by carbonate compared to silicate minerals are also shown in Figure 4.4.

The carbonate contribution to total weathering is ~74% prior to Typhoon

Mindulle. This contribution rapidly increases at the beginning of the storm to ~86%, but then changes very little during the remainder of the storm. After the storm, there is a relatively rapid return to lower carbonate contribution to total weathering. Following this initial rapid decline, carbonate weathering slowly becomes less important to the system before stabilizing near 75%. The carbonate contribution to CO2 export has much larger variation than the contribution to total weathering. It changes from ~45% pre-storm to

~65% at the beginning of the storm. Carbonate weathering CO2 export correlates with discharge and reaches a peak of ~69% during the highest discharge of the storm sampling period. Following the storm, carbonate slowly decreases in importance to CO2 export before stabilizing near 40% of the total.

+ 2- + - Values for TZ , SO4 adjusted TZ , XSO4, H2SO4 weathered HCO3 (mM), CO2

- - weathered HCO3 (mM), Silicate CO2 weathered HCO3 , Carbonate CO2 weathered

- - - HCO3 , %H2SO4 sourced HCO3 , % CO2 source HCO3 , % silicate contribution to CO2 sourced weathering, and % carbonate contribution to CO2 sourced weathering are listed in Table 4.1.

4.3.2 Silicate Weathering Yields and CO2 Consumption

-1 Total dissolved silica flux [g H4SiO4 s ] of the Choshui River watershed to the ocean varies by nearly two orders of magnitude over the sampling interval for this study.

-1 -1 Minimum H4SiO4 flux occurs pre-storm and is ~400 g s . Peak flux reaches ~36,600 g s during the falling edge of storm discharge (the sampling point directly after the three hour

“storm sampling” interval), and is ~76 times higher than the pre-storm minimum. Peak

-1 storm sample flux reaches ~29,000 g H4SiO4 s , an increase of ~73 times over pre-storm values. Choshui River discharge near the Renlun Bridge sampling location during the

Typhoon Mindulle event is shown in Figure 4.5 along with hourly precipitation totals

and total H4SiO4 flux related to this storm discharge. Total H4SiO4 flux varies closely with discharge during the storm sampling period.

The four methods established for estimating CO2 consumption/export rates were used to calculate total CO2 consumption/export for the pre-storm and storm samples. CO2 consumption is calculated as the average rate of CO2 consumption between two samples, multiplied by the total time interval between the samples. Post-storm consumption was not calculated because of relatively long periods of time that have no data and high variability between data points. This suggests variability in CO2 consumption may occur

post-storm that would not be accounted. An additional storm consumption total over 96 hours is adapted from Goldsmith et al. (2008).

2 CO2 consumption/export totals are given in Table 4.2 [tons CO2 per km ], along with 24 hour normalized CO2 consumption/export totals and the ratio of storm related

CO2 drawdown to pre-storm drawdown. The pre-storm consumption totals for each method are 0.067 ton km-2, 0.041 ton km-2, 0.021, and 0.038 ton km-2 respectively. The storm related CO2 export for the four methods and from Goldsmith et al. (2008) are 1.633 ton km-2, 0.980 ton km-2, 0.845 ton km-2, 2.507 ton km-2, and 2.100 ton km-2 respectively.

Total pre-storm daily normalized export for the four methods are 0.024 ton km-2, 0.015 ton km-2, 0.008 ton km-2, and 0.014 ton km-2 respectively. Daily normalized storm consumption for the four methods and Goldsmith et al. (2008) are 0.545 ton km-2, 0.327 ton km-2, 0.282 ton km-2, 0.836 ton km-2, and 0.525 ton km-2. Finally, the ratio of storm to pre-storm consumption/export is as follows, 22.3X, 22.3X, 37.5X, and 61X. Pre-storm data are not available from Goldsmith et al. (2008).

Method four should be the most accurate determination for CO2 export from the atmosphere, but not for CO2 consumption since it incorporates carbonate weathering.

-1 Figure 4.6 shows a time-series of both the CO2 export rate in mol CO2 s calculated by this method and the CO2 consumption rate calculated using [Si] and XSO4 in method 3.

-1 Pre-storm values for total export are very low (<10 mol CO2 s ). At the beginning of the

-1 storm there is a rapid increase to ~220 mol CO2 s concurrent with the first hydrograph

-1 peak. The export rate then decreases to ~150 mol CO2 s between discharge peaks before

-1 increasing and leveling at ~230 mol CO2 s during the second discharge peak. A very

-1 rapid increase from ~290 to 1200 mol CO2 s over 12 hours occurs synchronous with the

third and much larger discharge peak. The CO2 export rate then trends down relatively

-1 rapidly toward pre-storm levels over the next several days. It reaches ~110 mol CO2 s before slowly declining to near pre-storm levels over the following month. The XSO4 adjusted [2×Si] CO2 consumption rate from method 3 follows similar trends but to a much lesser degree. While the timing of the peaks in export occur during peaks in the storm hydrograph record, the changes in magnitude of the peaks in the CO2 consumption rate do not reflect the related magnitude changes in discharge. The third peak in each record is the highest of the three covered in the sampling period and the export rate increases by ~5 times over the earlier peaks. Discharge only increases by less than 3 times compared to the earlier peaks.

4.4 Discussion

4.4.1 Pyrite Oxidation Contribution to Weathering Fluxes

- Calculations for weathering source contributions to HCO3

Pyrite has previously been determined to be an important constituent in Taiwan watersheds underlain by black schists basement rocks of the eastern Central Range (Ho,

1975). Kao et al. (2004) identified the presence of pyrite in marine sedimentary rocks in

2- southwestern Taiwan. High concentrations of SO4 have been reported for both the

Danshuei and Liwu Rivers in northern Taiwan (Chu and You, 2007; Calmels et al., 2011) and concentrations up to nearly 8 times higher than global average have been reported for the Kaoping River in southern Taiwan (Chung et al., 2009). Das et al. (2012) calculated that ~85% of the sulfate yield in the Gaoping River is from pyrite oxidation. With this background, it is highly likely that pyrite is an important constituent of the weakly

metamorphosed slates that are a major portion of the bedrock in the upper Choshui River

2- watershed, and that nearly all dissolved SO4 is sourced from pyrite oxidation. Pyrite oxidation creates sulfuric acid in a 2:1 molar ratio of acid to pyrite following the simplified reaction described in Eq. 4.1.

- Eq. (4.1) 4FeS2 + 15 O2 +14 H2O  4Fe(OH)3 + 8 H2SO4

Each mole of H2SO4 is assumed to deprotonate fully at normal surface pH conditions since the pKa values for dissociation are well below typical pH at the surface

(pKa 1 = low (strong acid); pKa 2 = 1.92). Calculation of the relative contribution of

H2SO4 weathering to total weathering yields was estimated using the methodology of

Galy and France-Lanord (1999). This fraction of weathering is referred to as XSO4, and the calculation is as follows (Eq. 4.2).

[푆푂4]푅퐶 Eq. (4.2) 푋푆푂4 = [푆푂4]푇표푡+ [퐻퐶푂3]푇표푡

2- [SO4]RC refers to the rainfall and gypsum corrected values for measured SO4 .

Gypsum is assumed to be absent in the Choshui River watershed (Goldsmith, 2009).

2- [SO4]Tot refers to total measured SO4 from all inputs, and [HCO3]Tot is the total

- concentration of HCO3 calculated before any corrections. This method makes the assumption that H2SO4 sourced weathering of carbonates and silicates occurs at the same ratio as the weathering of these minerals from H2CO3 (CO2 sourced). This assumption is

+ likely valid since H is the weathering agent in both systems, with CO2 drawdown for the

+ H2CO3 system simply being a byproduct of the acid reaction. Regardless of source, H behaves in the same manner.

H2SO4 does not contribute to alkalinity through the weathering of silicate minerals since no carbon is added to the system. Therefore, by making an assumption of

- the ratio of dissolved silica to HCO3 created by CO2 mediated silicate weathering, an estimate of the total contribution of CO2 mediated silicate weathering to the HCO3 concentration can be made following Eq. 4.3, where [HCO3]Sil is the contribution of

- silicate weathering to the HCO3 concentration. (1-XSO4) represents the fraction of silicate weathering derived from H2CO3, and [Si] is the total rainfall adjusted dissolved silica

- concentration. This study assumes a ratio of 1:2 for the Si:HCO3 weathering ratio. This is to be consistent with CO2 consumption calculations from Goldsmith et al. (2008) who follow Edmund and Huh (1997). The 2X multiplier of Eq 4.3 comes from this assumption. The validity of this assumption is addressed in the following subsection.

- [HCO3]Sil is used in all calculations of source contributions to HCO3 , so this multiplier affects all estimates.

