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Doctoral Thesis

Erosion and weathering of the Northern Apennines with implications for the tectonics and kinematics of the orogen

Author(s): Erlanger, Erica

Publication Date: 2020

Permanent Link: https://doi.org/10.3929/ethz-b-000393261

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ETH Library Diss. ETH No. 26370

Erosion and weathering of the Northern Apennines with implications for the tectonics and kinematics of the orogen

Erica Danielle Erlanger Cover artwork by Reed Olsen DISS. ETH NO. 26370

Erosion and weathering of the Northern Apennines with implications for the tectonics and kinematics of the orogen

A thesis submitted to attain the degree of

DOCTOR OF SCIENCES of ETH ZURICH

(Dr. sc. ETH Zurich)

presented by

ERICA DANIELLE ERLANGER

Master of Science, Purdue University

born on 22.03.1986

citizen of France and the United States of America

accepted on the recommendation of

Prof. Dr. Sean D. Willett Prof. Vincenzo Picotti Prof. Sean F. Gallen Prof. Dr. Frank J. Pazzaglia

2020

2019 Abstract

Mountainous landscapes reflect the competition between denudation, uplift, and climate, which produce, modify, and destroy relief and topography. Bedrock rivers are dynamic topographic features and a critical link between these processes, as they record and convey changes in tectonics, climate, and sea level across the landscape. River incision models, such as the stream power model, are often used to quantify the relationship between topography and rock motion in the context of landscapes at steady state. At steady state, the stream power model predicts higher denudation rates for steeper river channels, while accounting for only the vertical motion of rock due to rock uplift or denudation. However, natural landscapes often have more complicated histories, particularly in convergent orogens with asymmetric topography, where steady state requires that denudation must balance both vertical and horizontal rock motion.

This thesis addresses this central issue by comparing the spatial and temporal pattern of denudation with metrics of topographic steepness in the Northern Apennine Mountains of Italy, a young and active orogen with asymmetric topography. New and existing catchment-averaged denudation rates from cosmogenic 10Be concentrations demonstrate that the steeper flank of the Northern Apennines is eroding more slowly than the gentler flank. Long-term denudation rates inverted from low-temperature thermochronometers show that this pattern of denudation across the orogen is long-lived, since at the least 3—5 Ma, and that denudation rates have decreased on the Ligurian side through time. The apparent decoupling between denudation rates and topography is resolved with a kinematic model of the orogenic wedge that accounts for the full vertical and horizontal rock velocity field. This model reconciles the 10Be concentrations, geomorphic observations, and geodetic rates of rock motion with the topography of the Northern Apennines, and provides new estimates for slab retreat rates consistent with recent estimates from tomography, surface geology, and morphology.

This thesis also explores the partitioning of denudation into physical erosion and chemical weathering in the Northern Apennines. Chemical weathering in particular is an important control on landscape evolution and the global CO2 budget. Most studies have focused on weathering in orogens comprised of silicate-rich lithologies, which can remove CO2 from the atmosphere over geologic timescales, whereas carbonate weathering is generally considered to be CO2 neutral. However, even in silicate-rich landscapes, carbonate weathering dominates total solute fluxes. Recently uplifted orogens in particular are often characterized by carbonate-rich, marine sedimentary sequences, so the global weathering flux of carbon and calcium to the oceans should be more strongly influenced by these orogens. However, the partitioning of denudation fluxes remains largely unexplored in mixed lithology orogens, so, it is unclear whether the same processes that control erosion and weathering apply to both silicate-rich and mixed lithology settings. Here, denudation fluxes from the Northern Apennines are partitioned into carbonate and silicate chemical weathering and physical erosion fluxes. These fluxes demonstrate that denudation is dominated by physical erosion of both silicate and carbonate rocks; carbonate physical erosion is controlled by lithology; weathering fluxes are dominated by carbonate dissolution; and denudation is negatively correlated with runoff. Finally, denudation fluxes from the Northern Apennines are similar to other temperature mountain ranges (e.g. Southern Alps of New Zealand), although total weathering fluxes from this study are generally higher, due to greater carbonate weathering fluxes. The results from this thesis challenge current interpretations regarding denudation rates through space and time and contribute to a broader understanding of surface and crustal processes in the Northern Apennines.

2 Zusammenfassung

Gebirgslandschaften spiegeln den Wettstreit zwischen Denudation, Hebung und Klimaeinflüssen wider, welche Relief und Topographie hervorbringen, verändern und zerstören. Erosive Flüsse sind dynamische topographische Merkmale dieser Landschaften und stellen eine wichtige Verbindung zwischen den genannten Prozessen dar, da sie Veränderungen der Tektonik, des Klimas und des Meeresspiegels aufzeichnen und diese Veränderungen auf die Landschaft übertragen. Flusserosionsmodelle wie das Stream-Power-Modell werden häufig verwendet, um den Zusammenhang zwischen Topographie und Hebungsrate im Gleichgewicht zu quantifizieren. Im Gleichgewicht sagt das Stream-Power-Modell höhere Denudationsraten für steile Flusslängsprofile voraus, wobei aber nur die vertikale Bewegung des Gesteins wegen Hebung oder Denudation berücksichtigt wird. Jedoch weisen natürliche Landschaften meist eine kompliziertere Entwicklung auf, insbesondere bei konvergenten Orogenen mit asymmetrischer Topographie, bei denen der Gleichgewichtszustand erfordert, dass die Denudation sowohl die vertikale als auch die horizontale Gesteinsbewegung ausgleicht.

Diese Dissertation befasst sich mit diesem zentralen Problem durch den Vergleich des räumlichen und zeitlichen Denudationsmusters mit der Steilheit der Topographie im nördlichen Apennin Italiens, einer jungen und aktiven Gebirgskette mit asymmetrischer Topographie. Schätzungen der Denudationsraten anhand von neuen und bestehenden, über das Einzugsgebiet gemittelten Konzentrationsmessungen von kosmogenem 10Be in Flusssedimenten zeigen, dass die steilere Flanke des nördlichen Apennins langsamer erodiert als die flachere Flanke. Langfristige Denudationsraten bestimmt durch Niedertemperatur-Thermochronometrie zeigen, dass dieses Denudationsmuster seit mindestens 3-5 Ma besteht und dass die Denudationsraten auf der ligurischen Seite im Laufe der Zeit abgenommen haben. Die anscheinende Entkoppelung zwischen Denudationsraten und Topographie kann mithilfe eines kinematischen Modells des Gebirgskeils, dass das vollständige vertikale und horizontale Gesteinsgeschwindigkeitsfeld berücksichtigt, erklärt werden. Dieses Modell stimmt die 10Be-Konzentrationen, geomorphologischen Beobachtungen und geodätischen Geschwindigkeiten der Gesteinsbewegung mit der Topographie des nördlichen Apennins ab und liefert neue Schätzungen der Rückzugsraten der Lithosphärenzunge im Einklang mit Schätzungen basierend auf Tomographie, Oberflächengeologie und Morphologie.

In dieser Dissertation wird außerdem die Aufteilung der Denudation in chemische Verwitterung und physikalische Erosion im nördlichen Apennin untersucht. Insbesondere die chemische Verwitterung spielt eine wichtige Rolle für die Landschaftsentwicklung und das globale CO2-Budget. Bisherige Forschungsarbeiten konzentrieren sich hauptsächlich auf Verwitterung in Orogenen bestehend aus silikatreichen Gesteinen, da diese der Atmosphäre

CO2 entziehen. Karbonat-Verwitterung hingegen ist CO2-neutral. Allerdings dominiert auch in Silikat-reichen Landschaften die Karbonat-Verwitterung den Gesamtfluss gelöster Stoffe. Vor allem kürzlich emporgehobene Orogene sind häufig durch karbonatreiche, marine Sedimentsequenzen gekennzeichnet, weshalb der globale Verwitterungsfluss von Kohlenstoff und Kalzium in die Ozeane stärker von solchen Orogenen beeinflusst werden sollte. Trotzdem ist die Aufteilung der Denudation in Orogenen mit gemischten Lithologien noch weitgehend unerforscht. Daher ist unklar, ob die gleichen Prozesse, die Erosion und Verwitterung steuern, sowohl auf silikatreiche als auch auf gemischte Lithologien zutreffen. Hier werden die Denudationsflüsse des nördlichen Apennins in Karbonat- und Silikatverwitterungflüsse und in physikalische Erosionsflüsse aufgeteilt. Diese Flüsse zeigen, dass die Denudation negativ mit dem Abfluss korreliert, die Denudation von physikalischer Erosion sowohl von Silikat- als auch von Karbonatgesteinen dominiert wird, die physikalische Erosion des

3 Karbonatgesteins durch die Lithologie beherrscht wird und die Verwitterungsmengen von der Lösung von Karbonatgestein dominiert werden. Schließlich sind die Denudationsraten im nördlichen Apennin vergleichbar mit denen in anderen Gebirgsketten in gemäßigten Klimazonen (z.B. Neuseeländische Alpen), obwohl die Verwitterungsraten dieser Studie im Vergleich höher sind. Die Ergebnisse dieser Dissertation stellen aktuelle Interpretationen der Denudationsraten durch Raum und Zeit in Frage und tragen zu einem breiteren Verständnis der Oberflächen- und Krustenprozesse im nördlichen Apennin bei.

4 Acknowledgements

I am indebted to the many people who have contributed to and shaped my research at ETH and life in Zürich. Pursuing a Doctoral Degree at the ETH Zürich has been a privilege, and my time in Switzerland has been a wonderful chapter in my life, filled with many new faces who I hope will continue to play a role in my future endeavors and adventures.

I must first thank my advisor, Sean Willett, or “Big Sean”. Thank you for taking me on as your student, for challenging me and supporting me in my PhD research, and for helping me to obtain the postdoc that awaits me in Potsdam. You are also a man of many other interests and talents, so I must also thank you for the occasional plant care tips and for the few times we went rappelling down your waterfalls in Bonassola. I would also like to thank the rest of my doctoral committee. Sean Gallen, your teaching style and enthusiasm for geology have been an inspiration to me during my PhD, and I’m so thankful for your support and willingness to brainstorm and execute new ideas. Thank you for your contributions to my research, and I hope we’ll continue to work together in the future. Vincenzo, you’ve taught me so much about the Apennines and about Italian food, I’m not even sure which is more important at this point. I know that to find the strath of a river terrace, you should follow the trail of mint plants or water that seeps from the terrace. I can also safely say that I know a Minestrone Genovese is only ready when the spoon sticks straight out of the soup, and that the best brodo is made with an old chicken. Your thirst for geology and Italian food, in all their glorious detail, have made my time at ETH both enjoyable and delicious. I would also like to express my gratitude to Frank Pazzaglia for flying out to Switzerland and acting as the external member of my examining committee. Finally, my thanks go to Whitney Behr, who agreed to act as the chairperson for my defense. I must also express my sincere gratitude to Giuditta. It’s hard for me to describe how important you’ve been to me and my research at ETH. You trained me to prep samples, took the time to date my samples, and worked closely with me to make sure I understood the ins and outs of thermochronometry. Thank you for your commitment to my work, and for occasional pizza dates.

My collaborators have also helped me in no small measure. My gratitude goes to Aaron Bufe, without whom my weathering chapter and SNF proposal would’ve suffered from a fundamental knowledge gap. Thank you for your hundreds of poignant, intelligent, and insightful comments on my writing. I am thankful for your help, and I am honored that I will be able to work with you for the next two years at the GFZ. I would also like to thank Jeremy Rugenstein. You’ve played a crucial role in fostering my interest in weathering and geochemistry. Thank you for your help in the field, for measuring my water chemistry samples, for always having an open ear to brainstorm, and for your eternally positive attitude. Thank you to Maarten Lupker for your advice on all things cosmogenic nuclides, and to Negar Hagipour, for all your help with processing and measuring my difficult 10Be samples. Thank you to the group of Frederic Hermann for hosting me at UNIL and allowing me to use their OSL labs and equipment.

I would also like to extends my thanks to my colleagues and friends for the professional and personal support they have given me. To the ESD Group: Thank you to former and current members of the ESD group: Richard Ott, Yanyan Wang, Odin Marc, Chia-Yu Chen, Malwina San Jose, Loraine Gourbet, and Kosuke Ueda for stimulating discussions, feedback, hikes, and trips to the Limmat. I must also thank our group secretary, Katharin Fehr. I can’t thank you enough for your help with the admissions process and with all the logistics of moving to another continent, and finally for the wonderful John baker fruit breads you brought to our institute coffee. Thank you to Alina Fiedrich and Richard Ott for editing my terrible German writing and to Fabian 5 Kuhn for moral support. A special thanks goes to Sascha Winterberg: my office mate, Alpine expert, and friend. Thank you for the many hikes, trips, vacations, family holidays, and conversations about the Greater Alpine area that we’ve shared. I hope we stay in each other’s lives for a long time. Special thanks also go to Julia Krawielicki. We’ve had so many wonderful moments together, and I don’t know how I’ll manage to see action movies without you. Thank you for the countless times you helped to pin me into my latest fabric creation and for also including me in your family Christmas celebrations.

Finally, I must thank my family. To my mom and dad, Jackie and Robert, thank you for being understanding of my choice to live and study on another continent, for traveling to see me, and editing my scientific writing. Thank you to my Swiss family: Michael, Marianne, Simon, Annica, Max, Lucian, Andrea, and Coco. I met all of you only four years ago when I arrived in Switzerland, but you quickly felt like family. Thank you for allowing me to be a part of your lives, for sharing brunches with me, and making Switzerland feel more like a home.

6 Chapter 1 Introduction

7 1 Introduction

Active orogens have the highest rates of uplift and denudation on Earth, and these processes compete to build, modify, and destroy mountain topography (Chamberlin, 1899; Raymo et al., 1988; Molnar and England, 1990; Gaillardet et al., 1999; Willett et al., 2001; West et al., 2005). Classical, conceptual models of landscape evolution have long recognized that the growth of topography is driven by tectonic uplift, while the degradation and lowering of topography are driven by denudation processes (Davis, 1899; Penck, 1953; Hack, 1975).

Rivers provide a fundamental link between denudation processes, uplift, and the evolution of topography. In particular, bedrock rivers are archives of changes in tectonics, climate, and sea level and transmit these changes over the landscape (Whipple and Tucker, 1999). For example, preserved fluvial strath terraces can be dated to estimate paleo-erosion rates or the rate of river incision, a proxy for rock uplift (Pazzaglia and Brandon, 2001; Picotti and Pazzaglia, 2008; Cyr and Granger, 2008; Erlanger et al., 2012). As rivers are the major transporting agents of sediment and solutes from the continent to the ocean, and they integrate large portions of the land surface, we can use the wealth of information from rivers to estimate denudation processes on a regional scale.