Eq. (4.3) [퐻퐶푂3]푆𝑖푙 = 2 × (1 − 푋푆푂4) × [푆𝑖]

- Since H2SO4 induced silicate weathering does not contribute HCO3 to the system,

- all remaining HCO3 after adjustment for [HCO3]Sil is assumed to be from carbonate

- - weathering. Both H2SO4 and H2CO3 contribute two HCO3 ions to the total HCO3 concentration per reaction with carbonate minerals following Eqs. 4.4 and 4.5. The XSO4 value for each sample should reflect the total fractional contribution to remaining HCO3

- from H2SO4 sourced weathering of carbonate material since all remaining HCO3 comes

- from carbonates, and both weathering pathways produce equal amounts of HCO3 per reaction. This calculation follows Eq. 4.6, and the related calculation of HCO3 contribution from H2CO3 sourced weathering is given in Eq. 4.7. [HCO3]H2SO4 represents

- total sulfuric acid contribution to HCO3 , [HCO3]RC is the rainfall corrected value for

- - total HCO3 concentration, and [HCO3]Carb (H2CO3) represents the total HCO3 contribution of carbonate weathering by H2CO3.

- 2+ 2- Eq. (4.4) H2SO4 + 2CaCO3  2HCO3 + 2Ca + SO4

- 2+ Eq. (4.5) H2CO3 + CaCO3  2HCO3 + Ca

Eq. (4.6) [퐻퐶푂3]퐻2푆푂4 = 푋푆푂4 × ([퐻퐶푂3]푅퐶 − [퐻퐶푂3]푆𝑖푙)

Eq. (4.7) [퐻퐶푂3]퐶푎푟푏 (퐻2퐶푂3) = ([퐻퐶푂3]푅퐶 − [퐻퐶푂3]푆𝑖푙 − [퐻퐶푂3]퐻2푆푂4)

Finally, the total HCO3 yield attributable directly to CO2 export from the

- atmosphere ([HCO3]CO2) can be calculated following Eq. 4.8. All silicate HCO3 is derived from CO2 consumption, and half of carbonate weathering from H2CO3 is derived from CO2 consumption, while the other half comes from the carbonate in the mineral itself.

Eq. (4.8) [퐻퐶푂3]퐶푂2 = [퐻퐶푂3]푆𝑖푙 + (0.5 × [퐻퐶푂3]푐푎푟푏(퐻2퐶푂3))

The relationships among XSO4, [HCO3]RC, [HCO3]CO2, [HCO3]H2SO4, [HCO3]Sil, and [HCO3]Carb (H2CO3) were used to calculate the percent contribution of H2SO4

weathering to total weathering, the percent contribution of H2SO4 weathering to the total bicarbonate yield, the percent contribution of silicate weathering to CO2 consumption, and the percent contribution of carbonate weathering to CO2 consumption. Percent contribution of H2SO4 to total weathering is calculated as (XSO4 x 100%). Percent H2SO4 contribution to HCO3 is (100% x ([HCO3]H2SO4 ÷[HCO3]RC)). Percent contribution of silicate weathering to CO2 consumption is (100% x [HCO3]sil ÷ [HCO3]CO2), and the percent contribution of carbonate weathering is (100% - silicate consumption percent).

H2SO4 Contribution to Weathering Yields

The positive charge of the Choshui River system is dominated by Ca2+ and Mg2+, and much of this likely comes from weathering of carbonate material. The ratio of the

2+ 2+ - combined charge input of Mg and Ca to that of HCO3 is used to provide an estimate of the maximum possible contribution of carbonate minerals to H2CO3 related weathering fluxes. This comparison is made in Figure 4.1. In the case of 100% carbonate

2+ 2+ contribution to weathering by H2CO3, the ratio would fall along a 1:1 line. Ca and Mg both contribute twice their concentration to the total positive charge, and weathering of carbonate minerals creates two bicarbonate ions, balancing the charge. However, all samples fall far into the Ca2+ and Mg2+ side of this line. This charge imbalance suggests that weathering by another acid is an important source of dissolved Ca2+ and Mg2+ into the system. For the Choshui River, this additional negative charge is likely coming from sulfate formed from pyrite oxidation (Eq. 4.1(A)). The movement of samples toward the

1:1 line during storm flow then gives an indication that the importance of weathering by sulfuric acid is diminished during storm events. The scatter around the generally negative

trend of the data may be associated with changes in the weathering fluxes of calcium poor silicate minerals. This would add a greater amount of HCO3- relative to Ca + Mg.

To confirm the importance of H2SO4 weathering in this river system, the

2- equivalency of SO4 input was added the previous figure to create Figure 4.1(B). This yields a much closer relationship between Ca2+ + Mg2+ and the anions associated with the weathering agents than before. This indicates that H2SO4 is a very important part of the

- 2- weathering regime. The slight excess of all samples toward the HCO3 + SO4 side of the

1:1 line suggests that a small amount of positive charge is missing. This is likely made up by Na+ and K+, and indicates that silicate weathering is also an important part of the weathering regime. This is because all Na+ and K+ is assumed to derive from silicate weathering. The trend associated with the linear relationship of the data is near a slope of

1, but moves closer to the 1:1 line during storm flow. This suggests a decreasing importance of Na+ and K+ during storm flow, likely reflecting a slight increase in the overall carbonate contribution to weathering fluxes. This matches the conclusions reached in Chapter 2 about changes in weathering source contributions, specifically that total carbonate contribution to the system increases as the weathering regime shifts from a signal with a carbonate poor, deep thermal water signature to one entirely dependent on surface weathering.

An initial approximation of the contribution of H2SO4 to total weathering was made using Figure 4.2. This figure compares the total cation concentration (TZ+) to the

2- + concentration of SO4 . Since H2SO4 releases twice the amount of H compared to H2CO3

at normal surface conditions (H2CO3 pKa 1 = 6.35; pKa 2 = 10.33), it is assumed that it contributes twice as much to the total cation flux per mole. Therefore a first order approximation of sulfuric acid contribution to weathering is half of the slope of the trend.

This amounts to ~40% of the total weathering contribution. The close relationship

+ 2- between TZ and SO4 initially suggests a stable fractional contribution of sulfuric acid to total weathering fluxes. However, an additional factor can affect changes in TZ+

2- compared to SO4 . This second factor is the ratio of silicate to carbonate weathering.

This is because H2SO4 would cause the release of two cations per reaction with carbonates, compared to one for most silicate minerals. These two effects are in opposite

+ 2- directions (increasing H2SO4 contribution = decreasing TZ :SO4 , while increasing

+ 2- carbonate contribution = increasing TZ :SO4 ). Since increased carbonate weathering is likely during storm flow based on the results of previous analyses (Chapter 3), this would suggest a decrease in the fractional contribution of weathering by sulfuric acid.

Additional calculations are needed to identify the likely weathering contributions for each weathering pathway, but 40% represents a simple first order approximation to be used to calculate CO2 consumption yields.

A separate estimate of the fractional contribution of pyrite oxidation to weathering yields was made following Galy and France-Lanord (1999) as described in

- the previous sub-section. After calculating the HCO3 inputs from silicates and carbonates for both H2SO4 and H2CO3 derived weathering using the XSO4 correction factor, the validity of this factor was tested. This was accomplished by a comparison of pre-adjusted

+ - + - + TZ versus HCO3 to post XSO4 adjusted TZ versus HCO3 (Figure 4.3). Adjusted TZ

+ values are simply the original TZ × XSO4. The reasoning behind this comparison is that successful correction for sulfuric acid sourced weathering should result in a close positive

+ - relationship between TZ and HCO3 . This is because all cation contribution should be coming from H2CO3 weathering after the correction. Figure 4.3(a) is the pre-correction

+ - baseline, and shows a negative correlation between TZ and HCO3 . This relationship is expected if sulfuric acid is variably contributing to total weathering yields. Following

XSO4 adjustment (Figure 4.3(b)), there is a close positive linear relationship between

+ 2 corrected TZ and H2CO3 related bicarbonate yields (R = 0.919). This indicates XSO4 is an accurate estimation of the H2SO4 fractional contribution to weathering within the

Choshui River watershed, and weathering yields from this pathway can be removed from

CO2 consumption calculations.

Figure 4.3(b) gives some additional insights into the weathering system in the watershed. The slope regression (0.699) suggests two possible weathering mechanics: 1)

Mica weathering is an important contributor to the weathering flux or 2) Cation exchange is occurring at a rapid pace in the soils during the entire sampling interval, creating a TZ+ higher than anticipated through weathering alone. The weathering of K+ rich mica minerals was touched upon in Chapter 3, but only in a general sense of a weathering regime switch to surface weathering dominance. To explain the slope relationship with

- mica weathering, it is important to note that the HCO3 concentration plotted includes all

- bicarbonate from CO2 induced carbonate mineral weathering, not just the HCO3 associated with CO2 export from the atmosphere. Chemical weathering equations for common non-mica silicate minerals typically show a release of a single cation per

- reaction, but they have a consumption of two CO2 to release two HCO3 . Carbonate

- weathering also releases two HCO3 per reaction, along with a single cation. This should

+ - cause the relationship of TZ :HCO3 to have a slope of 0.5. However, the weathering of complex mica minerals to simpler mica minerals typically release only one HCO3 per reaction, along with a cation. An example of this is the weathering of muscovite (or the chemically similar illite) to kaolinite or gibbsite. These reactions are as follows:

Eq. (4.9) Muscovite to Gibbsite:

+ - KAl3Si3O10(OH)2 + H2CO3 + 9H2O  3Al(OH)3 + K + 3H4SiO4 + HCO3

Eq. (4.10) Muscovite to Kaolinite:

+ 2KAl3Si3O10(OH)2 + 2H2CO3 + 3H2O  3Al2Si2O5(OH)3 + 2K +

- 2HCO3

The weathering of complex mica minerals would therefore result in a 1:1 line for

+ - TZ :HCO3 . Since the slope in Figure X(b) is between 0.5 and 1, mica weathering may be

- an important weathering pathway. If true, this would change the ratio of Si:HCO3 released during silicate weathering and make the earlier assumption of a 1:2 ratio false. These mica weathering pathways have different ratios for released dissolved silica compared to

CO2 consumption (see muscovite to kaolinite (Eq. 4.10) which has no dissolved silica release, and muscovite to gibbsite (Eq. 4.9) which has a 3:1 ratio). Additional study is needed to determine the exact weathering pathways of the mica minerals in the Choshui

River watershed in accordance with established mica stability series. This study does not

- have the information to make this determination, so 1:2 is used for the Si:HCO3 ratio as the assumption for all calculations. The second possibility to increase the slope of the

+ - TZ :HCO3 relationship is through cation exchange of a single higher charge cation

(Ca2+, Mg2+, and Sr2+) for two lower charge cations (Na+ and K+). However, cation exchange is not likely to be the major driver of this slope as total Na+ and K+ concentrations are far lower than Ca2+ and Mg2+ concentrations. This suggests exchange rates are fairly slow, and weathering dominates the cation signal.

With XSO4, a time-series of the percent contribution of H2SO4 to total weathering

- was created. HCO3 yields from carbonic acid weathering were then used to calculate the percent of contribution of carbonate minerals to total weathering and CO2 consumption

(Figure 4.4). The initial small increase in weathering from H2SO4 at the onset of

Typhoon Mindulle likely reflects the leading edge of soil water input being flushed into the system. The large rapid decrease of sulfuric acid sourced weathering products during the storm suggests carbonic acid becomes a much larger portion of the total acid available. This makes sense as rainfall is weakly acidic, and this acidity is primarily from dissolved CO2. The extreme rainfall yields of the storm would likely severely diminish the proportion of rainfall acidity associated with sulfuric acid from fossil fuel burning in southeast Asia also. Therefore, the larger the amount of rainfall, the greater the expected contribution from CO2 sourced weathering is. However, the contribution of sulfuric acid weathering has a minimum of nearly 40%, suggesting that active pyrite weathering is still occurring during the storm, and remains a major source of acidity. Following the storm,

H2SO4 sourced weathering slowly becomes more dominant as river stage returns to near base-flow levels. This form of weathering stabilizes near 70% of all weathering 81

contributions, and has a very large impact on calculated CO2 consumption rates unaddressed in the previous typhoon induced consumption study.

4.4.2 Carbonate Weathering Influence on CO2 Export

The contribution of carbonate weathering to the total weathering of the system increases during the onset of Typhoon Mindulle (Figure 4.4). This is consistent with the previously established mixing models from Chapter 3 that suggest a weathering regime change to near complete surface weathering domination of the system. A small additional increase in the carbonate contribution occurs during the highest discharge of the storm sampling period, consistent with a small increase in the importance of carbonate weathering of surface minerals across the storm. Following the storm, the gradual decrease in carbonate importance is likely caused by deep thermal waters becoming an important contributor to total weathering yields again. However, for the entire record, carbonate minerals are the dominant weathering source in the watershed. For all samples, carbonate weathering represents >65% of total weathering. This is consistent with previous analyses of weathering yields in mixed carbonate-silicate watersheds in southeast Asia and Taiwan (Goldsmith, 2009; Ryu et al., 2008)

Calculated CO2 consumption/export yields for silicate and carbonate minerals allowed for the calculation of the percent contribution of CO2 drawdown related to weathering of carbonate minerals. While discussions of long term carbon sequestration are typically limited to weathering silicate minerals, some evidence links carbonate weathering as a possible source of CO2 drawdown (Liu and Zhao, 2000; Liu et al., 2011).

The traditional view suggests CO2 consumption by carbonate weathering is compensated for through the rapid precipitation of carbonate in the ocean. This leads to a net

consumption change of zero over geologic timescales. However, this traditional view does not account for marine primary production uptake of surface derived dissolved inorganic carbon (DIC). Burial of organic matter is an important long-term sink for carbon (Burdige, 2007), and chemical weathering of continental minerals represent a substantial portion of total ocean DIC. Liu et al. (2011) estimates that carbonate contribution to CO2 consumption is underestimated by a factor of three since carbonate weathering controls the DIC of lakes and inland seas where primary production and burial rates are high, and may account for a significant portion of global consumption.

- Nearly all DIC in the ocean is in the form of HCO3 and the two primary sources are terrestrial weathering and direct CO2 flux from the atmosphere into the oceans. At least

- 20% of HCO3 flux to the ocean is from terrestrial weathering (Schlesinger and Andrews,

2000; Schlesinger, 1997). The majority of this terrestrial bicarbonate is from weathering of carbonate minerals (Meybeck, 1987; Gaillardet et al., 1999; Han and Liu, 2004).

Because of this, the bicarbonate flux associated with carbonate dissolution may provide an important source of CO2 consumption over geologic time, particularly in the near- shore environment. Typhoons increase nutrient loads in the near shore environment that have been linked to large increases in primary production (Lin et al., 2003; Tsuchiya et al., 2013). Similar to inland lakes, this production may largely use carbonate weathering sourced bicarbonate from the massive typhoon water outflows before it can re-precipitate.

Much of this organic matter would be sequestered rapidly since typhoons are common and their associated sediment fluxes are extremely high (Milliman and Kao, 2005;

Goldsmith et al., 2008). The importance of this consumption pathway is unknown on a global scale and may be minor, but at least some carbonate sourced DIC is likely to be

sequestered. This is ignored in the traditional view that carbonates do not contribute to

CO2 consumption. Because of this, carbonate sourced CO2 export rates should be considered.

The fraction of CO2 export related to carbonate weathering in the Choshui River watershed increases sharply by ~20 percentage points at the start of Typhoon Mindulle to contribute about three quarters of all CO2 export to the oceans. Following the storm, this contribution gradually decreases to less than half of the total. Since the variation in the total carbonate contribution to weathering is much smaller than the variation in carbon contribution to CO2 flux, small changes in silicate weathering rates likely have smaller effect on CO2 export than similar changes in carbonate weathering rates.

4.4.3 Dissolved Silica Flux

Every estimate of CO2 flux to the oceans in this study is directly or indirectly

-1 calculated from dissolved silica measurements. This flux (as g H4SiO4 s ) at the Renlun

Bridge sampling location is closely related to total discharge (Figure 4.5). Both dissolved silica and discharge change at the same rate, meaning total dissolved silica concentrations are consistent throughout the storm. This lack of dilution with increased precipitation indicates that the silicate weathering rate is largely dependent on rainfall intensity or water availability. This is especially evident during the Typhoon Mindulle storm samples.

Following the storm, dissolved silica fluxes are higher per unit discharge than during the storm, suggesting that the water availability relationship with silicate dissolution only exists after some threshold amount of water availability.

4.4.4 CO2 Consumption from Weathering Yields

Estimated daily average CO2 consumption/export yields for Typhoon Mindulle

-2 storm flow (as tons CO2 km ) range from ~46% lower to ~59% higher than those calculated in Goldsmith et al. (2008). These yields are given in Table 4.2. There are several likely reasons for this variability. The direct 2×[Si] daily estimate is nearly identical to the prior estimate for silicate consumption because it follows the exact same methodology. However, this study does not cover as much of the period of intense storm- flow as the prior study, so it is likely that silicate weathering yields are actually higher than estimated. Every calculated consumption estimate, aside from the direct 2×[Si] estimate, accounts for pyrite oxidation and its associate sulfuric acid weathering. This weathering would produce dissolved silica yields that range from ~40–70% of the total silica flux per sample, and would have a very large impact on the calculated CO2 consumption. This had not been accounted for in the prior study. Secondly, the

Goldsmith et al. (2008) study did storm sampling at a location much further downstream.

This is important since a major lithology change from the low-grade metamorphic slates and quartzites to immature unconsolidated marine siliciclastic sediments occurs downstream of this study’s sampling location (Selvaraj and Chen, 2006). The younger marine sediments may be composed of more weathering resistant material like clays that were not metamorphosed to more complex micas. Rainfall variability is likely to impact local weathering rates and weathering totals also. Of the four sub-watersheds analyzed in the physical hydrology chapter, the one representing the area upstream of this study’s sampling point had the lowest average rainfall for the storm event. However, the sampling location from Goldsmith et al. (2008) was downstream of all four sub-

watersheds. Since dissolved silica flux appears to be closely related to discharge, these extremely high discharge-per-area watersheds would likely consume CO2 at a higher rate than the area above Renlun Bridge. Finally, this study did not sample as long of an interval during the Typhoon Mindulle storm flow. Because of this, much of the peak- flow, and therefore rapid CO2 consumption, is missed. This likely reduces the average calculated consumption rate of the storm below true values.