There is an important distinction between denudation, which describes the overall removal of rock from the Earth’s surface, and its components: physical erosion and chemical weathering. Physical erosion is the mechanical breakdown of rock, and chemical weathering is the dissolution of rocks at the surface. Each of these processes can be measured in the landscape using various methods that integrate over different spatial and temporal scales. Physical erosion has historically been quantified with sediment yield measurements from river stream gauges and reflect short-term rates (1-10 years) (Clayton and Megahan, 1986; Granger and Riebe, 2013), and chemical weathering has been estimated from water solute fluxes (Gaillardet et al., 1997; Jacobson et al., 2003; West et al., 2005; Gaillardet et al., 2018). During the last couple of decades, cosmogenic nuclide dating has emerged as a standard way of measuring denudation, chemical weathering, and physical erosion in landscapes (Granger and Riebe, 2013). Chemical weathering and physical erosion rates can be quantified together from the mass balance of elements in soils and bedrock, (Riebe et al., 2001b, 2004; Granger and Riebe, 2013), and denudation can be quantified from bedrock landforms (e.g. inselbergs) (Bierman and Caffee, 2002), unconsolidated soils (Riebe et al., 2001a), or from alluvial sediment and watersheds (Granger et al., 1996, 1997). Estimating geologically recent (102 – 105 yrs), catchment-wide denudation rates with cosmogenic 10Be from bulk river sediment has become a well-established method in geomorphology (Granger et al., 1996; Bierman and Steig, 1996; von Blanckenburg, 2005). The concentration of cosmogenic 10Be reflects the residence time of a quartz mineral near the surface, as it is primarily produced within the upper meter below the ground surface. As rivers channels integrate hillslope and fluvial processes, they are a natural mixing agent for sediment, so that the 10Be concentration in river sediment reflects the spatially-averaged denudation rate of the entire upstream area that contributes sediment.

Over long timescales, orogenic systems are suggested to approach an equilibrium between rates of denudation and rock uplift, known as “steady-state”. At steady state, empirical scaling relationships between denudation and river channel steepness predict that bedrock river channels are steeper in more rapidly eroding landscapes. These relationships can be expressed by the stream power model, which describes the denudation rate in terms of area (A), slope (S), rock erodibility (K), and nondimensional exponents (m and n):

E = K⋅Am⋅Sn (1) 8 where rock erodibility includes substrate, hydrology, and climate processes. (Wobus et al., 2006; Kirby and Whipple, 2012). We can then solve for this equation in terms of slope:

S = (E/K)(1/n) ⋅A(m/n) (2)

Empirical observations of river profile geometries (Hack, 1957; Flint, 1974) first described slope interms of the power-law relationship between local channel slope (ks), contributing drainage area (A), and channel concavity (-θ):

-θ S = ks⋅A (3)

Combining equations (2) and (3), we can link the observed topographic form of the river channel with the stream power incision model, and solve for denudation rate in terms of channel steepness normalized for drainage area (ksn), rock erodibility (K), and the nondimensional exponent (n):

n E = K⋅ksn (4)

Most emperical studies have observed monotonic increases in denudation rates with respect to channel steepness, consistent with the function form of equation 1 (Safran et al., 2005; Harkins et al., 2007; DiBiase et al., 2010; Miller et al., 2013). In most of these cases, the relationship is non-linear and consistent with a power law exponent greater than 1 (Ouimet et al., 2009; Cyr et al., 2014; Hilley and Young, 2018) The Apennines Mountains in particular host some of the highest measured denudation rates, which are associated with some of the lowest channel steepness values (Figure 1). In this study, I explore relationships between denudation, weathering, erosion, and topography in the Apennines Mountains, in order to understand the dominant controls (e.g. tectonics, climate, lithology) on landscape evolution.

9 A) 800 E. Tibet, China (Ouimet et al., 2009) Appalachians, USA (Miller et al., 2013) Andes, Bolivia (Safran et al., 2005) Apennine Mtns, Italy (Cyr et al., 2010) NE Tibet, China (Hawkins et al., 2007) 600 San Gabriel Mtns, USA (DiBiase et al., 2010) E and S Alps, Europe (Norton et al., 2011) Rwenzori Mtns, E Africa (Roller et al., 2012) )

0.9 400 (m sn K

200

0 0 500 1000 1500 2000 2500 10Be Denudation Rate (m/Myr)

B) 1000

100 ) 0.9 (m sn K

E. Tibet, China (Ouimet et al., 2009) 10 Appalachians, USA (Miller et al., 2013) Andes, Bolivia (Safran et al., 2005) Apennine Mtns, Italy (Cyr et al., 2010) NE Tibet, China (Hawkins et al., 2007) San Gabriel Mtns, USA (DiBiase et al., 2010) E and S Alps, Europe (Norton et al., 2011) Rwenzori Mtns, E Africa (Roller et al., 2012) 1 1 10 100 1000 10000 10Be Denudation Rate (m/Myr) Figure 1. Comparison of basin-averaged denudation rate (m/Ma) with normalized channel steepness for orogens around the world on A) linear axes and B) log-log scale axes. Original data sources are: Bolivian Andes (Safran et al., 2005), Northeastern Tibet (Harkins et al., 2007), Eastern Tibet (Ouimet et al., 2009), San Gabriel Mountains (DiBiase et al., 2010), Apennines Mountains (Cyr et al., 2010), Eastern and Southern Alps (Norton et al., 2011), Rwenzori Mountains (Roller et al., 2012), Appalachian Mountains (Miller et al., 2013), and Santa Lucia Mountains (Hilley and Young, 2018).

10 1.1 Northern Apennines

One of the best-studied, active mountain chains in the world is the Apennine Mountains, which form the backbone of peninsular Italy (Figure 2). The Apennines are a young orogen, broadly formed due to the south- westward subduction of the Adriatic microplate beneath Eurasia since 30 Ma (Ricci Lucchi, 1986; Dewey et al., 1989). The Apennines developed as a subaqueous accretionary wedge, fed from the uplifting Central Alps until the Miocene, (Ricci Lucchi, 1975; Roveri et al., 2001; Gandolfi et al., 2007). From the Miocene to Plio- Pleistocene, rapid exhumation resulted in the sub-aerial exposure of the Apenninic wedge and the creation of topographic relief (Balestrieri et al., 1996; Boccaletti and Sani, 1998; Abbate et al., 1999; Fellin et al., 2007).

Unlike the classical model for convergent orogens, the Apennines evolved in response to rollback of the lower plate. Accretion of material into the upper plate coupled with retreat of the subduction system has produced contemporaneous crustal shortening in the external part of the orogen, and extension in the internal part of the orogen (Vai and Martini, 2001). Today, the Northern Apennines are characterized by relatively low average elevations of 400 m, mixed siliciclastic and carbonate lithologies, and a primary drainage divide that separates rivers draining to the Adriatic Sea (Adriatic side) from rivers draining to the Ligurian Sea (Ligurian side) (Figure 3).

ALPS N

Po Plain DINARIDES Adriatic Sea

Ligurian Sea APENNINES

Rome Ligurian Provençal Basin Tyrrhenian Sea

0 200 Km Figure 2. Colored hillshade map of Italy and ocean bathymetry, showing locations of mountain ranges in the greater Alpine area and adjacent ocean basins. Location of the study area is given by the white box. 11 9E 10E 11E 12E N 5 4

Parma

Bologna

Genoa

La Spezia N 4 4 Ligurian Sea 0 20 40 60 80 m Florence Figure 3. Topography of study area outlined in white on Figure 2. Black line illustrates the drainage divide that separates rivers draining to the Ligurian Sea and rivers that drain to the Adriatic Sea.

The topography of the Northern Apennines is asymmetric: at the 10-km scale, the Ligurian side is shorter and steeper, whereas the Adriatic side is longer and has gentler slopes. Numerical models suggest that horizontal rock motion is responsible for asymmetric topography in orogens, such as the Southern Alps of New Zealand, the Olympic Mountains of the USA, and the Central Range of Taiwan (Willett et al., 2001). These model results are consistent with the distribution of exhumation ages across the orogen (Fuller et al., 2006; Willett and Brandon, 2002; Willett et al., 2003), which reflects the unroofing history of rock as it approaches the surface, and is attributable to tectonic or denudational processes. Exhumation ages are determined with radioisotopic thermochronometers, which reflect the cooling history of rocks over timescales of 105-107 years and depths between 2-10 km (Reiners and Brandon, 2006). Using multiple thermochronometers within the same location can also constrain material paths in an orogen, and the relative importance of horizontal versus vertical rock motions. In the Northern Apennines, the distribution of exhumation ages from multiple thermochronometers suggests that material paths have an important component of horizontal rock motion (Thomson et al., 2010). Regionally, exhumation ages are youngest on the Adriatic side relative to the Ligurian side, which translates to an order of magnitude higher denudation rates on the Adriatic side between 3-5 Ma (Thomson et al., 2010). One of the main questions that remains is whether the evolution of the Northern Apennines and its asymmetric topography are also reflected in modern denudation rates?

Currently, modern denudation estimates exist only for the Adriatic side, and rates for major rivers vary between ~0.2–0.8 mm/yr (Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016). Previous studies comparing 10Be denudation rates, river incision rates, and paleo-erosion rates suggest that the Northern Apennines have been in a state of dynamic equilibrium over the past 900 ka (Cyr and Granger, 2008). This state of equilibrium suggests that the steeper Ligurian side should be eroding faster than the gentler Adriatic side, based on equation

1. However, low denudation rates coupled with high ksn values (Figure 1) have been observed along one Adriatic River, and this pattern was attributed to tributaries draining different lithologies with variable erodibility (K) values (Cyr et al., 2014). This study and others address a current debate in geomorphology on the role of lithology in modulating channel steepness. Some find a strong lithologic control on steepness from modeling (Stock and Montgomery, 1999) and field studies (Gallen, 2018), whereas others have found that denudation

12 rates and ksn are insensitive to rock type (Miller et al., 2013; Hilley and Young, 2018).

Although the role of lithology on denudation is debated, early studies on chemical weathering have shown that lithology is a primary control, as it dictates the minerals available at the surface for weathering (Stallard and Edmond, 1983; Meybeck, 1987; Galy and France-Lanord, 1999). Young orogens, such as the Northern Apennines, are typically characterized by marine sedimentary sequences that contain important volumes of carbonate, which can enhance chemical weathering, as carbonate weathers a factor of 3 times faster than silicates (Meybeck, 1987; Gaillardet et al., 2013). However, previous studies have estimated chemical weathering rates in orogens containing large volumes of silicate-rich minerals such as the Andes Mountains (Gaillardet et al., 1997), Sierra Nevada Mountains of California (Riebe et al., 2001b), Himalaya Mountains (West et al., 2005), and New Zealand Southern Alps (Jacobson et al., 2003; Jacobson and Blum, 2003), so chemical weathering rates are lacking from landscapes with more typical, mixed lithologic assemblages. As such, the Northern Apennine Mountains present an excellent opportunity to resolve denudation, physical erosion, and chemical weathering budgets in a young orogen.

Over the last several decades, numerous studies have addressed rates of exhumation (Balestrieri et al., 1996; Ventura et al., 2001; Zattin et al., 2002; Balestrieri et al., 2003; Fellin et al., 2007; Thomson et al., 2010; Malusà and Balestrieri, 2012; Carlini et al., 2013; Balestrieri et al., 2018), paleo-denudation and modern denudation rates on the Adriatic side of the Northern Apennines (Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016), geodetic uplift rates (D’Anastasio et al., 2006; Ferranti et al., 2006; Serpelloni et al., 2013), horizontal GPS rates (Bennett et al., 2012; Devoti et al., 2017) and orogen kinematics (Thomson et al., 2010). Despite numerous studies on the evolution of the Northern Apennines, and studies on the rates of denudation and surface deformation through time and space, a number of knowledge gaps still exist. (1) We have no estimates of catchment-averaged denudation rates or long-term denudation rates from rivers on the Ligurian side of the Northern Apennines to compare with existing estimates from the Adriatic side. (2) We lack information on chemical weathering and erosional fluxes for young orogens comprised of mixed siliciclastic-carbonate lithologies.

Another important point to consider is the fundamental assumption of a vertical reference frame for denudation rates and ksn estimates, which has implications for conditions of steady state. Steady state is commonly assumed to represent a balance between the vertical motions associated with denudation and rock uplift (England and Molnar, 1990), although previous modeling and field-based studies on active orogens have indicated that steady state, in fact, reflects a balance between both the horizontal and vertical motion of rock (Willett et al., 2001; Miller et al., 2007). Catchment-averaged denudation rates and ksn estimates also assume a vertical reference frame, whereas the pattern of exhumation rates in the Northern Apennines has shown an important horizontal component of rock motion (Thomson et al., 2010). Hence, one of the reasons the relationship between erodibility, denudation, and channel steepness in the Northern Apennines shows different patterns from other active orogens (Figure 1) could also be due to incorrect assumptions about the nature of denudation rates. As such, understanding the evolution and present topography of the Northern Apennines requires a holistic approach to quantifying and assessing the relationships between surface and crustal-scale processes. We address these knowledge gaps by taking advantage of the wealth of information provided by the geometry of river catchments, longitudinal river profiles, riverine water chemistry, and river sediments. The implications of this research are broad and will contribute new, quantitative data on the relationship between denudation rates through space and time, and the tectonics and kinematics of the Northern Apennines orogen. 13 1.2 Thesis Objectives The goal of this research is broadly to understand the spatial and temporal variability in surface and crustal- scale processes in the Northern Apennines. This thesis is divided into three chapters. Chapter 2 focuses on interpreting the kinematics of the Northern Apennines orogen, through a compilation of new and existing catchment-wide denudation rates derived from concentrations of cosmogenic 10Be (Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016) and geodetic rates of vertical and horizontal rock motion (D’Anastasio et al., 2006; Bennett et al., 2012; Devoti et al., 2017). Chapter 3 reviews existing exhumation data from low-temperature thermochronometers, contributes new detrital apatite fission-track ages, models long-term denudation rates, and proposes a new model for the spatial and temporal development of denudation through time across the Northern Apennines orogenic wedge. Chapter 4 focuses on calculating denudation, weathering, and erosional fluxes in the Northern Apennines from10 Be concentrations, water solutes, and the carbonate sand in the catchment. These fluxes are then partitioned between silicate and carbonate lithologies and compared with results from silicate-rich endmembers locations in other orogens. Finally, Chapter 5 provides a synthesis and concluding remarks on this thesis, as well as future work directions.