The ratio of CO2 consumption/export rates during the storm to those prior to

Mindulle have a large degree of variability, and are ~22 to 61 times higher for storm flow. Even the most conservative estimate of CO2 consumption rate change suggests that a single typhoon event can flux as much CO2 from the atmosphere as several months of base-flow conditions. Since only the first 72 hours of storm flow were sampled, and more than half of the storm related discharge occurred following the sampling period, this estimate is likely extremely underestimated. These values are also underestimated because the highest intensity precipitation, and thus highest likely weathering rates, occurred after the storm sampling interval. No change in consumption ratio was calculated for the data from Goldsmith et al. (2008) since no pre-storm values are given.

A study by Goldsmith (2009) calculated average annual CO2 consumption (as

CO2 derived carbon flux) from wet and dry season flow for silicate and carbonate minerals of watersheds across Taiwan. Four locations within the Choshui River watershed were sampled, including an upstream location near this study’s Typhoon sampling location. The calculated daily silicate consumption rate for the upstream location in the study is 0.112 ton km-2 day-1. This is nearly 40% of the typhoon silicate

weathering consumption rate calculated here, which would suggest minimal importance of typhoon storm flow to annual CO2 consumption. This runs counter to the storm:base- flow consumption ratio of this study.

Two reasons likely account for this though. The first is that a different methodology based on cation flux was used for the consumption calculation. This method assumes Ca/Na and Mg/Na ratios for bulk sedimentary rock based on Lan et al. (2002) to calculate the relative contributions of silicate and carbonate minerals to the weathering fluxes. However, as suggested in Chapter 3, base-flow has a substantially different ion ratio chemistry than what would be expected from surface weathering because of the Na rich and Ca poor input from deep thermal water. This would artificially inflate low-flow silicate weathering estimates based on cation fluxes. Secondly, wet season samples were all collected on 1 July, 2004, the same day as the start of Typhoon Mindulle storm flow.

Rainfall in the upper watershed began early in the day, and discharge increased by over

25 times between the start and end of the day (20.5 m3s-1 to 520 m3s-1). Any samples collected this day would likely be elevated from the wet-season base-flow. A small change in total discharge would represent a large change in annual consumption estimates when applied as the “typical” condition for wet-season flow. The discharge used in

Goldsmith (2009) is 317 m3s-1, which would lead to a possible overestimate of wet- season base-flow contributions of ~15 times. The carbonate contribution from Goldsmith

(2009) may also be exaggerated for this same reason.

Daily adjusted silicate carbon consumption in the Choshui River watershed represents an outsized portion of the daily global silicate consumption during Typhoon

Mindulle. Gaillardet et al. (1999) estimated a global annual silicate weathering driven

-1 consumption of 0.140 Gt C yr . After adjusting this value to a daily average CO2 consumption, it was compared to the calculated XSO4 silicate weathering daily consumption from this study. This comparison suggests that Mindulle related weathering in the watershed would account for ~0.03% of all global consumption for the 72 hours

8 that were sampled. Similar comparison to more recent estimates (1.5–3.3 × 10 ton CO2 yr-1; Hilley and Porder, 2008) suggest that this study’s watershed may contribute ~0.05–

0.11% of global silicate weathering consumption during the 72 hour storm sampling period. This all comes from a watershed that represents ~0.001% of earth’s land surface.

Considering this relatively large possible contribution, and that this study only considers a small portion of a single small watershed during a short portion of a single storm, this suggests that the CO2 consumption from storm induced weathering on HSIs worldwide may be a significant contribution to global carbon consumption.

-1 CO2 flux rates from the atmosphere (in mol s ) calculated from the XSO4 adjustments are likely to be the most accurate values in this study. The flux rate for the

XSO4 adjusted [Si] calculations before, during, and following Typhoon Mindulle and the total flux from the atmosphere for all weathering, is shown in Figure 4.6. The rate for silicate consumption of CO2 increases from ~2.5 mol/s to ~369 mol/s, an increase of over

140 fold. However, total export rate has a much more dramatic increase from ~4.7 mol/s to 1204 mol/s, an increase of over 250 fold. This reflects the importance of carbonates in

CO2 export to the ocean. Even with the highly conservative estimate of zero net drawdown from carbonate weathering, the high change in consumption rates for silicate

minerals still suggest extreme weather plays an outsized and possibly dominant role in carbon consumption from the watershed annually.

4.5 Conclusions

The methods to determine the fraction of weathering attributable to pyrite oxidation (Galy and France-Lanord, 1999) are affective within the Choshui River watershed. Pyrite oxidation contributes significantly to total carbonate and silicate weathering and is much more pronounced during base-flow conditions, accounting for

~70% of total weathering. However, this decreases to ~40% during the highest storm flow. Furthermore, after accounting for sulfuric acid sourced weathering, the percent contribution of carbonate and silicate minerals to total weathering and carbon uptake from the atmosphere can be calculated from dissolved silica and bicarbonate data.

Carbonate weathering is the primary weathering mechanism in the watershed and accounts for a minimum of 65% of total weathering yields. This fraction increases with rainfall and discharge. Silicate weathering accounted for more than half of CO2 export from the atmosphere during base-flow conditions, but carbonate weathering was responsible for nearly 70% of CO2 export during the Typhoon Mindulle storm event.

Storm related CO2 consumption/export rates increased by more than two orders of magnitude for both silicate and total weathering compared to pre-storm conditions. These

- increased rates may be controlled by rainfall intensity. The total export of HCO3 increases at a rate higher than proportional to discharge as discharge increases. Silicate consumption increases more linearly with discharge. This implies that increasing precipitation on a short time scale may affect weathering rates more than spreading the same water amount over a longer period, particularly for carbonate minerals. Total CO2

consumption yields are similar to previous studies on typhoon induced weathering, but are much higher than normal consumption rates for other high standing island watersheds previously reported (Lyons et al., 2005; Jacobson and Blum, 2003). Finally, daily CO2 consumption totals during Typhoon Mindulle indicate that extreme weather induced weathering of HSIs may be an important, but largely overlooked, contributor to global carbon consumption.

Table 4.1. Contributions of Pyrite Oxidation, Carbonate Weathering, and Silicate Weathering to Total Weathering Yields for Each Sample % % [HCO3] [HCO3] [HCO3] [HCO3] [HCO3] % Sil % Sil % Carb Date & Time XSO4 TZ+ H2SO4 Carb H2SO4 H2CO3 sil carb CO2(carb) Total (CO2) (CO2) Total Total 6/28/04 11:20 0.69 2.49 0.48 0.24 0.13 0.22 0.11 68.54 26.538 73.46 54.91 45.09 6/30/04 9:48 0.69 2.49 0.47 0.23 0.13 0.21 0.10 69.25 26.633 73.37 55.13 44.87 7/1/04 6:05 0.71 2.40 0.44 0.20 0.11 0.18 0.09 71.00 25.880 74.12 54.42 45.58 7/1/04 12:03 0.72 2.46 0.47 0.18 0.09 0.18 0.09 72.11 21.583 78.42 48.65 51.35 7/1/04 15:00 0.74 2.19 0.40 0.14 0.06 0.14 0.07 73.66 20.386 79.61 47.07 52.93 7/1/04 18:00 0.61 1.90 0.46 0.23 0.09 0.29 0.15 61.20 16.067 83.93 38.16 61.84 7/1/04 21:04 0.54 1.86 0.49 0.32 0.10 0.42 0.21 53.81 13.805 86.20 33.01 66.99 7/1/04 23:54 0.55 1.72 0.44 0.29 0.10 0.37 0.18 54.67 15.265 84.74 35.79 64.21

7/2/04 0:00 0.54 1.70 0.44 0.29 0.11 0.37 0.19 54.18 15.523 84.48 36.17 63.83 7/2/04 2:57 0.57 1.97 0.50 0.29 0.10 0.37 0.19 57.47 14.708 85.29 35.20 64.80 7/2/04 5:57 0.55 1.74 0.45 0.29 0.11 0.37 0.19 54.79 15.804 84.20 36.75 63.25 7/2/04 8:59 0.54 1.92 0.50 0.32 0.11 0.42 0.21 54.48 14.666 85.33 34.68 65.32 7/2/04 11:57 0.51 1.81 0.49 0.35 0.12 0.46 0.23 51.34 14.502 85.50 33.92 66.08 7/2/04 15:20 0.57 1.74 0.44 0.28 0.11 0.34 0.17 56.56 16.481 83.52 38.19 61.81 7/2/04 18:15 0.52 1.79 0.48 0.34 0.12 0.45 0.22 51.67 14.669 85.33 34.27 65.73 7/2/04 21:00 0.54 1.76 0.46 0.31 0.11 0.40 0.20 53.76 15.697 84.30 36.41 63.59 7/3/04 0:00 0.53 1.76 0.46 0.32 0.12 0.40 0.20 53.27 16.193 83.81 37.20 62.80 7/3/04 3:00 0.51 1.84 0.49 0.37 0.13 0.48 0.24 50.51 15.133 84.87 34.93 65.07 7/3/04 6:00 0.54 1.75 0.45 0.31 0.12 0.38 0.19 54.03 16.368 83.63 37.61 62.39 7/3/04 8:58 0.54 1.87 0.49 0.32 0.11 0.42 0.21 53.80 15.020 84.98 35.22 64.78 7/3/04 12:00 0.53 1.88 0.49 0.33 0.11 0.43 0.21 53.48 14.797 85.20 34.77 65.23 7/3/04 15:11 0.48 1.75 0.46 0.39 0.14 0.51 0.25 47.55 15.359 84.64 34.87 65.13 7/3/04 17:55 0.40 1.74 0.46 0.51 0.16 0.70 0.35 39.67 13.657 86.34 30.64 69.36 7/3/04 21:05 0.40 1.72 0.46 0.50 0.16 0.68 0.34 40.22 14.016 85.98 31.37 68.63