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17 CHAPTER 2 The paradox of topography and erosion rates in the active orogen of the Northern Apennines

Erica Erlanger, Yanyan Wang, Sean D. Willett, Sean F. Gallen, Vincenzo Picotti

Submitted to Geology Journal

18 The paradox of topography and erosion rates in the active orogen of the Northern Apennines

Erica D. Erlanger1, Yanyan Wang1, Sean D. Willett1, Sean F. Gallen2, and Vincenzo Picotti1

1Department of Earth Sciences, ETH Zürich, Zürich, 8092, Switzerland

2Department of Geosciences, Colorado State University, Fort Collins 80523, CO

ABSTRACT The Northern Apennines are an active orogenic wedge with tectonic deformation driven by subduction of the Adriatic plate in the east and backarc spreading in the Ligurian Sea to the west. The orogen is characterized by: 1) Late Cenozoic subduction rates of 5-10 mm/yr; 2) geodetic extension rates of 2–4 mm/yr across the range, nearly balanced by contraction across the Adriatic mountain front; 3) geodetic uplift rates of 0.5–2.5 mm/yr in the NE, but stability or subsidence in the SW; 4) an asymmetric topography with the main divide offset to the west resulting in a steeper southwest-facing mountain flank; and 5) high erosion rates in the NE and low erosion rates in the SW based on detrital cosmogenic 10Be concentrations presented in this paper. These last two observations imply a negative correlation between erosion rate and metrics of landscape steepness. In this paper, we demonstrate that this apparent paradox of decoupling between topographic form and erosion rate can be resolved by a kinematic model of slab retreat, in which erosional flux is described by a vector with horizontal and vertical components. This model reconciles geodetic data, 10Be concentrations, and geomorphic observations.

INTRODUCTION Subduction of the Adriatic plate under the Northern Apennines has been continuous over the last 30 Ma, and nearly equal in rate to the rollback of the slab and trench with respect to stable Europe (Faccenna et al., 2014; Rosenbaum and Piana Agostinetti, 2015), offering an opportunity to study the crustal kinematics of a retreating subduction system. Deformation in the Northern Apennines is dominated by mid-crustal thrusting at the mountain front, and upper crustal normal faulting focused in the center of the range (Malinverno and Ryan, 1986; Cavinato and De Celles, 1999) Horizontal GPS measurements (Bennett et al., 2012; Devoti et al., 2017) illustrate NE motion with increasing velocity from SW to NE, indicating that the Ligurian and part of the Adriatic mountain flanks are under extension, while contraction occurs primarily at the mountain front and buried thrusts underlying the Po Plain sediments. Geodetic releveling data (D’Anastasio et al., 2006) indicate uplift focused on the Adriatic side, with a maximum rate of 2.5 mm/yr. The topography of the Northern Apennines illustrates a ridge crest that is offset to the west (Fig. 1A), resulting in a regionally steeper southwestern flank and a gentler northeastern flank, an observation that we quantify in this paper. The topographic asymmetry of the range is inconsistent with the pattern of geodetic uplift, as higher uplift rates normally imply steeper topography. In this study, we present new catchment- averaged erosion rates from major drainage basins in the Northern Apennines, supplementing published data, to evaluate how erosion rates compare to rock uplift rates and topographic metrics. Our results illustrate a disconnect between the present distribution of topography, geodetic uplift and apparent erosion rates, and we 19 show that these contradictory observations can be reconciled with a self-consistent kinematic model of the orogen.

TOPOGRAPHIC CHARACTERISTICS To demonstrate that the asymmetry of the Northern Apennines extends to the scale of geomorphic processes, we conducted two topographic analyses (Figs. 1B-1C), calculating the normalized length of the river, χ (Perron and Royden, 2013; Willett et al., 2014), and the channel steepness normalized for upstream drainage area (ksn) (Wobus et al., 2006). The Ligurian side is shorter, in normalized length, with lower values of χ at the divide (Fig. 1B), and steeper, with higher ksn values, particularly in the upper reaches. In contrast, the Adriatic side is longer, with larger χ values at the divide and lower average ksn values (Fig. 1C, Fig. DR1).

The contrast in χ at the water divide is large. Consistent with the contrast in ksn across the divide, these metrics imply that the water divide should move NE for long-term equilibrium to be obtained. Differential uplift rate, precipitation or rock erodibility can alter that expectation, but uplift rates are an order of magnitude higher on the Adriatic side (D’Anastasio et al., 2006) and precipitation is higher on the Ligurian side (Crespi et al., 2018), each of which should produce asymmetry with the opposite sense. Rock erodibility could partially explain the asymmetry and is discussed later. Numerous wind gaps attesting to river capture or basin beheading also support motion of the main Apennines divide to the NE (Ferraris et al., 2012; Spagnolo and Firpo, 2007).

EROSION RATES To test how modern erosion rates reflect the geomorphic asymmetry, we measuredin situ cosmogenic 10Be concentrations from fluvial quartz sediment, obtained from 11 catchments across the Northern Apennines. These supplement published data (Cyr and Granger, 2008; Wittmann et al., 2016)river incision, and uplift rates in the northern and central Apennines, Italy, since 0.9 Ma, are determined from new cosmogenic nuclide data. Beryllium-10 concentrations in modern and middle Pleistocene sediments indicate erosion rates from 0.20 to 0.58 mm/yr. These rates are similar to estimates of sediment yield (0.12-0.44 mm/yr and provide good spatial coverage of the range (Fig. 1D). Erosion rates (Methods, Data Repository) vary from 0.254 ± 0.044 mm/yr to 0.643 ± 0.230 mm/yr on the Adriatic side and from 0.105 ± 0.016 mm/yr to 0.284 ± 0.036 mm/yr on the Ligurian side (Fig. 1D, Table 1). Erosion rates for Ligurian catchments are a factor of 2-6 times lower than their Adriatic counterparts (Figs. 1 & 2).

PARADOX OF EROSION RATES AND GEOMORPHIC ASYMMETRY In general, geomorphic process laws predict a positive correlation between metrics of steepness, be they relief, slope or channel steepness (Wobus et al., 2006; Kirby and Whipple, 2012) and both rock uplift and erosion rates. In the Northern Apennines, the regional steepness pattern from ksn, χ plot slope, and 10 km-scale topographic slope show the opposite trend, with the basins of the Ligurian side exhibiting denudation rates systematically lower than denudation rates from the gentler basins on the Adriatic side (Fig. 2). Geodetic and geomorphic measures of rock uplift rate show the same discrepancy, with high uplift rates on the Adriatic side, and low uplift rates on the Ligurian side. Furthermore, the prediction of χ mapping is that the main divide is moving NE, a process normally associated with higher erosion rates in the aggressor basins, in this case those draining to the SW. Lithology is variable, exposing different sedimentary units across the range and meta-sedimentary mélanges and ophiolite units that are more prevalent on the Ligurian side, suggesting the harder lithologies could be associated with the steeper catchments. However, many of the upper catchments on the Adriatic side expose the same, harder lithologies, with no systematic increase in steepness. Spatial changes in ksn coincide 20 with the drainage divide, not major lithologic changes. Although we recognize that rock erodibility plays a role, it cannot be the exclusive control on denudation. Cosmogenic 10Be is accumulated over 103 to 104 yrs, so it is possible these data record only a short-term transient state, but the rates obtained here are consistent with paleo-erosion rates from Early-Middle Pleistocene marine terraces in the Romagna Apennines (Cyr and Granger, 2008) and thermochronometric ages from both sides of the Apennines (Thomson et al., 2010). These contradictory observations thus constitute a paradox that fails to explain the present distribution of topography, kinematics and geomorphic process rates in the Northern Apennines.

HORIZONTAL MOTION We propose that the key to reconciling the difference between erosion rates, uplift rates, and topographic steepness is to recognize that 10Be concentrations can be interpreted in a more general manner to account for tectonic kinematics. Assuming secular equilibrium, such that the total 10Be produced in a catchment is equal to the total 10Be exported, the concentration of 10Be is the ratio of the total 10Be produced and the total volume of quartz converted to sediment by erosion. The volume of quartz released per unit time is a mass flux and can be expressed as the product of the catchment area projected onto a horizontal surface, as is implicitly done in every DEM analysis, and the vertical rock velocity with respect to the surface, which is the erosion rate. However, rock motion with respect to the Earth’s surface is not always vertical, particularly in tectonically active settings where horizontal motion and deformation are important (Willett et al., 2001; Miller et al., 2007). In this case, the mass flux of quartz from the Earth should be calculated using a velocity with a non-zero horizontal component, so the catchment surface should be projected onto a plane orthogonal to the direction of the rock motion (Fig. DR2). As an end-member, we can consider pure horizontal motion of the rock relative to the surface, where the appropriate area for calculating the flux is the basin surface projected onto a vertical place. The horizontal velocity in this formulation is the average velocity of the Earth’s surface with respect to the rock, but does not imply uniform, horizontal motion of the surface at each point within the catchment. A typical catchment, comprised of channels and hillslopes at various orientations, can be regarded as eroding downward, but at differential rates along the catchment, such that the net result is apparent horizontal motion of the land surface at the kilometer scale (Willett et al., 2018). Cosmogenic 10Be production is thus unaffected by the assumption of horizontal motion, but the quartz or rock velocity implied by a given flux is affected. We demonstrate this concept by calculating an apparent velocity consistent with our 10Be concentrations, assuming a purely horizontal flux, where the catchment surface is projected onto a vertical plane with a strike direction parallel to the main divide (Methods, Data Repository). A positive flux requires that the velocity be directed away from the main divide, so taking a reference frame as positive to the NE implies that apparent velocities on the Ligurian side will be negative. We find apparent horizontal velocities ranging from -1.6 to -5.1 mm/yr for the Ligurian side and 5.3 to 21.1 mm/yr for the Adriatic side (Table 1). The more general case with vertical and horizontal velocity components is discussed below.

KINEMATIC MODEL These concepts and data can be unified in a kinematic model of an orogenic wedge abovea subducting, retreating slab (Fig. 3), where the prowedge (Adriatic side) and retrowedge (Ligurian side) are the accreting and non-accreting sides of the orogen, respectively, sensu Willett et al. (1993) (Methods, Data

Repository). Subduction is driven entirely by slab retreat at a velocity EVH (Fig. 3A). The relief formed by orogenesis sits above the slab hinge zone, with a topographic surface that is fixed in the vertical and has a horizontal velocity EVS relative to Eurasia. The surface velocity, EVS, must be equal to the retreat velocity, EVH, or the topography would be progressively offset from the subduction zone. Accretion of material into the wedge 21 implies motion of rock within the wedge with respect to the surface, SVW, so there is a non-zero horizontal component to the erosional flux. Horizontal GPS velocities across the Apennines provide an estimate of the wedge velocity, EVW, indicating an average of 2 to 4 mm/yr towards the NE, and this velocity must always be less than the hinge retreat velocity, EVH. These velocities can be expressed with respect to one another, recognizing the vector addition relationship:

EVH = EVS = EVW - SVW = EVW + WVS (1) The interpretation of detrital 10Be concentrations is consistent with the vertical and horizontal kinematics of Figure 3A when the non-vertical flux theory described above is applied. This is illustrated by showing the erosional flux as a function of the horizontal and vertical velocities (Fig. 3B). Each point on the y-axis represents a 10Be concentration converted to an apparent vertical erosion rate, as in conventional analysis (Table 1); the same measurement converted to an apparent horizontal velocity (Table 1) plots as a single point on the x-axis. Because velocity is defined to be positive to the NE, retrowedge points plot on the negative x-axis and prowedge points on the positive x-axis. The solid green and gray lines (Fig. 3B) are fit to the means of all 10Be measurements for the retrowedge and prowedge, respectively, and points on these lines represent combinations of horizontal and vertical velocities consistent with the 10Be measurements. The horizonal velocity SVW will always be negative, but given the opposite slopes of the wedge surface, SVW decreases the contribution of horizontal motion to the prowedge denudational flux and increases the contribution of horizontal motion to the retrowedge flux. Given our assumption of a common horizontal velocity across the wedge, but independent vertical velocities for the prowedge UP and retrowedge UR (gray and green bands), the intersection of the gray and green lines with any vertical line defines a kinematic model consistent with the 10 Be data. A horizontal velocity, SVW, between approximately -2 and -5 mm/yr, thus provides an acceptable fit to both geodetic uplift data and cosmogenic 10Be data. Horizontal surface motion of 2–5 mm/yr is sufficient to fully account for the 10Be concentrations in catchments of the retrowedge, suggesting the erosional flux of the retrowedge is driven entirely by the horizontal velocity. For the prowedge, the model suggests a combination of vertical and horizontal motion to account for both the growth of the topography and the erosional flux.

The estimate of SVW can also be combined with the GPS-based estimate of EVW to obtain the hinge retreat rate, EVH, using equation (1), giving an estimate of ~4 to 9 mm/yr. The late Cenozoic retreat rate is estimated independently as 6 to 10 mm/yr, dependent on latitude (Faccenna et al., 2014; Rosenbaum and Piana Agostinetti, 2015), so although uncertainties are large, these estimates are essentially equivalent, suggesting that geodetic data and erosional flux data are consistent with a steady rate of retreat over the late Cenozoic. The asymmetric topography of the Northern Apennines is consistent with a large component of horizontal motion. Long-wavelength asymmetry is a consequence of horizontal advection of topography balanced by steeper rivers (Willett et al., 2001). Miller et al. (2007) showed that the Siwalik Hills are similarly characterized by spatially variable vertical rock uplift rates, where higher channel steepness and concavity in distal (retrowedge) rivers were attributed to horizontal advection in the direction of streamflow. The degree of asymmetry is difficult to estimate as it depends on assumptions regarding erosion processes. For example, Willett et al. (2018) showed that the importance of topographic advection depends strongly on the slope exponent, n, in a stream-power incision law. In the Northern Apennines, the steeper Tyrhennian topography is consistent with horizontal advection driven by tectonic crustal accretion towards the SW (Fig. DR3, wedge schematics, Fig. 3B). Erosion on the retrowedge counters the divide motion, and the 10Be concentrations are consistent with a balance between the horizontal tectonic velocity and the velocity of the surface with respect to the underlying rock. The relative motion between the surface and the underlying rock gives the appearance of divide motion to the 22 NE, consistent with geomorphic observations, when, in fact, the divide distance to baselevel at either mountain front is steady in time. Like most paradoxes, the disconnect between the topography, the uplift rates, and the erosion rates in the Northern Apennines is simply an issue of the conceptual framing of the problem. In landscapes characterized by asymmetric topography, the direction of horizontal advection and balance between vertical and horizontal rock motion are manifest in the morphology of river profiles, the pattern of asymmetric topography, and as we have shown here, our interpretation of detrital 10Be concentrations in terms of a rock flux that has non-vertical components.

ACKNOWLEDGMENTS

This research is funded by the SINERGIA Swiss Alp Array project (SNF number 154434).