Table 4.1 (Continued). Contributions of Pyrite Oxidation, Carbonate Weathering, and Silicate Weathering to Total Weathering Yields % % [HCO3] [HCO3] [HCO3] [HCO3] [HCO3] % Sil % Sil % Carb Date XSO4 TZ+ H2SO4 Carb H2SO4 H2CO3 sil carb CO2(carb) Total CO2 (CO2) Total Total 7/4/04 3:00 0.44 1.74 0.46 0.44 0.15 0.58 0.29 44.01 14.800 85.20 33.35 66.65 7/4/04 6:00 0.42 1.67 0.44 0.45 0.15 0.60 0.30 42.16 14.946 85.05 33.32 66.68 7/5/04 11:05 0.60 2.22 0.53 0.33 0.15 0.36 0.18 59.61 20.323 79.68 44.88 55.12 7/8/04 14:18 0.59 2.20 0.52 0.34 0.16 0.36 0.18 59.17 21.468 78.53 46.53 53.47 7/9/04 12:53 0.52 1.92 0.47 0.41 0.20 0.43 0.21 52.43 23.342 76.66 48.14 51.86 7/9/04 17:38 0.58 2.14 0.51 0.35 0.16 0.38 0.19 57.55 20.956 79.04 45.52 54.48 7/12/04 9:40 0.45 1.77 0.42 0.51 0.26 0.50 0.25 45.44 26.157 73.84 50.75 49.25 7/14/04 8:50 0.64 2.12 0.46 0.27 0.14 0.26 0.13 64.06 25.009 74.99 52.25 47.75

92 7/16/04 8:55 0.63 2.15 0.47 0.28 0.14 0.27 0.13 63.48 24.618 75.38 51.64 48.36 7/19/04 8:50 0.61 2.29 0.53 0.33 0.16 0.34 0.17 61.14 22.498 77.50 48.34 51.66 7/21/04 8:50 0.70 2.30 0.43 0.22 0.13 0.18 0.09 70.07 28.749 71.25 57.85 42.15 7/23/04 8:50 0.67 2.25 0.45 0.25 0.14 0.22 0.11 67.30 27.559 72.44 56.00 44.00 7/26/04 9:05 0.77 2.40 0.36 0.15 0.09 0.11 0.05 77.27 33.319 66.68 63.92 36.08 7/28/04 9:15 0.72 2.43 0.44 0.20 0.12 0.17 0.09 72.12 28.376 71.62 57.70 42.30 7/30/04 9:10 0.75 2.33 0.38 0.16 0.10 0.13 0.06 75.12 31.192 68.81 61.36 38.64 8/2/04 10:15 0.71 2.37 0.44 0.21 0.12 0.18 0.09 71.18 27.915 72.08 57.00 43.00 8/4/04 10:53 0.76 2.41 0.39 0.16 0.10 0.12 0.06 75.68 31.796 68.20 62.09 37.91 8/6/04 12:41 0.68 2.47 0.51 0.26 0.13 0.24 0.12 67.55 24.916 75.08 52.65 47.35 8/9/04 12:51 0.76 2.42 0.39 0.16 0.10 0.12 0.06 75.96 31.730 68.27 62.06 37.94 - -Columns beginning with [HCO3] indicate the total concentration of HCO3 (mM) contributed through weathering of what is listed below it. H2SO4 = weathering by sulfuric acid H2CO3 = weathering by carbonic acid Sil = silicate weathering Carb = carbonate weathering CO2(carb) = HCO3 from carbonate weathering associated with CO2 drawdown For percentages, Total indicates contribution to total weathering and (CO2) indicate contribution to CO2 consumption.

-2 Table 4.2. CO2 Consumption Estimates (Tons km ) Pre-Storm Storm Annual Storm To Pre- Storm Method Pre-Storm Daily Flow Daily Daily Storm Flow Average Average Average Consumption Ratio 2x[Si] 0.068 1.633 0.024 0.545 – 22.29 40% of 2x[Si] 0.041 0.980 0.014 0.327 – 22.29

93 XSO4 Adjusted [Si] 0.021 0.845 0.008 0.282 – 37.45

Total Consumption 0.049 2.517 0.014 0.837 – 61.06 Goldsmith et al. (2008) – 2.100 – 0.525 – – Goldsmith (2009) – – – – 0.112 – Silicate Goldsmith (2009) – – – – 0.590 – Carbonate -Total consumption is adjusted for XSO4, but includes carbonate consumption of CO2.

2+ 2+ - Figure 4.1. (a) Comparison of charge equivalence of Ca + Mg versus HCO3 in meq/L, and (b) 2+ 2+ - 2- Comparison of charge equivalence of Ca + Mg versus HCO3 + SO4 in meq/L. The 1:1 line is drawn in blue.

Figure 4.2 Plot of total cation concentration (TZ+) versus sulfate concentration. Two lines are drawn on the plot indicating a 2:1 ratio and a 1:1 ratio.

Figure 4.3. Plots of (a) Total cation concentration (TZ+) versus total bicarbonate concentration before adjustment for XSO4, and (b) post adjustment concentration of TZ+ versus bicarbonate. + These values indicate TZ and HCO3 from weathering by H2CO3.

Figure 4.4. Time-series of the percent contribution of sulfuric acid to total weathering (Green), carbonate weathering to total weathering (Blue), and carbonate weathering to CO2 consumption (Red). Discharge is plotted at top (Black).

-1 Figure 4.5. Silica flux time-series (as g H4SiO4 s ) compared to changes in discharge and precipitation.

Figure 4.6. CO2 consumption rate time-series for XSO4 adjusted silicate weathering (Red) and Total weathering (Blue). The yellow bar indicates the time period associated with the “storm samples”.

Final Conclusions

Typhoon Mindulle dramatically affected both the physical hydrology and chemical weathering regimes of the Choshui River watershed. Extreme storm event effects on High Standing Islands and their weathering yields may play an important, but largely ignored, role in the global carbon budget. Additional studies need to be done on other HSIs that frequently experience tropical cyclones in Oceana, the Caribbean, and

Pacific to refine our understanding of their role in atmospheric carbon uptake.

Typical rainfall totals for the five days of Typhoon Mindulle related precipitation within the watershed were greater than 70 cm, and locally exceeded 1m. Calculated runoff ratios in sub-watersheds of varying slope and relief had a range of 0.52–0.87 using

Thiessen Polygon weighted rainfall. Unlike typically accepted drivers of runoff, slope steepness appears to have only a marginal effect, if any, on runoff ratio during extreme storm events. Rainfall intensity and totals are the primary drivers of increased runoff ratios during Typhoon Mindulle.

Geochemical analyses allowed for the determination of stream solute weathering sources. Sources of chemical weathering solutes were identified as surface silicate minerals, disseminated carbonate minerals at the surface, and deep thermal weathering of silicate material. Disseminated carbonates may make up a substantially greater percentage of the surface lithology than previously identified. Deep thermal water is an 100

important source of solutes, particularly Na+, during base-flow conditions. Molar ratios of major cations indicate that during Typhoon Mindulle, the weathering regime shifts to a surface weathering controlled system. Strontium isotope ratios suggest that initial rainfall causes release of soil pore waters. This creates a stronger signal of silicate weathering input. However, over the course of typhoon related rainfall, carbonate weathering gradually increases its relative contribution to the total weathering input. Original soil water is depleted during the storm, reducing silicate contribution. Rapid weathering of more easily dissolvable carbonate minerals further contributes to the shift toward more carbonate contribution. Major ion data also suggest that the weathering of mica minerals may be an important contribution to the overall surface silicate weathering load.

However, additional study is needed to determine what fraction of total silicate weathering the mica minerals represent.

Estimates of the fractional contributions of sulfuric acid induced weathering and

CO2 induced weathering to total weathering yields were calculated from dissolved silica, sulfate, and bicarbonate concentrations throughout the storm. Estimates of the relative and total contributions of carbonate weathering to CO2 flux from the atmosphere were also calculated. Pyrite oxidation related sulfuric acid weathering contributions ranged

40%–77%, reaching the minimum during peak discharge. This percentage is highest during post-storm base-flow conditions. Carbonate minerals represent the majority of total weathering in the Choshui watershed (66%–86%), and reach the maximum contribution during the highest discharge. This dominance of minor amounts of disseminated carbonate on the weathering fluxes is similar to previous findings in other

101

actively uplifting terrains like the New Zealand Southern Alps and the Himalaya orogen

(Jacobson et al., 2002; Jacobson et al., 2003). Carbonate minerals represent the majority of CO2 flux from the atmosphere during storm flow, while silicate minerals consume the majority during base-flow conditions. Carbonate export represents 36%–69% of the CO2 flux, peaking during the highest discharge. Total silicate consumption of CO2 for the first

72 hours of Typhoon Mindulle related rainfall is estimated at 0.45 ton km-2, while total flux including carbonates is estimated as 1.33 ton km-2. After normalizing to daily average weathering over the 72 hours, both estimates are among the highest denudation rates recorded globally. Both values are also likely heavily underestimated since the majority of storm related discharge occurred after the 72 hour sampling period. CO2 consumption/export rates increased by over 140 times for silicate weathering and 250 times for total weathering between pre-storm and peak flow.

Overall, this study indicates that the weathering environment during extreme weather is much different than wet or dry season base-flow conditions, at least on High

Standing Island terrains. A single typhoon may contribute the equivalent of months of base-flow weathering to the weathering yields and carbon consumption of a watershed.

Since many High Standing Islands are commonly affected by typhoons (Taiwan, Japan,

Oceana) and hurricanes (Dominica, Lesser Antilles, and other Caribbean islands), the total worldwide weathering impact of these storm on the global carbon budget may be important and needs to be studied further. Further study is particularly needed because of the well-established research indicating that these islands already represent a much greater contribution of chemical weathering relative to their size than any other areas of

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the globe. Since so few studies of chemical weathering have incorporated severe storm fluxes into their models, an important contribution to the global carbon budget may be missing.

103

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Appendix A: Sub-Watershed Slope Maps

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Appendix B: Discharge and Precipitation Measurements

Gauge Name 1510H075 1510H050 1510H049 1510H024 1510H071 Sub- Watershed Extreme High Medium Low Primary Date and Time Discharge (m^3/s) 6/28/04 0:00 15.2 6/28/04 12:00 29.8 6/29/04 0:00 14.4 6/29/04 12:00 14.8 6/30/04 0:00 17.6 6/30/04 12:00 20 7/1/04 0:00 20.5 7/1/04 1:00 22.5 1.5 48 3 11 7/1/04 2:00 24 1.5 48 4 11 7/1/04 3:00 23.5 1.5 48 4 11 7/1/04 4:00 24.5 1.5 49 4 11 7/1/04 5:00 26.8 1.5 50 4 11 7/1/04 6:00 27.4 1.5 50 4 11 7/1/04 7:00 33.4 1.5 50 4 11 7/1/04 8:00 39.1 1.5 46 4 12 7/1/04 9:00 62 1.5 36 4 11 7/1/04 10:00 66 1.5 35 4 11 7/1/04 11:00 68 1.5 34 4 11 7/1/04 12:00 81 1.5 34 4 10 7/1/04 13:00 94.4 1.5 32 4 8 7/1/04 14:00 121.8 1.5 33 4 7 7/1/04 15:00 127.2 1.5 35 3 7 7/1/04 16:00 138 1.5 36 4 9 7/1/04 17:00 173.4 1.5 37 4 12 7/1/04 18:00 209 1.5 45 3 13 7/1/04 19:00 272 1.5 42 4 13 7/1/04 20:00 406 1.5 39 4 38 7/1/04 21:00 338 1.5 41 3 175 7/1/04 22:00 420 1.5 40 4 217 7/1/04 23:00 496 1.5 38 4 253 7/2/04 0:00 520 1.5 40 4 327

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Gauge Name 1510H075 1510H050 1510H049 1510H024 1510H071 Sub- Watershed Extreme High Medium Low Primary Date and Time Discharge (m^3/s) 7/2/04 1:00 706.67 1.5 47 3 347 7/2/04 2:00 834.33 1.5 48 3 388 7/2/04 3:00 757 1.5 48 3 406 7/2/04 4:00 686 1.5 49 4 435 7/2/04 5:00 598 1.5 49 5 452 7/2/04 6:00 580 1.5 50 4 488 7/2/04 7:00 586 1.5 50 4 550 7/2/04 8:00 628 1.5 50 4 588 7/2/04 9:00 464 2 49 5 581 7/2/04 10:00 472.83 3 50 18 584 7/2/04 11:00 517 13 54 121 576 7/2/04 12:00 516 52 63 188 559 7/2/04 13:00 557 79 121 315 576 7/2/04 14:00 470.83 69 140 411 616 7/2/04 15:00 869 73 145 478 729 7/2/04 16:00 887.33 102 142 505 876 7/2/04 17:00 730 148 252 545 1116 7/2/04 18:00 666 126 190 553 1445 7/2/04 19:00 661 82 112 568 1936 7/2/04 20:00 687 62 103 583 2035 7/2/04 21:00 775.33 50 73 565 1948 7/2/04 22:00 814.67 52 106 571 1864 7/2/04 23:00 754 59 113 575 1618 7/3/04 0:00 698 73 108 582 1526 7/3/04 1:00 700 118 120 585 1571 7/3/04 2:00 663 101 123 598 1706 7/3/04 3:00 614 147 134 623 1440 7/3/04 4:00 574 207 210 623 1239 7/3/04 5:00 598 201 328 634 1524 7/3/04 6:00 718 212 264 642 2925 7/3/04 7:00 810 318 318 650 4230 7/3/04 8:00 857.33 286 316 752 2367 7/3/04 9:00 900 251 331 776 5758 7/3/04 10:00 1096 162 307 782 3853 7/3/04 11:00 1347 140 338 797 3314 7/3/04 12:00 1681.67 81 334 980 4473

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Gauge Name 1510H075 1510H050 1510H049 1510H024 1510H071 Sub- Watershed Extreme High Medium Low Primary Date and Time Discharge (m^3/s) 7/3/04 13:00 1941 64 329 1387 3641 7/3/04 14:00 1962.5 64 362 744 3298 7/3/04 15:00 2103.67 47 255 686 3078 7/3/04 16:00 2222.83 25 187 689 2890 7/3/04 17:00 2482.67 19 179 522 2710 7/3/04 18:00 2373.67 16 157 517 2535 7/3/04 19:00 1909.83 14 152 648 2585 7/3/04 20:00 2140.5 27 176 933 2647 7/3/04 21:00 2339.83 24 187 1026 2703 7/3/04 22:00 1966.67 16 179 610 2803 7/3/04 23:00 1850 12 173 495 2700 7/4/04 0:00 1580 11 175 500 2628 7/4/04 1:00 1551.67 9 175 505 2540 7/4/04 2:00 1812.5 7 163 500 2598 7/4/04 3:00 1733.33 6 164 495 2300 7/4/04 4:00 1511.67 6 164 495 1957 7/4/04 5:00 1516.67 10 164 520 1976 7/4/04 6:00 1690 45 207 662 1927 7/4/04 7:00 1788.33 52 484 722 2251 7/4/04 8:00 2005.5 51 477 746 3167 7/4/04 9:00 2088.83 171 1030 838 3153 7/4/04 10:00 2385.67 558 1024 1074 2970 7/4/04 11:00 2420 193 1180 1445 5298 7/4/04 12:00 2322.5 74 1520 1292 7569 7/4/04 13:00 2114.5 117 1000 1106 6583 7/4/04 14:00 2216.33 137 844 986 6388 7/4/04 15:00 2082 125 700 865 4158 7/4/04 16:00 2149.17 120 784 856 3403 7/4/04 17:00 2118.83 114 712 723 2700 7/4/04 18:00 2053.83 108 555 734 2357 7/4/04 19:00 1931.33 110 596 650 2382 7/4/04 20:00 1681.67 102 625 661 2333 7/4/04 21:00 1590 85 675 740 2406 7/4/04 22:00 1745.83 82 640 820 3439 7/4/04 23:00 2056.5 79 573 936 2631 7/5/04 0:00 2283.5 71 564 659 2770

119

Gauge Name 1510H075 1510H050 1510H049 1510H024 1510H071 Sub- Watershed Extreme High Medium Low Primary Date and Time Discharge (m^3/s) 7/5/04 1:00 2183.83 63 582 622 3022 7/5/04 2:00 2596.33 30 492 647 2615 7/5/04 3:00 2239.17 57 510 567 2686 7/5/04 4:00 1895 55 457 416 2810 7/5/04 5:00 2035.83 56 401 389 2900 7/5/04 6:00 1759.17 73 376 483 3117 7/5/04 7:00 1794.17 76 366 407 3140 7/5/04 8:00 1770 61 369 384 3239 7/5/04 9:00 1696.67 56 328 428 2827 7/5/04 10:00 1889.17 52 310 371 2414 7/5/04 11:00 1764.17 51 307 309 2860 7/5/04 12:00 1380 49 295 365 1988 7/5/04 13:00 1178.67 46 388 253 1667 7/5/04 14:00 1303.33 45 275 254 1519 7/5/04 15:00 1251.33 56 283 255 1474 7/5/04 16:00 1251.33 116 270 327 1640 7/5/04 17:00 1259.33 163 253 395 1711 7/5/04 18:00 1318.33 136 273 304 1880 7/5/04 19:00 1388.33 101 283 281 1818 7/5/04 20:00 1332.33 80 290 260 1635 7/5/04 21:00 1169.33 68 280 248 1504 7/5/04 22:00 1127.33 62 275 272 1437 7/5/04 23:00 1000.67 60 278 272 1835 7/6/04 0:00 986.67 53 278 266 1257 7/6/04 1:00 824 51 265 233 1227 7/6/04 2:00 782 51 253 225 1201 7/6/04 3:00 718 50 240 210 1089 7/6/04 4:00 720 49 225 195 960 7/6/04 5:00 710 48 198 175 1018 7/6/04 6:00 677.33 46 215 160 974 7/6/04 7:00 657.33 45 200 151 984 7/6/04 8:00 658 43 198 150 1000 7/6/04 9:00 642.67 42 193 149 948 7/6/04 10:00 601.33 40 190 154 877 7/6/04 11:00 620 38 178 160 788 7/6/04 12:00 582.67 38 175 160 903