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24 N N N N 5 4 5 4 4 4 4 4 E 2 E 1 2 ** 1 21 a a Adriatic Adriatic n n g g ** ** o o l l 20 19 o o B B Ligurian e e ** c c 9 E 18 n n Erosion Rate (mm/yr) Rate (mm/yr) Erosion E 1 e e 1 1 r 0.10 0.60 r 1 o o l l F F 17 8 a a m m χ (m) # a r a r 16 7 P P # 6 15 14 0 22 E E 13 0 0 5 1 1 # 12 m 4 m 11 a a 0 0 l l 5 5 o o # s s 3 10 a s a s 5 5 a 2 2 n n a 1 # o o o o E E 2 n B B n 9 9 e e B D 0 0 G G E 2 E 1 2 1 a a n n g g ) o o 0.9 l l o o (m B B sn K Allochthonous Jurassic to e e c c E n n E 1 e e 0 279 1 1 r r uscan metamorphic rocks 1 Marnoso Arenacea Unit- turbidites foredeep Miocene Macigno/Cervarola Units- turbidites foredeep to Mid-Miocene Ligurian Unit - sedimentary deepwater Cenozoic early and ophiolites mélange - Epiligurian deposits and continental shallow marine T o o l l F F a a D m m a r a r P P C E E 0 0 1 1 m B m a 0 0 l 5 5 a l o s o s a s A 5 5 a s 2 2 n a a n o o o E E o B n n 9 9 B e e C A 0 0 G G N N N N 5 4 5 4 4 4 4 4

25 Figure 1. A: Simplified geologic map of the Northern Apennines and 15 km-wide swath profiles (black rectangles). Letters (A-D) on swath profiles correspond with the arrangement of the swath profiles in Figure 2. The Ligurian Unit is divided into the Internal Ligurian (IL) Unit (dark green color) and External Ligurian (EL) Unit (light green color).The primary drainage divide (solid black line) and thrust front (sawtooth line) are indicated. B: Normalized length parameter, χ, mapped over catchments using a base level of 50 m. Location of all figures (red box), Adriatic Sea, and Ligurian Sea are shown in the inset image.C: ksn map of catchments using a 1 km smoothing window. D: Compilation of catchment-averaged erosion rates from this study, Cyr and Granger (2008) (numbers with ** symbol), and Wittmann et al. (2016) (numbers with # symbol).

A 2 SW NE 3 Erosion Rate (mm/yr) Reference Velocity 2 (2 mm/yr) 1

Elevation (km) 1

0 0 B Uplift/Erosion Rate (mm/yr) 2 3

2

1

1 Elevation (km)

0 0 C 2 3 Erosion Rate (mm/yr)

2 1 1 Elevation (km)

0 0

D Uplift/Erosion Rate (mm/yr) 2 3

2 Reno 1 1 Elevation (km)

0 0 50 100 Distance (km) 26 § § 6.5 9.3 6.6 9.1 6.5 1.66 1.98 3.43 2.31 5.14 3.06 1.84 5.29 9.56 21.1 12.0 16.4 12.2 13.7 N.D N.D ------velocity velocity (mm/yr) Horizontal Horizontal ) ) vert § § 2 5.4 7.0 8.6 8.1 30.9 12.2 19.3 29.5 59.6 20.8 64.1 19.5 11.0 31.3 42.4 35.7 20.0 38.8 29.0 N.D N.D (km Projected catchment area (A § § 16° 16° 35° 35° 35° 35° 35° (°) 215° 215° 215° 196° 196° 196° 215° 215° 215° 215° 215° 215° N.D N.D vector Velocity Velocity orientation † § § § N.D N.D N.D (mm/yr) Erosion rate Erosion 0.277 0.109 ± 0.330 0.051 ± 0.382 0.072 ± 0.435 0.076 ±

0.119 0.014 ± 0.104 0.017 ± 0.217 0.031 ± 0.199 0.027 ± 0.293 0.041 ± 0.194 0.023 ± 0.138 0.021 ±

0.229 0.042 ± 0.415 0.206 ± 0.598 0.120 ± 0.519 0.129 ± 0.694 0.242 ± 0.372 0.061 ± 0.234 0.041 ± § § § *† mf N.D N.D N.D 0.047 0.049 0.047 0.048 0.046 0.044 0.045 0.049 0.048 0.048 0.045 0.044 0.046 0.046 0.047 0.048 0.046 0.044 P

§ § § *† ms N.D N.D N.D 0.018 0.019 0.017 0.018 0.016 0.015 0.016 0.019 0.018 0.018 0.016 0.015 0.017 0.017 0.017 0.018 0.017 0.015 (at/g/yr) P § § § *† n N.D N.D N.D 8.469 9.858 7.999 8.836 7.436 6.362 7.098 9.802 8.680 8.965 6.664 6.205 7.875 7.516 8.209 9.113 7.534 6.519 P

(mm/yr) Erosion rate rate Erosion 0.274 0.109 ± 0.298 0.046 ± 0.401 0.073 ± 0.430 0.076 ± 0.268 0.048 ± 0.442 0.224 ± 0.603 0.123 ± 0.497 0.124 ± 0.643 0.230 ± 0.358 0.058 ± 0.479 0.100 ± 0.311 0.037 ± 0.583 0.112 ± 0.108 0.013 ± 0.105 0.016 ± 0.216 0.032 ± 0.193 0.026 ± 0.284 0.036 ± 0.190 0.022 ± 0.136 0.021 ± 0.254 0.044 ± ) ) 2 plan area 122.0 680.5 308.3 264.2 614.3 917.6 480.5 992.1 989.9 132.5 179.2 190.1 296.8 555.4 946.8 251.7 316.2 149.4 264.2 Basin (km (A 1248.9 1160.5 840 877 848 883 (m) 685 843 716 743 819 665 598 627 591 558 521 619 985 825 885 559 641 Mean Elevation * mf 0.047 0.048 0.047 0.048 0.046 0.047 0.046 0.046 0.047 0.045 0.045 0.045 0.045 0.044 0.044 0.045 0.049 0.047 0.048 0.044 0.045 P

* ). ms mf 0.018 0.018 0.018 0.018 0.016 0.018 0.017 0.017 0.017 0.016 0.016 0.016 0.016 0.016 0.015 0.016 0.019 0.018 0.018 0.016 0.016 (at/g/yr) P * n 8.355 8.800 8.462 8.719 P 7.390 8.451 7.594 7.815 8.369 7.229 6.733 6.921 6.750 6.679 6.403 7.088 9.481 8.403 8.767 6.546 7.163 Be] (P muons fast and ) at/g) 3 ms 10 [ 9.0 1.5 ± 9.2 2.8 ± 8.4 1.3 ± (10 21.6 7.5 ± 20.8 2.4 ± 14.9 2.3 ± 14.3 2.0 ± 13.5 5.7 ± 11.2 2.4 ± 14.5 1.8 ± 10.2 1.9 ± 16.1 0.8 ± 44.7 2.5 ± 44.6 4.8 ± 23.6 2.4 ± 34.3 2.9 ± 20.9 2.0 ± 32.5 2.0 ± 35.0 3.7 ± 20.3 2.7 ± 19.8 2.8 ± (°E) 9.6118° 8.8598° 9.5849° 9.3616° 9.8569° 9.9258° 9.0576° 10.1692° 10.9240° 10.2381° 10.0913° 10.4083° 10.7571° 11.2573° 11.6213° 11.6865° 11.8849° 10.3762° 10.5060° 10.5539° 11.1258° Longitude ), slow muons (P muons slow ), n BE CONCENTRATIONS, EROSION RATES, AND HORIZONTAL VELOCITY CALCULATIONS CALCULATIONS VELOCITY AND HORIZONTAL RATES, EROSION CONCENTRATIONS, BE 10 (°N) Latitude 44.8327° 44.6322° 44.4201° 44.5716° 44.7256° 44.9061° 44.6953° 44.6222° 44.5322° 44.3923° 44.2211° 44.1698° 44.1187° 44.3518° 44.1950° 44.1867° 44.1360° 43.9299° 43.9993° 43.9278° 44.8926° ** ** ** ** Be production rates for neutrons (P neutrons for rates production Be 10 # # # # # Nure Baganza Panaro Parma River/Location Scrivia Trebbia Taro Enza Secchia Basin) (Whole Reno Senio at Casola Valsenio Lamone at San Eufemia Davadola at Montone Entella Vara Magra Serchio at Filicaia at Serchio Serchio at Piaggone at Serchio Lima Bisenzio Staffora 2 3 4 5 6 7 8 9 1 Catchment sampled by Cyr and Granger (2008). Granger and by Cyr sampled Catchment 11 12 13 16 17 19 20 21 Basin-averaged in catchment. distribution quartz uneven for corrected rates erosion and rates Production not determined. = N.D. 14 15 18 10 Catchment sampled by Wittmann et al. (2016). (2016). al. et by Wittmann sampled Catchment TABLE 1. CATCHMENT METRICS, TABLE * † § ** II. LiguriaII. and Tuscany III. Emilia Romagna III. I. Piemonte #

27 Figure 2. Swath profiles extending near to or from the Ligurian coastline to the Po Plain. The shaded boxes show erosion rates for the Ligurian side (blue) and Adriatic side (red); the height of the box indicates the erosion rate with uncertainties, and the width of box shows the distance along the profile where the rate is applicable. Dashed lines show geodetic uplift rates (D’Anastasio et al., 2006), and arrows above the profile show horizontal velocities from GPS measurements (Bennett et al., 2012). Outlined arrows indicate GPS stations located on the profile, and hatched arrows indicate the station and velocity vector were projected onto the profile from a maximum distance of 15 km.

A

SW EVS EVS NE EVW

V SVE S W Corsica SVE Zone

Material Spatial Hinge EVS Point Feature +V E W E Zero Velocity

Velocity

Frame Reference Velocity S Subduction Hinge Eurasia

B V SVW SVW = 0 S W Retro Pro Retro Pro Retro Pro 1

09

08

Acceptable 0 Range of sVw

0 Retrowedge Average UP 0

0

0 Uplift (mm/yr) Prowedge 0

01

0 10 8 0 8 10 1 1 1 18 01 Average UR

0 Horizontal Velocity of Wedge Relative to Surface (mm/yr)

28 Figure 3. A. Kinematic model of the Apenninic orogenic wedge driven by subduction with slab hinge retreat and crustal accretion. The inset illustrates the model components: Eurasia (E) and the orogenic wedge (W), and two spatial features: the slab hinge zone (H) and Earth’s surface (S). Velocity is designated using plate notation, where AVB signifies the velocity of object B relative to object or reference frame A. Horizontal velocities are given in one of two reference frames, where red vectors are in a reference frame fixed to the slab hinge and blue vectors are in a Eurasia-fixed reference frame. Vertical velocities are always relative to the geoid. Solid arrows indicate the velocity of material points (rock or geodetic monuments), and hatched arrows indicate spatial feature velocities (slab hinge, Earth’s surface). Circles indicate zero velocity. Motion between Adria and Europe in the direction of the profile is neglected. Internal deformation of the wedge is neglected in the horizontal, but the prowedge and retrowedge are given independent vertical velocities. B. Summary of material velocities and erosion rates across the Apenninic wedge assuming topographic steady state. Data points along y axis and x axis show concentration-based calculations of vertical and horizontal velocities, respectively, for the prowedge (gray circles) and retrowedge (green circles). Solid lines are fit to mean prowedge (gray line) and retrowedge (green line) velocities (square symbols). Uplift estimates from geodetic measurements for the prowedge (UP) and retrowedge (UR) are shown as light gray horizontal bands. Acceptable range of horizontal and vertical velocities for the kinematic model is highlighted in red.

Schematic orogenic wedges show expected wedge morphologies for a symmetric wedge (SVW = 0) and for asymmetric wedges given the direction of SVW.

29 METHODS

χ and ksn calculations

We performed χ analysis on a SRTM 90 m DEM, using Matlab scripts based on Topotoolbox

METHODS2 functions (Schwanghart and Scherler, 2014). We integrate upstream from a base level of 50

χ and ksn calculationsm to avoid including the Po Plain or smaller intermontane basins on the Tyrrhenian side, We performed a χ analysis on a SRTM 90 m DEM, using Matlab scripts based on Topotoolbox 2 functions where the stream network geometry is often poorly constrained due to flat topography or (Schwanghart and Scherler, 2014). We integrate upstream from a base level of 50 m to avoid including the Po Plain or smallerephemer intermontaneal alluvial channels. basins onWe the incorporate Ligurian aside, channel wher concavitye the stream of 0.45 network (Kirby geometry and is often poorly constrained due to flat topography or ephemeral alluvial channels. We incorporate a channel concavity of 0.45 2 sn Whipple, 2012) and a channel initiation threshold area of 0.1 km for both2 χ and k (Kirby and Whipple, 2012) and a channel initiation threshold area of 0.1 km for both χ and ksn calculations. The equationscalculations. for χ and The steady equations state elevationfor χ and steady can be stat expressede elevation as canfollows: be expressed as follows:

(1) , (1) ( %& - 𝜒𝜒𝜒𝜒 = ∫(1 $%(())+ 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑′ (2) 8 (2) 6 - , 3 7%& where xb is the base level for χ integration,𝑧𝑧𝑧𝑧( 𝑑𝑑𝑑𝑑A)0 =is 𝑧𝑧𝑧𝑧a( reference𝑑𝑑𝑑𝑑 ) + $ drainage+ 𝜒𝜒𝜒𝜒 area, A is upstream drainage area, m and n are empiricalwhere xb is constants, the base level U is forrock χ integration,uplift due toA 0tectonic is a references, K is drainagethe rock area,erodibility A is upstream constant, and z is elevation. χ maps and χ plots present an image of the current state of the fluvial landscape (Willett et al., drainage area, m and n are empirical constants, U is rock uplift due to tectonics, K is the rock 2014). On a χ map, a stable drainage network geometry is illustrated as equal χ values across drainage divides separatingerodibility adjacent drainageconstant, basins.and z is Contrasts elevation. in χ mapsχ values and betwχ plotseen present neighboring an image drainage of the current basins indicate geometric differences that suggest a transient river network, where one basin is gaining area at the expense state of the fluvial landscape (Willett et al., 2014). On a χ map, a stable drainage network of the adjacent basin. We note that changing the base level to sea level does not produce a different pattern between thegeometry Adriatic is andillustrated Ligurian as equalsides χ(Giachetta values across and drainage Willett, divides2018). separating adjacent drainage

basins. Contrasts in χ values between neighboring drainage basins indicate geometric We estimate ksn from χ and elevation data following equation (2), where ksn represents the slope (Hilley and differences that suggest a transient river network, where one basin is gaining area at the Young, 2018; Schwanghart and Scherler, 2014) of the data, using a smoothing window of 1 km. Ksn values range from 0 to 279 m0.9 for the studied basins, with the headwaters generally displaying the highest k expense of the adjacent basin. We note that changing the base level to sea level does not sn values (Fig. 1C). produce a different pattern between the Adriatic and Tyrrhenian sides (Giachetta and Willett,

Catchment-averaged2018). (vertical) erosion rates from 10Be concentrations We collected samples upstream of the mountain front on the Adriatic side or upstream of major basins on the Ligurian side, in order to avoid mixing of older fluvial sediments that may have undergone radioactive decay dueWe to long-termestimate k snstorage from χ times. and elevation We sampled data following trunk channels equation or (tributaries2), where kofsn represents11 catchments the with no or 2 minimal glacialslope (Hilley impact and (von Young, Blanckenburg, 2018; Schwanghart 2005) and and are Scherler, of a sufficient 2014) of size the (> data, 100 using km ) ato outweigh the effects of landsliding (Niemi et al., 2005).