120

Gauge Name 1510H075 1510H050 1510H049 1510H024 1510H071 Sub- Watershed Extreme High Medium Low Primary Date and Time Discharge (m^3/s) 7/6/04 13:00 556 36 180 158 861 7/6/04 14:00 525.33 35 183 156 760 7/6/04 15:00 522.67 33 166 151 745 7/6/04 16:00 542.67 31 164 144 705 7/6/04 17:00 518.67 30 156 135 647 7/6/04 18:00 510.67 30 149 120 755 7/6/04 19:00 496 29 147 119 880 7/6/04 20:00 466.67 29 138 109 815 7/6/04 21:00 458.67 28 140 107 733 7/6/04 22:00 401.83 27 140 104 753 7/6/04 23:00 392 27 126 101 773 7/7/04 0:00 407 26 131 100 782 7/7/04 3:00 412 25 122 96 750 7/7/04 6:00 405 25 129 96 750 7/7/04 9:00 383 22 127 84 670 7/7/04 12:00 337 21 124 78 720 7/7/04 15:00 321 20 122 75 720 7/7/04 18:00 332 18 126 70 720 7/7/04 21:00 347 19 140 68 750 7/8/04 0:00 358 18 126 65 770 7/8/04 3:00 385 17 116 64 690 7/8/04 6:00 413 16 127 62 680 7/8/04 9:00 406 16 124 62 600 7/8/04 12:00 436 16 127 57 610 7/8/04 15:00 332 14 127 52 610 7/8/04 18:00 364 15 127 51 456 7/8/04 21:00 290 52 133 144 392 7/9/04 0:00 444 37 120 90 424 7/9/04 3:00 399 28 116 74 480 7/9/04 6:00 350 25 131 67 480 7/9/04 9:00 350 22 140 63 376 7/9/04 12:00 350 20 143 56 353 7/9/04 15:00 270 17 116 51 432 7/9/04 18:00 270 17 113 49 464 7/9/04 21:00 250 17 98 48 504 7/10/04 0:00 280 16 94 46 528

121

Gauge Name 1510H075 1510H050 1510H049 1510H024 1510H071 Sub- Watershed Extreme High Medium Low Primary Date and Time Discharge (m^3/s) 7/10/04 3:00 265 16 94 48 512 7/10/04 6:00 260 15 87 45 456 7/10/04 9:00 280 15 81 42 432 7/10/04 12:00 236 15 76 39 368 7/10/04 15:00 228 14 71 37 346 7/10/04 18:00 200 14 100 43 339 7/10/04 21:00 212 17 87 61 353 7/11/04 0:00 208 14 78 45 408 7/11/04 3:00 204 14 70 43 392 7/11/04 6:00 216 14 73 40 408 7/11/04 9:00 220 14 63 40 416 7/11/04 12:00 245 14 60 37 424 7/11/04 15:00 192 12 63 32 440 7/11/04 18:00 196 12 68 30 392 7/11/04 21:00 196 12 68 30 368 7/12/04 0:00 212 12 70 30 424

122

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 SUM (mm) 805 910 851 883 714 483 545 714 735 1033 669

Date/Time Hourly Precipitation (mm) 7/1/04 1:00 0 0 0 0 0 0 0 0 0 0 0 7/1/04 2:00 0 0 0 0 0 1 0 0 0 0 0 7/1/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/1/04 4:00 0 0 0 0 0 0 0 0 0 0 0 7/1/04 5:00 0 0 0 0 0 0 0 0 0 0 0 7/1/04 6:00 0 0 0 0 0 1 0 0 0 0 0 7/1/04 7:00 0 0 0 1 0 1 0 0 0 0 0 7/1/04 8:00 0 0 1 0 0 3 0 0 0 0 0

12 7/1/04 9:00 0 0 1 1 0 2 0 0 0 0 0

7/1/04 10:00 0 0 0 2 0 2 0 0 0 0 0 7/1/04 11:00 0 0 0 3 0 1 0 0 0 0 0 7/1/04 12:00 0 0 1 0 0 1 0 0 0 0 0 7/1/04 13:00 0 0 1 0 0 4 0 0 0 0 0 7/1/04 14:00 0 0 0 2 0 2 0 0 0 0 0 7/1/04 15:00 0 0 0 0 0 1 0 0 0 0 0 7/1/04 16:00 5 0 3 2 0 2 0 0 0 0 0 7/1/04 17:00 2 0 0 0 0 6 0 0 0 0 0 7/1/04 18:00 2 0 5 2 0 5 0 0 0 0 0 7/1/04 19:00 1 0 2 1 0 5 0 0 0 0 0 7/1/04 20:00 1 0 3 4 0 3 0 0 0 0 0 7/1/04 21:00 3 0 5 3 0 10 0 0 0 0 0 7/1/04 22:00 5 0 7 6 2 9 0 1 0 0 0 7/1/04 23:00 6 0 6 9 1 13 0 1 0 0 0 7/2/04 0:00 0 1 1 10 1 5 0 0 0 0 0 123

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/2/04 1:00 2 0 2 17 0 4 0 0 0 0 0 7/2/04 2:00 1 0 2 3 1 2 0 0 0 0 0 7/2/04 3:00 0 1 1 10 0 0 0 1 0 0 0 7/2/04 4:00 0 0 0 5 0 0 0 0 0 0 0 7/2/04 5:00 0 0 1 1 0 1 0 0 0 0 0 7/2/04 6:00 0 0 1 1 0 0 0 0 0 0 0 7/2/04 7:00 0 0 0 1 0 1 3 0 0 0 0 7/2/04 8:00 3 3 3 0 3 0 4 1 16 16 9 7/2/04 9:00 9 28 13 4 21 4 5 20 28 28 22

12 7/2/04 10:00 19 30 17 18 19 12 9 13 25 25 21

4 7/2/04 11:00 15 23 17 20 18 10 5 13 24 24 10

7/2/04 12:00 10 23 14 14 15 10 2 14 22 22 9 7/2/04 13:00 8 6 8 10 7 8 18 8 8 8 12 7/2/04 14:00 7 6 11 12 4 3 37 8 12 12 12 7/2/04 15:00 18 17 14 9 13 10 18 12 36 36 24 7/2/04 16:00 11 11 9 13 16 7 22 13 28 27 30 7/2/04 17:00 13 6 14 7 6 5 14 7 8 8 8 7/2/04 18:00 9 4 5 6 4 7 19 6 11 11 5 7/2/04 19:00 2 1 3 5 2 3 1 1 3 3 1 7/2/04 20:00 11 5 9 4 4 1 0 10 8 8 0 7/2/04 21:00 10 10 4 6 9 7 5 8 8 8 0 7/2/04 22:00 5 6 5 7 5 4 56 4 16 17 7 7/2/04 23:00 7 13 8 3 10 5 30 8 14 14 6 7/3/04 0:00 6 5 6 10 6 4 17 7 2 2 2 7/3/04 1:00 2 4 5 5 4 2 62 3 22 22 17 124

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/3/04 2:00 8 16 11 6 13 5 22 17 45 45 26 7/3/04 3:00 35 18 26 9 21 14 2 39 39 39 11 7/3/04 4:00 26 18 34 12 20 21 0 29 34 34 4 7/3/04 5:00 15 13 18 8 12 13 7 16 18 18 5 7/3/04 6:00 22 15 11 6 4 3 27 16 37 37 27 7/3/04 7:00 31 30 26 10 0 12 60 14 32 31 8 7/3/04 8:00 29 23 28 18 51 14 3 24 19 22 25 7/3/04 9:00 5 9 13 32 12 10 1 7 6 6 7 7/3/04 10:00 16 2 18 27 1 10 0 10 3 3 3