Samples were processed and analyzed at ETH Zürich labs and AMS facilities. Sample preparation and 10Be analyses were carried out following the method of Lupker et al. (2012), using grain sizes between 125-710 µm (Supplementary Table 1). The 10Be data were calibrated with the primary standard ICN 01-5-1 that has a nominal 10Be/9Be ratio of 2.709 x 10-11 (Nishiizumi et al., 2007). Average procedural blank values for 10Be/9Be (8.34 ± 1.59 x 10-15) were determined from multiple AMS runs and were propagated through all calculations.

The catchment-averaged erosion rate reflects the loss and the time required to erode through one cosmic ray penetration length, or approximately 60 cm of quartz (Masarik and Reedy, 1995). For the average concentration of 10Be to reflect an erosion rate for the catchment, the source of quartz should be evenly distributed throughout the catchment; sediment is well-mixed and representative of the entire catchment; 30 to residence time on the hillslopes (Granger et al., 1996). The cosmogenic nuclide

concentration at the surface is inversely proportional to the erosion rate e

(3)

1 𝑃𝑃𝑃𝑃? 𝑃𝑃𝑃𝑃AB 𝑃𝑃𝑃𝑃AC 𝑁𝑁𝑁𝑁 = ⋅ = ? + AB + ACD ℇ 𝜇𝜇𝜇𝜇 𝜇𝜇𝜇𝜇 𝜇𝜇𝜇𝜇

where P is nuclide production due neutrons (Pn), slow muons (Pms), and fast muons (Pmf), and

3 to residenceµi is the average time on density the hillslopes of quartz (Granger(2.65 g/cm et) al.,divided 1996) by. theThe effective cosmogenic attenuation nuclide length and sediment transport time is short relative to residence time on the hillslopes (Granger et al., 1996). The 2 2 cosmogenicconcentration nuclide(Li). We concentration assumed at the surfaceeffective at the is attenuation surfaceinversely is lengthsinverselyproportional of 160proportional g/cmto the forerosion neutronsto the rate erosion ( Len ), 1500 rate g/cmε

2 for slow muons (Lµs), and 4320 g/cm for fast muons (Lµf) (Braucher et al., 2011). (3) (3) 1 𝑃𝑃𝑃𝑃? 𝑃𝑃𝑃𝑃AB 𝑃𝑃𝑃𝑃AC 𝑁𝑁𝑁𝑁 = ⋅ = ? + AB + ACD 10 ℇ 𝜇𝜇𝜇𝜇 𝜇𝜇𝜇𝜇 𝜇𝜇𝜇𝜇 where P is nuclideSea levelproduction high latitude due neutrons (SLHL) (PBen), production slow muons rates (P werems), and scaled fast for muons latitude (P andmf), altitudeand μi is the 3 average density of quartz (2.65 g/cm ) divided by the effective attenuation length (Λi). We assumed effective whereaccording P is nuclide to Stone production2 (200), using due SLHL neutrons production (Pn), slowrates2 ofmuons 4.01 (at/g/yr)(Pms), and for fastspallation muons (Pmf),2 and attenuation lengths of 160 g/cm for neutrons (Λn), 1500 g/cm for slow muons (Λμs), and 4320 g/cm for fast muons (Λ ) (Braucher(Borchers et et al., al., 2011).2016), 0.012 (at/g/yr) for slow3 muons (Braucher et al., 2011), and 0.039 steepμfµi is (> the 60 average°) slopes density (DiBiase, of quartz 2018) .(2.65 For method g/cm ) dividedconsistency, by the erosion effective rates attenuation from previous length (at/g/yr) for fast muons (Braucher et al., 2011). 2 2 Sea levelstudies high(Li) .latitude We(Cyr assumed and (SLHL) Granger, effective 10Be 2008; production attenuation Wittmann rates lengths et were al., of2016)scaled 160 wereforg/cm latitude also for recalculatedneutrons and altitude (L nusing), accor 1500 theding g/cm to Stone

(2000), using SLHL production rates of 4.01 (at/g/yr)2 for spallation (Borchers et al., 2016), 0.012 (at/g/yr) for methodfor slow of Lupkermuons (etL µal.s), (2012)and 4320. g/cm for fast muons (Lµf) (Braucher et al., 2011). slow muons (BraucherWe determined et al., basin2011),-wide and production 0.039 (at/g/yr) rates usingfor fast equation muons 4 , (Brauchermodified from et al., Lupker 2011). et al

(2012): We determined basin-wide production rates using equation 4, modified from Lupker et al (2012): Sea level high latitude (SLHL) 10Be production rates were scaled for latitude and altitude The geology of the Northern Apennines is primarily comprised of marine and continental (4) clasticaccording sedimentary to Stone units (200), (see using Figure SLHL 1A) production, although some rates lithologiesof 4.01 (at/g/yr) have afor significant spallation (4) 1 𝑃𝑃𝑃𝑃 = F 𝑃𝑃𝑃𝑃(𝑑𝑑𝑑𝑑, 𝑦𝑦𝑦𝑦) 𝑆𝑆𝑆𝑆 I where S proportionis (Borchersbasinwhere area, of Set andcarbonate,is al., basin P(x,y) 2016) area, ,isshale, 0.012and production P(x,y) or (at/g/yr) ophiolites is rateproduction for at slow (Zattineach rate muonspo e atintt al.,each in (Braucher 2002;the point basin. Ricciin theet The al.,Lucchi,basin production 2011). The 1986), productionand rate that0.039 was determinedcontribute(at/g/yr) on ratea per was littlefor pixel determinedfast to basis nomuon quartz using son (Braucher a to anper the SRTM pixel sand et basis al.,90-sized m2011)using Digital fraction. an. SRTM Elevation We 90 corrected m ModelDigital the(DEM)Elevation erosion clipped Model rates to for the area of each catchment. We excluded the glacier correction and topographic shielding from the original equation of Lupker etthese al., (2012),heterogeneous(DEM) clippedas there tocatchments is the no area ice coverof byeach digitizing in catchment our study quartz. We area excluded-poor (Armstrong lithologies the glacier et al., from correction 2005), 1:100,000 and and recent scale studies have shown that topographic shielding corrections are inappropriate for most catchments, except those with geologicWe topographicdetermined maps. These shieldingbasin lithologie-wide from production thes were original clipped rates equation usingfrom of equationtheLupker catchment et 4al.,, modified (2012), DEM asprior from there toLupker is calculating no ice et al steep (> 60°) slopes (DiBiase, 2018). For method consistency, erosion rates from previous studies (Cyr and cover in our study area (Armstrong et al., 2005), and recent studies have shown that Granger,the 2008;(2012): pixel Wittmann by pixel etproduction al., 2016) rates.were alsoThe recalculatederosion rate calculationsusing the method were ofthen Lupker performed et al. (2012).as topographic shielding corrections are inappropriate for most catchments, except those with described above. Published erosion rates were also corrected for heterogeneous quartz The geology of the Northern Apennines is primarily comprised of marine and continental clastic sedimentary (4) units (seedistribution Figure 1A), if althoughnecessary. some Adjusted lithologies production have1 a rates, signific basinant proportionarea, and erosion of carbonate, rates are shale, given or in ophiolites (Zattin et al., 2002; Ricci Lucchi, 1986)𝑃𝑃𝑃𝑃 = that F contr𝑃𝑃𝑃𝑃(𝑑𝑑𝑑𝑑ibute, 𝑦𝑦𝑦𝑦) little to no quartz to the sand-sized fraction. Supplementary Table 1. We found this correction𝑆𝑆𝑆𝑆 I adjusted the erosion rates by an average of We correctedwhere the Serosion is basin rates area, for and these P(x,y) heterogeneous is production catchments rate at each by digitizingpoint in the quartz-poor basin. The lithologies production from 1:100,0005%, ratescale indicating was geologic determined the maps. robustness onThese a per lithologiesof pixel our originalbasis were using calculations, clipped an SRTM from and 90the mjustifying catchment Digital Elevationthe DEM use priorof Modelthe to calculating the pixel by pixel production rates. The erosion rate calculations were then performed as described above. original erosion rates and full catchment area for calculating the horizontal velocities Published erosion(DEM) ratesclipped were to thealso area corrected of each for catchment heterogeneous. We excluded quartz distribution the glacier if correction necessary. and Adjusted productiondescribedtopographic rates, basin below shieldingarea,. and erosionfrom the rates original are given equation in Su ofpplementary Lupker et al., Table (2012), 1. We as found there isthis no correction ice adjusted the erosion rates by an average of 5%, indicating the robustness of our original calculations, and justifying thecover use in of our the study original area erosion (Armstrong rates and et al., full 2005) catch, mentand recent area for studies calculating have shown the horizontal that velocities described below. 10 Catchmenttopographic-averaged shielding horizontal corrections velocities are inappropriate from Be for concentrati most catchments,ons except those with

The horizontal rock velocity ( ) can be expressed in terms of the conventional vertical Catchment-averaged horizontal velocities from 10Be concentrations The horizontalerosion rock rate velocity ( ) as: (v) can be𝑣𝑣𝑣𝑣 expressed in terms of the conventional vertical erosion rate (ė) as:

ė (1) (5) PQRST v = ė* PUVWX where Aplan is the conventional, downward-projected catchment area, and the area of the where Aplan is the conventional, downward-projected catchment area, and the area of the catchment projected 31 catchment projected onto a vertical surface is calculated as . A basin projected onto a

Z[\] vertical plane will invariably be smaller than the projection Aonto a horizontal plane, so a onto a vertical surface is calculated as Avert. A basin projected onto a vertical plane will invariably be smaller than the projection onto a horizontal plane, so a given 10Be concentration implies a much larger horizontal velocity compared to the conventional, vertical erosion rate.

Kinematic Model Setup The kinematic model expresses vertical motion with respect to the geoid (vertical blue vectors). Horizontal velocity is expressed in two reference frames: relative to Eurasia (blue vectors) and relative to the subducting slab hinge (red vectors) (Fig. 3A). We also differentiate between the movement of spatial features, in particular the slab hinge and the Earth’s surface (diagonal pattern), and material features such as rock (solid colors) (Fig. 3A). We assign all internal deformation of the wedge to its boundary, allowing us to express the problem in terms of two blocks, Eurasia (E) and the orogenic wedge (W), and two spatial features, the slab hinge zone (H) and the Earth’s surface (S) (inset image, Fig. 3A). The topographic form is assumed to be in steady state in size and shape, with topography moving with the slab hinge. Retreat of the Adriatic slab and its hinge zone occur relative to Eurasia, and we express both the distal upper and lower plates as Eurasia by neglecting the Apenninic trench-normal component of Adria-Eurasia motion. We prescribe a single horizontal velocity to the wedge, although we assume independent rock uplift rates for the prowedge (UP) and the retrowedge (UR). We assume that elastic strain accumulation on faults is at a short wavelength and can neglected, allowing us to use horizontal GPS velocities to estimate the wedge velocity

(EVW). Finally, our assumption of steady state allows the rock uplift rates from the geodetic measurements to constrain the vertical velocities UP and UR.

REFERENCES CITED Armstrong, R., Raup, B., Khalsa, S.J.S., Barry, R., Kargel, J., Helm, C., and Kieffer, H., 2005, GLIMS glacier database: National Snow and Ice Data Center, Boulder, Colorado, USA,. von Blanckenburg, F., 2005, The control mechanisms of erosion and weathering at basin scale from cosmogenic nuclides in river sediment: Earth and Planetary Science Letters, v. 237, p. 462–479, doi: 10.1016/j. epsl.2005.06.030. Borchers, B., Marrero, S., Balco, G., Caffee, M., Goehring, B., Lifton, N., Nishiizumi, K., Phillips, F., Schaefer, J., and Stone, J., 2016, Geological calibration of spallation production rates in the CRONUS-Earth project: Quaternary Geochronology, v. 31, p. 188–198, doi: 10.1016/j.quageo.2015.01.009. Braucher, R., Merchel, S., Borgomano, J., and Bourlès, D.L., 2011, Production of cosmogenic radionuclides at great depth: A multi element approach: Earth and Planetary Science Letters, v. 309, p. 1–9, doi: 10.1016/j. epsl.2011.06.036. Cyr, A.J., and Granger, D.E., 2008, Dynamic equilibrium among erosion, river incision, and coastal uplift in the northern and central Apennines, Italy: Geology, v. 36, p. 103–106, doi: 10.1130/G24003A.1. DiBiase, R.A., 2018, Short communication: Increasing vertical attenuation length of cosmogenic nuclide production on steep slopes negates topographic shielding corrections for catchment erosion rates: Earth Surf. Dynam., v. 6, p. 923–931, doi: 10.5194/esurf-6-923-2018. Giachetta, E., and Willett, S.D., 2018, A global dataset of river network geometry: Scientific Data, v. 5, p. 180127, doi: 10.1038/sdata.2018.127. Granger, D.E., Kirchner, J.W., and Finkel, R., 1996, Spatially Averaged Long-Term Erosion Rates Measured 32 from in Situ-Produced Cosmogenic Nuclides in Alluvial Sediment: The Journal of Geology, v. 104, p. 249–257, doi: 10.1086/629823. Hilley, G.E., and Young, H.H., 2018, Millennial-scale denudation rates of the Santa Lucia Mountains, California: Implications for landscape evolution in steep, high-relief, coastal mountain ranges: GSA Bulletin, v. 130, p. 1809–1824, doi: 10.1130/B31907.1. Kirby, E., and Whipple, K.X., 2012, Expression of active tectonics in erosional landscapes: Journal of Structural Geology, v. 44, p. 54–75, doi: 10.1016/j.jsg.2012.07.009. Lupker, M., Blard, P.H., Lavé, J., France-Lanord, C., Leanni, L., Puchol, N., Charreau, J., and Bourlès, D., 2012, 10Be-derived Himalayan denudation rates and sediment budgets in the Ganga basin: Earth and Planetary Science Letters, v. 333–334, p. 146–156, doi: 10.1016/j.epsl.2012.04.020. Masarik, J., and Reedy, R.C., 1995, Terrestrial cosmogenic-nuclide production systematics calculated from numerical simulations: Earth and Planetary Science Letters, v. 136, p. 381–395, doi: 10.1016/0012-821X(95)00169-D. Niemi, N.A., Oskin, M., Burbank, D.W., Heimsath, A.M., and Gabet, E.J., 2005, Effects of bedrock landslides on cosmogenically determined erosion rates: Earth and Planetary Science Letters, v. 237, p. 480–498, doi: 10.1016/j.epsl.2005.07.009. Nishiizumi, K., Imamura, M., Caffee, M.W., Southon, J.R., Finkel, R.C., and McAninch, J., 2007, Absolute calibration of 10 Be AMS standards: Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, v. 258, p. 403–413, doi: 10.1016/j.nimb.2007.01.297. Ricci Lucchi, F., 1986, The Oligocene to Recent foreland basins of the northern Appennines: Foreland Basins, p. 105–140. Schwanghart, W., and Scherler, D., 2014, Short Communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences: Earth Surf. Dynam, v. 2, p. 1–7, doi: 10.5194/esurf-2-1-2014. Willett, S.D., McCoy, S.W., Taylor Perron, J., Goren, L., and Chen, C.Y., 2014, Dynamic reorganization of River Basins: Science, v. 343, doi: 10.1126/science.1248765. Wittmann, H., Malusà, M.G., Resentini, A., Garzanti, E., and Niedermann, S., 2016, The cosmogenic record of mountain erosion transmitted across a foreland basin: Source-to-sink analysis of in situ 10Be, 26Al and 21Ne in sediment of the Po river catchment: Earth and Planetary Science Letters, v. 452, p. 258–271, doi: 10.1016/j.epsl.2016.07.017. Zattin, M., Picotti, V., and Zuffa, G.G., 2002, Fission-Track Reconstruction of the Front of the Northern Apennine Thrust Wedge and Overlying Ligurian Unit: v. 302, p. 346–379.

33 A 1400

1200

1000

800

Elevation (m) 600

400

200

0 4 8 12 16 20 20 16 12 8 4 0 Serchio (m) Reno (m) B 1400

1200

1000

800

Elevation (m) 600

400

200

0 4 8 12 16 20 20 16 12 8 4 0 Entella (m) Trebbia (m)

Figure DR1. Selected χ plots illustrating contrast in ksn (slope of the χ plot) and χ values across the

main divide, where Ligurian Rivers (left side, A and B) show overall higher ksn values and lower χ values at the headwaters compared with Adriatic Rivers (right side, A and B). Pink, thick lines show the trunk channel for each catchment. Hillshade river catchments and channels (black lines) are shown to scale.

34 Avert

Aplan θ

Figure DR2. Catchment surface projected onto a horizontal (Aplan) or vertical (Avert) plane. A vertical erosion rate (as in conventional analysis) is calculated by assuming the fl ux of 10Be out of the surface is purely in the vertical direction, with the catchment surface projected onto plane Aplan. A horizontal velocity is calculated by assuming the fl ux of 10Be out of the surface is purely in the horizontal direction, with the catchment surface projected onto plane Avert.

35 Retrowedge Elevation (km) Prowedge

UP

SVW SVW 1.4 5) Magra 10) Trebbia 1.2 3) Entella 12) Taro 1.6 1 1.6 88 m 0.9

0.9 1.2 1.2 47 m 0.8 104 m0.9 0.8 0.8 57 m 0.9 Elevation (km) Elevation (km) 0.6 0.4 0.4

0 0.4 0 0 5 10 15 20 20 15 10 5 0 (m) Channel pro le (m) 0.2 Direction of rock motion = 1 mm/yr 0 0 20 40 60 80 100 120 100 80 60 40 20 0 Distance (km) Figure DR3. Selected trunk river channel profi les (solid, colored lines) for two retrowedge rivers and two prowedge rivers. Rivers names and numbers correspond to basins shown in Table 1. Figure illustrates scaled horizontal and vertical velocities from kinematic model solution. River channel profi les with longer channel lengths are predicted to have higher rates of river incision (vertical rock motion) than rates of horizontal advection, while river channel profi les with shorter channel lengths are predicted to have higher rates of

horizontal advection (Willett et al., 2018). Inset images show corresponding χ plots and average ksn values

(channel slope) for each river, illustraing higher ksn values for retrowedge rivers relative to prowedge rivers.

36 CHAPTER 3 Exhumation and denudation of the Northern Apennines, Italy: an updated overview of and new insights from low-temperature thermochronometers

Erica D. Erlanger, Maria Giuditta Fellin, Sean D. Willett

37 Abstract Analysis of new detrital apatite fission-track ages from modern river sands, published bedrock and detrital apatite fission-track ages, and bedrock apatite (U-Th)/He ages in the Northern Apennines provide an updated overview on the spatial variability of denudation rates through time. The pattern of long-term denudation rates derived from the apatite (U-Th)/He ages illustrates a lower denudation rate on the Ligurian side relative to apatite fission-track rates. This is consistent with the short-term pattern of slower denudation on the Ligurian side, relative to the Adriatic side, as constrained by cosmogenic 10Be nuclide data on quartz grains from modern river sands. Given new constraints on 10Be denudation rates and slab retreat rates in the Northern Apennines, we find that modeled cooling ages replicate the pattern of measured cooling ages across the Northern Apennines and illustrate a large component of horizontal motion on the retrowedge relative to the prowedge.These results imply that horizontal motion continues to be important in the Northern Apennines for controlling modern denudation rates and that the asymmetry of the orogen is long-lived.

3.1 Introduction The Apennines mountains of Italy are an active orogen characterized by contemporaneous extensional and compressional tectonics. In the Northern Apennines, these features are linked to rollback of the Adriatic slab beneath Eurasia, suggested to be active since the Oligocene (Malinverno and Ryan, 1986). The interplay between extension and compression has affected the overall tectonic evolution of the Northern Apennines and, in particular, its exhumational and topographic evolution. Low-temperature bedrock and detrital thermochronology studies in the Northern Apennines have constrained the timing and rates of exhumation at the orogen-scale (e.g. Thomson et al., 2010; Malusà and Balestrieri, 2012) and at the regional scale along the extensional retroarc of the orogen (Ligurian side; e.g. Fellin et al., 2007) and along the frontal fold-and-thrust belt (Adriatic side; e.g. Balestrieri et al., 1996; Zattin et al. 2002; Carlini et al., 2013). Age-elevation profiles and multiple thermochronometers have revealed spatially variable exhumation across and along strike of the orogen, and temporal variability in exhumation rates. While these findings have improved our understanding of the evolution of the Northern Apennines, a comparison between basement and detrital data and between exhumation patterns across the primary drainage divide is lacking.

In this paper, we review low-temperature bedrock and detrital thermochronologic studies in the Northern Apennines, and present the first regional comparison of long-term and short-term denudation rates. In addition to existing cooling ages, we present new detrital apatite fission-track ages from select drainage basins around the Northern Apennines. We use these data to propose an updated orogenic wedge model that is consistent with the pattern AHe, AFT, and ZHe coolong ages across the orogen, as well as the lower average denudation and slab retreat estimates constrained from recent studies on the Northern Apennines.

3.1.1 Structural Evolution Development of the Apenninic wedge began at ~30 Ma, due to convergence and southwest-directed subduction of the Adriatic microplate beneath Eurasia. From the Late Oligocene, sediments supplied largely by the Alps were deposited as turbidite sequences into a series of northward-migrating foredeep basins (Figure 1A; Macigno, Cervarola, and Marnoso-Arenacea Basins), which were eventually deformed and thrust during the Neogene (Ricci Lucchi, 1986). Until the Pliocene, these Tertiary foredeep basins were overridden by the Ligurian Unit (Figure 1A), a non-metamorphosed, allochthonous accretionary complex that was thrust upon the Tertiary foredeep deposits as a surficial nappe (Merla, 1952; Pini, 1999). Eocene-to-Pliocene basins 38 formed on top of the Ligurian Unit (Epi-Ligurian Unit; Ori and Friend, 1984; Cibin et al., 2001), which record discontinuous deposition of shallow-marine and continental sediments (Ricci Lucchi, 1986), and presently exist as denudational remnants above the Ligurian Unit. Today, the Ligurian and Epi-Ligurian Units are the highest structural units exposed in the Northern Apennines.

A

Mt. Cimone

Mt. Falterona

9°E 10°E 11°E Valdarno 12°E 9°E 10°E 11°E BA 9°E 10°E 11°E 10Be sampling112°E location2°E

N Detrital AFT sampling location °

5 tv upstream catchment area 4 45 °N Parma Common 10Be and detrital Parma AFT sampling location

Nure Scrivia Taro Bologna Trebbia Bologna Enza Secchia Genoa Magra Vara Panaro Reno

Serchio Lima

N La Spezia 4 4 44 °N Pescia Bisenzio

0 50 100 Florence km 0 25 50 75 100 m

Figure 1. A) Geologic Map of the Northern Apennines, primary drainage divide (black line), mountain front (dashed line) and thrust front (sawtooth line) (modified from Thomson et al., 2010). White dots and labels show locations of AER profiles from Thomson et al. (2010). B) Sample locations (red dots) and area coverage (pink polygons) for new and existing detrital AFT samples. Black dots illustrate locations sampled for 10Be catchment-wide denudation rates.

39 3.2 Thermochronology Review

3.2.1 Major Concepts A number of methods exist for determining denudation rates over various timescales, including sediment loads (1-10 years), cosmogenic nuclides (103-104 years), and thermochronologic methods (106 years). Thermochronologic methods are based on the production and retention of either isotopes or radiation damage through radioactive decay. The retention of these decay products depends on the thermal history of the sample and on the diffusion kinetics, which vary according to the mineral used and mode of decay. Low-temperature thermochronometers, such as (U-Th)/He and fission-track ages on apatite, are used to resolve thermal histories for samples buried below temperatures of 200˚C (Reiners and Brandon, 2006). Detrital minerals, which were buried during or after deposition to temperatures high enough to cause diffusion of all decay products, will be thermochronologically reset and will record only the post-burial thermal history. If burial temperatures were not high enough, then the detrital minerals will be only partially or completely non-reset and will preserve the record of their pre-burial thermal history.

Thermochronometers can additionally be distinguished based on whether detrital or bedrock samples are used. As bedrock samples are extracted from an intact lithologic unit, the cooling age can then be compared with known stratigraphic ages to assess, for instance, whether a sample has been reset. For detrital samples, the age distribution within a single sample of well-mixed fluvial sediment reflects a much larger area than a bedrock sample, if the dated mineral species are similarly distributed within the exposed rocks. Moreover, like the cosmogenic 10Be detrital data, detrital cooling ages can be used to derive a catchment-averaged denudation rate and also to identify variable denudation rates within catchments, by deconvolving the age distribution into populations or peaks.

3.2.2 Application to the Northern Apennines Most cooling ages in the Northern Apennines are primarily limited to AFT and AHe methods (Balestrieri et al., 1996; Ventura et al., 2001; Zattin et al., 2002; Balestrieri et al., 2003; Fellin et al., 2007; Thomson et al., 2010; Malusà and Balestrieri, 2012; Carlini et al., 2013; Balestrieri et al., 2018), as the region is dominated by sedimentary rocks that have experienced relatively low burial temperatures of less than 200-250˚C (Reutter et al., 1983), sufficient to reset only the thermochronometers with the lowest closure temperatures. Non- reset clastic rocks in the Northern Apennines are commonly exposed close to the frontal thrust zone, near the mountain front, and at high elevations (e.g. Zattin et al., 2002; Thomson et al., 2010; Carlini et al., 2013). Thus, despite good spatial coverage of cooling ages in the study region, many samples in the Northern Apennines reflect ages that have not been reset during exhumation and are therefore not relevant for understanding the syn-orogenic thermal history.

On the Ligurian side of the northwestern Apennines, age elevation profiles and track-length distribution indicate rapid exhumation began at 8 Ma (Balestrieri et al., 1996). However, partially reset ages at high elevation are ~ 13 Ma in age, and could suggest an earlier, slow phase of exhumation. An initial exhumation age of 14 Ma is alternatively constrained by maximum burial depth and exhumation rate inverted for age (Ventura et al., 2001). Finally, the onset of exhumation is constrained by the present extent and depositional ages of the Epi-Ligurian Units on the Adriatic side, which commonly are not younger than the Tortonian; however, to the ESE, near Bologna, they can be as young as the Pliocene (Cibin et al., 2001).

40 Existing bedrock cooling ages <10 Ma from all thermochronmeters illustrate a younging trend in AFT and AHe ages towards the northeast, from the Ligurian side to the Adriatic side (Thomson et al., 2010). AHe data are more spatially limited to the region east of approximately 10˚30’ E, but reflect the same pattern as the AFT ages, albeit with a greater difference in ages across the divide. In contrast to the pattern of bedrock AFT ages, Malusa et al. (2010) found young, reset detrital AFT ages near the mountain front in all Adriatic catchments west of 11˚ E, with the exception of the Scrivia River (Figure 1B). The pattern of reset versus non-reset ages from multiple thermochonometers defines the reset front of the Northern Apennines. The AHe reset front is generally offset to the northeast relative to the AFT, except between 10˚30’ E and 11˚30’ E, where the two reset fronts coincide (Thomson et al., 2010). The pattern of cooling ages and the location of the reset fronts has been explained using an orogenic wedge model (Thomson et al., 2010). In this model, the Apennines are approximated as a doubly tapering orogenic wedge, where the Adriatic and Ligurian sides of the orogen can be defined as the accreting prowedge and non-accreting retrowedge of the orogen, respectively (e.g. Willett et al., 2001). The Thomson et al. (2010) model has a symmetric retrowedge and prowedge, frontal accretion of a 10-km thick column of material at a flux of 17 km/My, and vertical surface denudation rates of 0.3 mm/yr (Ligurian side) and 1 mm/yr (Adriatic side). Using only frontal accretion to account for incoming crust, the model is able to reproduce offset AHe and AFT reset fronts, although it cannot account for the area where the two reset fronts coincide. Additionally, the model does not address temporal changes in surface exhumation rates and different exhumation paths (e.g. prowedge underplating). We contribute detrital AFT cooling ages for the Ligurian side, west of Florence, and reproduce the orogenic wedge model to address some of these knowledge gaps.

3.3 Methods

3.3.1 Thermochronologic Dating Bulk samples of modern sand were collected from eight locations along six different rivers in on the Ligurian side of the Northern Apennines (Figure 1B). These samples are sourced from the Macigno, Alpi Apuane, and Ligurian Units. As some Ligurian catchments such as the Magra and Serchio Rivers contain basins with Pliocene sediments of the Villafranchian Unit, additional samples (Magra2 and Serchio) were collected in tributaries above these basins to avoid sampling the younger, syn-orogenic sediments.