12 7/3/04 11:00 13 1 9 55 3 1 0 2 1 1 1

5 7/3/04 12:00 2 0 4 35 0 1 0 0 0 1 0

7/3/04 13:00 2 0 5 25 2 0 0 1 2 1 0 7/3/04 14:00 1 1 3 19 1 0 2 1 1 1 1 7/3/04 15:00 1 0 1 18 0 1 6 0 1 1 3 7/3/04 16:00 0 1 0 13 1 0 1 0 0 1 3 7/3/04 17:00 1 2 1 8 2 1 1 0 1 1 1 7/3/04 18:00 0 3 1 10 2 0 0 3 29 28 1 7/3/04 19:00 12 3 14 13 4 1 0 13 4 4 0 7/3/04 20:00 4 1 3 4 1 1 0 1 0 0 0 7/3/04 21:00 1 0 1 1 0 0 2 0 0 0 0 7/3/04 22:00 1 2 2 6 2 0 0 4 2 2 0 7/3/04 23:00 1 0 0 3 0 1 1 1 0 0 0 7/4/04 0:00 0 0 0 9 0 0 1 0 0 0 0 7/4/04 1:00 0 0 0 3 0 0 0 0 0 0 2 7/4/04 2:00 0 0 0 2 0 0 2 0 9 7 0 125

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/4/04 3:00 1 1 2 0 0 0 0 2 16 16 4 7/4/04 4:00 9 9 12 0 5 1 3 16 19 29 6 7/4/04 5:00 29 40 26 0 40 3 0 38 1 10 20 7/4/04 6:00 41 18 15 1 30 17 0 7 5 29 7 7/4/04 7:00 42 51 64 3 36 21 0 41 12 36 13 7/4/04 8:00 41 82 48 1 45 25 21 30 1 17 73 7/4/04 9:00 26 96 70 3 64 13 20 64 11 21 110 7/4/04 10:00 63 54 50 16 33 15 0 40 5 14 38 7/4/04 11:00 26 19 10 2 14 12 0 14 0 1 2

12 7/4/04 12:00 8 0 0 8 1 9 0 1 0 1 0

6 7/4/04 13:00 3 0 1 7 0 0 0 1 0 0 0

7/4/04 14:00 0 0 0 6 0 0 5 0 0 0 0 7/4/04 15:00 1 0 1 2 0 0 1 0 5 29 2 7/4/04 16:00 10 3 3 8 1 0 0 1 9 14 7 7/4/04 17:00 1 2 4 6 6 1 0 7 2 8 0 7/4/04 18:00 8 3 1 4 1 6 0 5 1 4 3 7/4/04 19:00 3 3 0 0 0 3 0 1 1 11 0 7/4/04 20:00 14 2 27 0 2 0 0 6 3 5 3 7/4/04 21:00 19 1 12 39 8 5 0 11 0 8 0 7/4/04 22:00 15 1 13 31 2 4 0 2 0 8 0 7/4/04 23:00 9 1 13 21 0 0 0 4 0 1 0 7/5/04 0:00 1 0 11 27 0 1 0 2 0 0 0 7/5/04 1:00 0 0 0 19 0 0 0 0 0 0 0 7/5/04 2:00 0 1 0 2 0 0 0 0 0 0 0 7/5/04 3:00 0 0 0 0 0 0 23 0 0 0 0 126

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/5/04 4:00 0 0 0 0 0 0 0 0 10 14 10 7/5/04 5:00 2 8 4 1 8 1 3 10 19 40 12 7/5/04 6:00 3 2 5 2 3 5 0 3 1 1 2 7/5/04 7:00 1 1 3 0 0 1 0 1 0 1 1 7/5/04 8:00 0 0 0 0 1 0 0 0 0 0 0 7/5/04 9:00 0 0 0 0 0 0 0 0 0 0 0 7/5/04 10:00 0 0 0 0 0 0 1 0 0 0 0 7/5/04 11:00 0 0 0 0 0 0 1 0 2 2 0 7/5/04 12:00 0 0 0 0 0 0 0 0 0 3 0

12 7/5/04 13:00 0 4 0 1 6 0 0 0 3 5 0

7 7/5/04 14:00 0 1 0 4 0 1 0 1 1 2 7

7/5/04 15:00 0 8 0 6 3 0 0 1 28 50 14 7/5/04 16:00 5 10 4 7 7 0 0 9 2 13 8 7/5/04 17:00 0 0 0 0 1 0 0 0 0 0 2 7/5/04 18:00 0 0 0 0 0 0 0 0 1 1 2 7/5/04 19:00 0 1 0 0 0 0 0 0 0 0 0 7/5/04 20:00 0 0 0 0 0 0 0 0 0 0 0 7/5/04 21:00 0 0 0 0 0 0 0 0 0 0 0 7/5/04 22:00 0 0 0 0 0 0 0 0 0 0 0 7/5/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 0:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 1:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 2:00 0 0 0 0 0 0 0 0 0 1 0 7/6/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 4:00 0 0 0 0 0 0 0 0 1 0 0 127

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/6/04 5:00 0 0 0 0 0 0 0 0 0 1 0 7/6/04 6:00 0 0 0 2 0 0 0 0 0 2 0 7/6/04 7:00 0 0 0 1 0 0 0 0 0 0 0 7/6/04 8:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 9:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 10:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 11:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 12:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 13:00 0 0 0 0 10 0 0 0 0 0 0

12 7/6/04 14:00 0 0 0 0 0 0 0 0 0 0 0

8 7/6/04 15:00 0 0 0 0 0 0 0 0 0 0 0

7/6/04 16:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 17:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 18:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 19:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 20:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 21:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 22:00 0 0 0 0 0 0 0 0 0 0 0 7/6/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 0:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 1:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 2:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 4:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 5:00 0 0 0 0 0 0 0 0 0 0 0 128

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/7/04 6:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 7:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 8:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 9:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 10:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 11:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 12:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 13:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 14:00 0 0 0 0 0 0 0 0 0 0 0

29 7/7/04 15:00 0 0 0 0 0 0 0 0 0 0 0

7/7/04 16:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 17:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 18:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 19:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 20:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 21:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 22:00 0 0 0 0 0 0 0 0 0 0 0 7/7/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 0:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 1:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 2:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 4:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 5:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 6:00 0 0 0 0 0 0 0 0 0 0 0 129

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/8/04 7:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 8:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 9:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 10:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 11:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 12:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 13:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 14:00 0 0 0 0 0 0 0 0 0 0 0 7/8/04 15:00 0 0 0 0 0 0 0 0 0 0 0

13 7/8/04 16:00 0 0 0 0 1 1 0 0 0 0 0

0 7/8/04 17:00 0 5 0 0 13 10 0 22 0 0 0

7/8/04 18:00 2 44 3 4 6 3 0 1 0 9 0 7/8/04 19:00 0 77 0 5 43 3 1 16 1 27 0 7/8/04 20:00 0 0 3 2 0 2 1 0 0 0 0 7/8/04 21:00 1 0 0 0 0 0 0 0 0 0 0 7/8/04 22:00 0 0 0 0 1 0 0 0 0 0 0 7/8/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 0:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 1:00 0 0 0 0 0 0 0 0 0 1 0 7/9/04 2:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 4:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 5:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 6:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 7:00 0 0 0 0 0 0 0 0 0 0 0 130

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/9/04 8:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 9:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 10:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 11:00 0 0 0 1 0 0 0 0 0 0 0 7/9/04 12:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 13:00 0 1 0 0 0 0 0 0 0 0 0 7/9/04 14:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 15:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 16:00 0 0 0 0 0 0 0 0 0 0 0

13 7/9/04 17:00 0 0 0 0 0 0 0 0 0 0 0

1 7/9/04 18:00 0 0 0 0 0 0 0 0 0 0 0

7/9/04 19:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 20:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 21:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 22:00 0 0 0 0 0 0 0 0 0 0 0 7/9/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 0:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 1:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 2:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 4:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 5:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 6:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 7:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 8:00 0 0 0 0 0 0 0 0 0 0 0 131

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/10/04 9:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 10:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 11:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 12:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 13:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 14:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 15:00 0 0 1 2 0 0 0 0 0 0 0 7/10/04 16:00 0 0 6 11 0 0 0 0 0 18 0 7/10/04 17:00 0 0 0 1 0 0 0 0 1 5 0

13 7/10/04 18:00 2 0 1 15 0 21 0 0 0 0 0

7/10/04 19:00 0 0 0 4 0 1 0 0 0 1 0 7/10/04 20:00 0 0 0 3 0 0 0 0 0 0 0 7/10/04 21:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 22:00 0 0 0 0 0 0 0 0 0 0 0 7/10/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 0:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 1:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 2:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 3:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 4:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 5:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 6:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 7:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 8:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 9:00 0 0 0 0 0 0 0 0 0 0 0 132

Gauge Name 1510P030 1510P075 1510P087 1510P105 1510P088 1510P116 1510P080 1510P125 1510P079 1510P104 1510P046 Date/Time Hourly Precipitation (mm) 7/11/04 10:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 11:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 12:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 13:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 14:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 15:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 16:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 17:00 0 0 1 0 0 0 0 0 0 0 0 7/11/04 18:00 0 0 0 16 0 2 0 0 0 0 0

13 7/11/04 19:00 0 0 0 0 0 0 0 0 0 0 0

3 7/11/04 20:00 0 0 0 0 0 0 0 0 0 0 0

7/11/04 21:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 22:00 0 0 0 0 0 0 0 0 0 0 0 7/11/04 23:00 0 0 0 0 0 0 0 0 0 0 0 7/12/04 0:00 0 0 0 0 0 0 0 0 0 0 0

133