Samples were processed according to the external detector method for apatite fission track dating (Reiners and Brandon, 2006; Gallagher et al., 1998). Samples were sieved to obtain grains <500 μm and gravity separated using a Wilfley Table. Apatites were separated using heavy liquids, magnetic separation with the Frantz Magnetic Separator, and manual removal of grains (as needed). Apatite grains were then mounted, polished, and chemically etched for 20 s in HNO3 5.5 N. Mounts were packaged with mica sheets and sent for neutron irradiation to the Oregon State University TRIGA Reactor. Fission tracks were analyzed using the Fish Canyon tuff and Durango as standards for the zeta calibration. Detrital samples that represent multiple lithologies can have large grain-age distributions, so the age is expressed as populations. We determined age populations for these samples based on dominant age peaks identified with the Binomfit (Brandon, 2002) program, and for direct comparison with recent AFT ages from the Adriatic side of the orogen (Malusà and Balestrieri, 2012) that were also interpreted using this method. We compared the detrital cooling ages with minimum depositional ages of the Tertiary foredeep units exposed in the drainage areas, in order to estimate whether our samples were reset during the Apenninic orogenic event, and used the following chronostratigraphic divisions: Macigno Unit (Chattian-Aquitanian; Cita Sironi et al., 2006), Cervarola Unit (Aquitanian-Langhian; Delfrati et al., 2002), and Marnoso Arenacea Unit (late 41 Burdigalian-Tortonian; Pialli et al., 2000).

We compiled samples with thermochronometric cooling ages <10 Ma (Figure 2) from: new and existing AHe samples (135), detrital AFT samples (16), bedrock AFT samples (94), and ZHe samples (26) (Abbate et al., 1994; Balestrieri et al., 1996; Ventura et al., 2001; Zattin et al., 2002; Balestrieri et al., 2003; Fellin et al., 2007; Thomson et al., 2010; Malusà and Balestrieri, 2012; Carlini et al., 2013; Balestrieri et al., 2018). We assume a spatially constant onset age of exhumation, so ages older than 10 Ma were not included in our analysis. From the detrital AFT samples, we selected the minimum reset thermochronometric age from the sample grain-age distributions. Reset ages were then converted to surface denudation rates using the AGE2EDOT program, (Willett and Brandon, 2013), which allows us to calculate denudation rates for all low-temperature thermochronometers, given the kinetic parameters listed in Table 1. As the Alpi Apuane have a different denudational history compared to the rest of the Northern Apennines (Balestrieri et al., 2003; Fellin et al., 2007), we disregard these samples in our conversion of thermochronometric ages to denudation rates. To determine exhumation rates, we used the closure temperature concept (Dodson, 1979), although this is a simplification of the overall thermal history of each sample. Each thermochronometer has a particular closure temperature, also expressed as a depth below the surface, above which the crystal retains information in the form of fission tracks or parent/daughter isotope. For simple cooling histories, the measured age of the sample is represented by the time needed for a rock to move from the closure depth to the surface (e.g. Reiners and Brandon, 2006).

Table 1. AGE2EDOT Parameters for each thermochronometer used in this study.

Spontaneous Induced AFt AFt Age Mount Rho-s Rho-i Rho-d age age 1 P( 2) disp Sample Sampling Site Lat Long # Ng (x 105 cm-2) Ns (x 105 cm-2) Ni (x 105 cm-2) (Ma) (Ma) (%) (%) 𝝈𝝈𝝈𝝈 𝜒𝜒𝜒𝜒 Vara Piana Battolla 44.1950° 9.8569° 1 74 1.13 283 30.7 7672 14.8 10.5 0.74 0 49 Vara Piana Battolla 44.1950° 9.8569° 2 76 1.83 432 39.0 9212 15 Magra2 Pontremoli 44.3873° 9.8868° 1 31 0.94 117 34.5 4285 14.3 6.94 0.6 0 Magra2 Pontremoli 44.3873° 9.8868° 2 69 1.17 220 41.8 7878 14.8 50 Magra1 Isola 44.1867° 9.9258° 1 127 0.95 642 33.9 22978 16.2 7.81 0.45 0 Magra1 Isola 44.1867° 9.9258° 2 23 0.67 72 30.9 3342 15.9 27 Serchio Piazza al Serchio 44.1920° 10.3016° 1 72 1.36 359 37.2 9834 12.8 8.08 0.51 0 Serchio Piazza al Serchio 44.1920° 10.3016° 2 28 0.96 78 34.6 2801 13.3 18 Lima2 Cutigliano 44.0907° 10.7596° 1 62 1.00 237 43.1 10202 15.0 6.63 0.46 0 Lima2 Cutigliano 44.0907° 10.7596° 2 38 1.15 152 41.8 5527 14.2 29 Lima1 Borgo a Mozzano 43.9993° 10.5540° 1 31 0.75 97 35.9 4616 14.5 5.41 0.59 89 0 Bisenzio Vaiano 43.9277° 11.1258° 1 87 1.02 215 41.2 8992 14.8 7.09 0.71 0 57 Pescia Pietrabuona 43.9294° 10.6933° 1 33 1.43 209 42.4 6192 13.8 8.95 0.69 0 Pescia Pietrabuona 43.9294° 10.6933° 2 44 0.29 242 3.5 6644 13.9 37 Ng: number of individual grains dated Rho-s: spontaneous track density Rho-i: Induced track density in external detector Rho-d: induced track density in external detector adjacent to dosimeter glass Age disp: Age Dispersion

42 A 9E 10E 11E AFT Age (Ma) N 0.79 9.95 5 4 Parma

Bologna

Genoa

N La Spezia 4 4

Florence

0 25 50 75 100 m

B AHe Age (Ma) N

0.79 9.95 5 4 Parma

Bologna

Genoa

N La Spezia 4 4

Florence

0 25 50 75 100 9E 10E m 11E 12E

Figure 2. A) AFT bedrock (diamonds outlined in black) and detrital AFT ages (diamonds outlined in white), and B) AHe ages. Samples from the Alpi Apuane (gray, striped polygon) were not included in the analysis.

43 The denudation rate (ė ) of the sample is the given by equation (1)

ė = zc /τ (1)

where τ is the sample age, and zc is the depth of the closure isotherm (Reiners and Brandon, 2006; Willett and Brandon, 2013). The model solves for the denudation rates analytically and requires the following data: 1) cooling age (Tables 2–5), 2) onset of denudation, 3) initial or final geothermal gradient, 4) elevation at which the sample was obtained, and 5) mean elevation around the sample (Tables 2-5). For bedrock samples, an elevation correction is required for calculating total exhumation paths. To calculate topographically corrected exhumation rates for these samples, the total exhumation since reaching the closure depth is given as:

Zc = Zc’ + (h-hm) (2)

where Zc’ is closure depth, h is the sample elevation, and hm is the average topography approximated with a circle of radius 2πzc. For detrital samples, we assume h – hm = 0, as the source of each apatite grain within the upstream area is unknown.

AFT and AHe age-elevation relationships (AER) from the Northern Apennines reflect an increase in denudation at ~4 Ma (Thomson et al., 2010), which would violate the EDOT model assumption of steady denudation through time (Willett and Brandon, 2013). We therefore calculate long-term denudation rates using two methods: (1) using a single geothermal gradient through time, and (2) using separate geothermal gradients for ages of 0-4 Ma and ages >4 Ma. To apply the thermal solution to the older age group, we modified the elevation and age parameters, based on the solution for the 0-4 Ma age group, for which we calculated a final geothermal gradient of 44° C/km and an average denudation rate of 0.5 mm/yr. We then reduced all sample elevations in the older age group by the total eroded thickness of rock (2 km), assuming this steady denudation rate over the last 4 Ma.

We used a modern surface temperature of 13.8˚C, which represents the calculated yearly average surface temperature at Bologna from 1813-2004 (NOAA Global Temperature Summary of the Year dataset). We assume an initial, spatially constant geothermal gradient of 25˚C/km for the Northern Apennines (Ventura et al., 2001; Zattin et al., 2002; Balestrieri et al., 2003). We compared the modeled, final geothermal gradient for AHe samples with the present-day geothermal gradient, calculated using available heat flow maps (Della Vedova et al., 2001; Pauselli et al., 2019) and a spatially constant thermal conductivity value for sandstone (2 W/mK). South of Florence, the heat flow maps used to calculate the modern geothermal gradient cover overlapping areas of the Apennines. Particularly at the crest of the Northern Apennines, the maps illustrate large (> 30 °C/ km) differences in interpolated heat flow measurements. From the mountain front to 44.5 N, the Della Vedova (2001) map was used to constrain heat flow measurements, and the Pauselli et al. (2019) map was used south of this latitude.

44 Reference Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Fellin et al. (2007) al. et Fellin Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) (km) 0.383 1.194 0.870 0.340 0.440 0.665 0.688 0.356 0.546 0.704 0.656 0.808 1.295 1.155 0.709 0.709 0.899 0.899 0.899 1.050 1.131 1.131 0.688 0.582 0.582 0.582 0.836 0.582 0.582 Mean Elevation* Elevation* (2σ) 0.22 0.36 0.22 1.22 0.33 0.35 0.41 0.31 0.21 0.24 0.38 0.28 0.20 0.28 0.59 0.38 0.40 0.46 0.37 0.32 0.44 0.31 0.48 0.42 0.42 0.48 0.41 0.49 0.38 Error 5.93 3.66 3.60 6.89 5.45 5.86 6.85 5.10 3.51 4.04 6.32 4.74 3.36 4.65 9.80 6.27 6.60 7.66 6.12 5.33 7.40 5.22 7.92 7.04 7.00 8.01 6.85 8.10 6.37 Age (Ma) (km) 1.640 0.756 0.890 0.285 0.600 0.305 1.060 0.675 0.335 0.270 0.530 1.055 0.880 0.815 0.425 0.425 1.035 1.035 1.035 1.320 1.815 1.815 1.060 0.370 0.370 0.370 0.765 1.495 1.495 Sample Elevation 10.488 10.068 10.259 10.107 10.277 10.308 10.380 10.059 10.115 10.315 10.156 10.326 10.632 10.735 10.429 10.429 10.529 10.529 10.529 10.542 10.553 10.553 10.380 10.463 10.463 10.463 10.438 10.480 10.480 Longitude 44.201 44.122 44.128 44.066 43.974 44.003 44.014 44.124 44.162 44.009 44.177 44.098 44.190 44.110 44.129 44.129 44.111 44.111 44.111 44.130 44.142 44.142 44.014 44.084 44.084 44.084 44.148 44.188 44.188 Latitude Lithology Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno Unit Macigno Macigno Unit Unit Macigno Macigno Unit Unit Macigno PseudoMacigno Unit/Apuan autoch. PseudoMacigno Unit/Apuan autoch. PseudoMacigno Unit/Apuan autoch. PseudoMacigno Unit/Apuan autoch. AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe Method ID Compilation of AHe ages and site characteristics from ublished studies. Compilation of

03AP34 03AP51 03AP47 03AP58 03RE20 03RE19 03GB07 03GB04 03GB09 03GB10 03RE02 020620-3 03RE06A 03AP12A 03AP23A 03AP23B 03AP28A 03AP28C 03AP28C 03AP28D 03AP28D 03AP29A 03AP31A 03AP31B 03AP51C 03AP51C 03AP52A 03AP52B 03AP52C 03AP52C 03RE05C 03RE05D 03AP08AB Table 2. Table 45 Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) 0.776 0.942 0.942 0.635 0.635 1.138 1.138 1.138 1.214 1.214 0.881 0.881 0.904 0.578 0.578 0.449 1.205 1.205 1.205 1.194 1.024 1.024 0.965 0.965 0.957 0.957 0.702 0.471 0.471 0.471 0.471 0.957 0.08 0.35 0.33 0.32 0.60 0.40 0.34 0.56 0.17 0.19 0.40 0.45 0.26 0.49 0.44 0.40 0.28 0.32 0.27 0.39 0.11 0.11 0.32 0.33 0.56 0.66 0.10 0.35 0.17 0.37 0.19 0.40 1.34 5.87 5.55 5.28 9.95 6.73 5.71 9.41 2.88 3.14 6.70 7.51 4.27 8.18 7.27 6.74 4.73 5.38 4.55 6.43 1.15 1.84 5.28 5.42 9.29 6.04 1.65 5.88 2.89 6.14 3.16 6.61 0.700 0.460 0.460 0.840 0.840 1.495 1.495 1.495 1.600 1.600 0.979 0.979 0.678 0.153 0.153 0.047 1.645 1.645 1.645 1.640 1.112 1.112 1.239 1.239 1.272 1.272 0.700 0.250 0.250 0.250 0.250 1.272 12.146 10.767 10.767 10.665 10.665 10.480 10.480 10.480 10.676 10.676 10.568 10.568 10.600 10.593 10.593 10.552 10.628 10.628 10.628 10.488 10.664 10.664 10.674 10.674 10.668 10.668 11.504 11.729 11.729 11.729 11.729 10.668 43.790 44.059 44.059 44.005 44.005 44.188 44.188 44.188 44.200 44.200 44.086 44.086 44.080 44.013 44.013 43.980 44.124 44.124 44.124 44.201 44.263 44.263 44.276 44.276 44.280 44.280 44.037 44.107 44.107 44.107 44.107 44.280 Macigno Unit Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Unit Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Unit Macigno Helminthoid Flysch Helminthoid Flysch Helminthoid Flysch Helminthoid Flysch Helminthoid Flysch Helminthoid Flysch Helminthoid Flysch Marnoso Arenacea Unit Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AP1 1929 1926 1926C 1926C 03RE7 1926D 1926D 1926B 03RE5A 03RE5B 03TH02 03RE06B 03RE12A 03RE12B 03RE14A 03RE14B 03RE5CD 03RE7R1 03TH02B 03TH12B 03TH13A 03TH13C 03TH18A 03TH23A 03TH23C 03TH23BD 050320-1C 050320-2C 050320-3C 050320-1D 050320-2B 050320-3A 050320-3B 46 Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) 0.77 0.727 0.879 0.801 0.271 0.423 0.858 0.860 0.860 0.868 0.897 0.897 0.906 0.907 0.907 0.521 0.836 0.830 0.811 0.722 0.746 0.521 0.521 0.521 0.629 0.674 0.605 0.909 0.765 0.766 0.667 0.747 0.14 0.20 0.10 0.08 0.77 0.18 0.12 0.20 0.20 0.12 0.22 0.06 0.14 0.20 0.20 0.36 0.12 0.32 0.12 0.12 0.08 0.13 0.06 0.16 0.08 0.08 0.12 0.17 0.22 0.05 0.37 0.23 2.41 3.27 1.62 1.29 9.54 2.95 1.97 3.08 3.35 2.02 3.12 1.04 2.33 3.29 3.29 5.96 1.92 5.33 1.93 2.08 1.32 2.15 1.01 2.72 1.28 1.37 1.94 2.86 3.62 0.79 6.11 3.87 0.60 0.900 0.750 0.650 0.370 0.150 1.200 0.515 0.515 0.725 0.940 0.940 1.365 1.655 1.655 0.200 0.565 0.907 0.690 1.070 0.450 0.200 0.200 0.200 0.300 0.400 0.600 0.630 0.500 0.605 0.950 0.700 12.15 12.149 11.779 11.792 11.955 11.951 11.914 11.733 11.733 11.746 11.749 11.749 11.739 11.711 11.711 11.501 11.791 11.656 11.670 11.687 11.719 11.501 11.501 11.501 11.449 11.431 12.110 10.913 11.002 11.191 10.807 10.864 43.79 43.815 43.895 43.919 44.097 44.015 43.797 43.818 43.818 43.824 43.844 43.844 43.864 43.879 43.879 44.189 43.905 43.934 43.961 44.013 43.995 44.189 44.189 44.189 44.147 44.115 43.876 44.060 44.113 44.143 44.001 44.021 Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Marnoso Arenacea Unit Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso Unit Arenacea Marnoso Marnoso Arenacea Unit Unit Arenacea Marnoso AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe C1 C16 C16 C10 C10 C11 C11 C13 C13 AP3 AP5 AP8 AP9 AP2 AP17 AP17 AP30 AP30 AP33 AP37 AP38 AP52 AP53 AP54 AP55 AP57 AP5B AP5C AP5D AP36E AP43R1 AP43R2 AP44R1 AP45R1 AP45R2 AP47R1 AP48R1 AP48R2 47 Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) 0.921 0.836 0.471 0.685 0.685 0.660 0.821 0.719 0.775 0.706 0.855 1.064 0.753 0.980 0.798 0.661 0.779 0.755 0.757 0.738 1.174 1.174 1.165 1.184 1.184 1.196 1.196 1.208 1.208 1.208 1.230 1.230 0.21 0.17 0.23 0.22 0.42 0.23 0.24 0.26 0.17 0.24 0.16 0.10 0.12 0.10 0.22 0.21 0.12 0.12 0.11 0.10 0.20 0.20 0.16 0.16 0.21 0.17 0.18 0.18 0.16 0.15 0.16 0.16 3.49 2.80 3.87 6.94 3.89 3.62 3.97 4.27 2.84 4.05 2.73 1.59 1.96 1.73 3.64 3.43 1.92 2.05 1.89 1.62 3.35 3.34 2.74 2.63 3.55 2.84 2.98 2.98 2.69 2.53 2.62 2.68 1.4 0.5 0.5 0.625 0.497 0.830 0.850 0.884 0.320 0.780 0.510 0.641 0.680 1.250 0.610 0.360 0.650 0.625 0.700 1.000 2.165 2.165 2.045 1.950 1.950 1.830 1.830 1.750 1.750 1.750 1.660 1.660 9.386 9.949 11.640 10.919 11.645 11.603 11.645 11.012 10.932 10.929 11.025 10.683 11.038 10.758 11.044 11.503 11.044 11.039 11.204 11.204 10.699 10.699 10.704 10.692 10.692 10.684 10.684 10.677 10.677 10.677 10.666 10.666 43.653 44.068 43.612 43.594 43.612 44.004 44.041 44.021 44.731 44.014 44.417 44.246 44.028 44.223 44.049 44.013 44.095 44.111 44.115 44.106 44.194 44.194 44.194 44.196 44.196 44.200 44.200 44.202 44.202 44.202 44.201 44.201

Macigno Unit Macigno Unit Macigno Unit Macigno Unit Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe

C2 C3 C4 C5 C6 C7 C8 C9 C17 C17 C22 C22 C23 C23 C29 C29 C34 C34 C37 C37 C40 C40 C52A C52A CIM1 CIM2 CIM3 CIM4 CIM5 CIM6 CIM5A CIM1R1 CIM3R1 CIM4R1 CIM5R1 CIM6R1 VALD1a VALD2a VALD10a VALD2R1

48 Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) 0.730 0.730 0.730 0.747 0.747 0.746 0.746 0.658 0.782 0.782 0.3 0.25 0.21 0.21 0.24 0.23 0.23 0.22 0.24 0.25 4.11 3.52 3.46 4.01 3.92 3.86 5.03 3.75 3.96 4.09 1.2 1.2 1.1 0.74 0.74 0.85 0.88 0.74 0.85 0.88 11.648 11.656 11.659 11.684 11.648 11.648 11.684 11.656 11.659 11.651 43.620 43.621 43.620 43.626 43.620 43.620 43.626 43.621 43.620 43.604

Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit AHe AHe AHe AHe AHe AHe AHe AHe AHe AHe

VALD5a VALD6a VALD7a VALD4a1 VALD4a2 VALD4R1 VALD5R1 VALD6R1 VALD8R1 VALD8R2

49 Reference Balestrieri (2000) Balestrieri (2000) Balestrieri (2000) Balestrieri (2000) Balestrieri (2000) Balestrieri (2000) Abbate et al. (1994) Abbate et al. (1994) Abbate et al. (1994) Abbate et al. (1994) Abbate et al. (1994) Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Abbate et al. [1994] Balestrieri et al. (1996) al. et Balestrieri Balesterieri et al. (2018) al. et Balesterieri Balesterieri et al. (2018) al. et Balesterieri (2018) al. et Balesterieri (2018) al. et Balesterieri (2018) al. et Balesterieri (2018) al. et Balesterieri (km) 0.560 1.061 0.980 0.811 0.811 1.061 1.096 0.104 0.104 0.079 0.079 0.079 0.502 0.562 0.560 0.560 0.563 0.562 0.571 0.607 0.701 0.701 0.619 0.610 0.636 0.634 0.554 0.779 0.634 0.554 Mean Elevation Elevation ) 𝝈𝝈𝝈𝝈 1 1.60 1.34 1.43 1.30 1.00 1.40 0.86 0.78 0.55 1.19 0.42 0.67 0.77 1.10 0.70 1.00 1.00 1.40 ( 0.63 0.71 1.00 0.36 0.59 0.61 0.59 0.56 0.31 0.25 0.58 0.36 Error Error 5.00 8.93 9.19 8.91 8.21 9.51 6.19 4.58 4.73 8.11 8.46 7.13 3.86 5.50 5.00 3.90 4.00 6.60 3.96 4.71 5.59 5.95 1.96 1.91 1.63 5.24 7.50 4.95 3.93 Age 3.64 (Ma) 0.36 0.89 0.76 0.48 (km) 1.300 0.965 0.270 0.240 1.850 1.300 0.475 0.525 0.000 0.000 0.000 0.787 0.675 0.575 0.170 0.840 1.620 1.620 0.450 0.425 0.460 0.840 0.452 0.840 0.675 0.650 Sample Elevation Elevation 9.787 9.787 9.830 9.830 9.830 9.425 11.476 10.446 10.424 10.415 10.415 10.642 10.656 10.192 11.472 11.472 11.475 11.477 11.477 10.193 10.226 10.267 10.267 10.375 10.375 10.388 10.256 10.256 10.264 10.264 Longitude 43.993 44.206 44.174 44.105 44.105 44.125 44.134 44.085 44.051 44.051 44.051 44.040 44.085 44.002 44.001 44.000 43.998 43.994 44.055 44.069 44.058 44.078 44.078 44.036 44.033 44.044 44.055 44.028 44.017 Latitude 44.43525 Lithology gneiss cgl gneiss graywacke cgl granite graywacke graywacke Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Torrente Carigiola Torrente Apuan autochthon Gottero Sandstone Hercynian Basement Hercynian Basement Marnoso Arenacea Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Pseudomacigno Apuan autochthon Hercynian Basement Apuan autochthon AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT Method ID G2 FC5 FC3 CP1 CP3 BT2 CB3 CB4 CB5 BT1 AR1 FO1 FO4 FO5 AR3 CAS1 CAS2 Compilation of AFT bedrock ages and site characteristics from published studies. AFT Compilation of TCGA BOR2 CAST2 CAST3 GOM2 GOM3 ROM1 ROM2 ROM3 ROM4 ROM5 . MD1 (MAD1) AR2(4) (AR2A)a AR2(4) Table 3 Table 50 Bonini et al. (2013) Bonini et al. (2013) Bonini et al. (2013) Bonini et al. (2013) (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini Carlini et al. (2013) al. et Carlini Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri (1996) al. et Balestrieri (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri 0.136 0.632 0.606 0.587 0.587 0.587 0.861 0.639 0.726 0.776 0.776 0.789 0.638 0.337 0.379 0.373 0.376 0.808 0.700 0.837 1.001 1.001 0.881 0.851 0.883 0.817 0.777 0.136 0.136 0.136 0.193 0.779 0.748 1.20 1.10 1.10 1.00 0.80 0.70 0.90 0.90 1.00 0.50 0.60 0.90 1.30 0.90 1.00 0.90 1.00 1.10 1.20 1.90 0.50 0.80 0.30 0.50 0.90 1.00 0.80 1.20 0.70 1.10 1.00 1.00 1.10 8.70 8.60 6.50 7.50 7.30 6.50 4.70 7.00 4.70 3.20 4.10 4.30 9.50 5.60 6.10 6.90 7.40 8.70 8.70 7.30 4.10 4.60 2.30 2.50 5.40 6.20 7.80 8.00 7.60 9.70 9.70 6.40 4.90 0.000 1.318 1.075 1.075 0.950 0.948 1.111 0.248 0.618 0.710 0.702 0.600 1.349 0.303 0.504 0.722 0.857 0.880 0.668 1.085 0.860 0.780 0.500 1.082 1.135 0.600 0.718 0.000 0.000 0.000 0.000 0.450 0.301 9.638 9.311 9.312 9.312 9.311 9.311 9.995 9.824 9.931 9.931 9.783 9.442 9.930 9.776 9.966 9.602 9.943 9.804 9.638 9.638 9.638 9.472 9.425 9.940 10.562 11.411 11.430 11.382 11.380 10.196 10.194 10.528 10.564 44.329 44.320 44.525 44.522 44.522 44.456 43.601 43.541 43.565 43.562 44.463 44.353 44.472 44.379 44.380 44.338 44.331 44.463 44.446 44.456 44.550 44.4268 44.4268 44.3908 44.13381 44.43401 44.42207 44.42207 44.13381 44.13381 44.13381 44.43525 44.23452 Tusc. Ligurian Ligurian Ligurian Ligurian Ligurian Ligurian Epiligurian Subligurian Subligurian Subligurian Subligurian Subligurian Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Tuscan Nappe Tuscan Nappe Tuscan Nappe Tuscan Nappe Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT CH2 CH3 CH4 CH1 MS5 PR 3 PR 5 MS4 MS2 MS1 PR 7 MG3 ZAT2 BORl PR 11 PR 12 PR 15 PR 17 PR 18 PR 20 PR 22 PR 26 PR 27 RAM1 RAM3 RAM4 RAM5 RAM6 PR 6.1 PR 23.1 PR 25.1 PR 28.1 PR 28.2

51 Bonini et al. (2013) Bonini et al. (2013) Bonini et al. (2013) Bonini et al. (2013) (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini (2013) al. et Carlini Carlini et al. (2013) al. et Carlini Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri (1996) al. et Balestrieri (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri Balestrieri et al. (1996) al. et Balestrieri 0.136 0.632 0.606 0.587 0.587 0.587 0.861 0.639 0.726 0.776 0.776 0.789 0.638 0.337 0.379 0.373 0.376 0.808 0.700 0.837 1.001 1.001 0.881 0.851 0.883 0.817 0.777 0.136 0.136 0.136 0.193 0.779 0.748 1.20 1.10 1.10 1.00 0.80 0.70 0.90 0.90 1.00 0.50 0.60 0.90 1.30 0.90 1.00 0.90 1.00 1.10 1.20 1.90 0.50 0.80 0.30 0.50 0.90 1.00 0.80 1.20 0.70 1.10 1.00 1.00 1.10 8.70 8.60 6.50 7.50 7.30 6.50 4.70 7.00 4.70 3.20 4.10 4.30 9.50 5.60 6.10 6.90 7.40 8.70 8.70 7.30 4.10 4.60 2.30 2.50 5.40 6.20 7.80 8.00 7.60 9.70 9.70 6.40 4.90 0.000 1.318 1.075 1.075 0.950 0.948 1.111 0.248 0.618 0.710 0.702 0.600 1.349 0.303 0.504 0.722 0.857 0.880 0.668 1.085 0.860 0.780 0.500 1.082 1.135 0.600 0.718 0.000 0.000 0.000 0.000 0.450 0.301 9.638 9.311 9.312 9.312 9.311 9.311 9.995 9.824 9.931 9.931 9.783 9.442 9.930 9.776 9.966 9.602 9.943 9.804 9.638 9.638 9.638 9.472 9.425 9.940 10.562 11.411 11.430 11.382 11.380 10.196 10.194 10.528 10.564 44.329 44.320 44.525 44.522 44.522 44.456 43.601 43.541 43.565 43.562 44.463 44.353 44.472 44.379 44.380 44.338 44.331 44.463 44.446 44.456 44.550 44.4268 44.4268 44.3908 44.13381 44.43401 44.42207 44.42207 44.13381 44.13381 44.13381 44.43525 44.23452 Tusc. Ligurian Ligurian Ligurian Ligurian Ligurian Ligurian Epiligurian Subligurian Subligurian Subligurian Subligurian Subligurian Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Tuscan Nappe Tuscan Nappe Tuscan Nappe Tuscan Nappe Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone Gottero Sandstone AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT CH2 CH3 CH4 CH1 MS5 PR 3 PR 5 MS4 MS2 MS1 PR 7 MG3 ZAT2 BORl PR 11 PR 12 PR 15 PR 17 PR 18 PR 20 PR 22 PR 26 PR 27 RAM1 RAM3 RAM4 RAM5 RAM6 PR 6.1 PR 23.1 PR 25.1 PR 28.1 PR 28.2

52 Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Felin et al. (2007) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) 0.752 0.760 0.662 0.374 0.330 0.976 0.976 0.342 0.558 0.724 0.356 2.165 2.045 1.950 1.830 1.750 1.660 0.860 0.754 0.863 0.853 0.951 0.844 0.815 0.781 0.755 0.471 0.836 0.645 0.717 0.685 0.559 0.730 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.90 0.75 1.00 0.95 0.85 0.85 2.30 1.30 1.10 1.40 1.05 8.70 7.10 9.60 5.80 6.80 6.80 6.10 5.30 6.60 5.70 7.50 6.50 6.60 5.10 7.30 5.00 7.53 6.40 8.80 6.50 7.90 7.84 7.44 6.68 7.22 6.60 6.12 6.63 4.97 7.33 7.35 6.73 5.35 1.6 1.4 0.5 0.73 1.79 1.53 1.42 1.26 1.13 0.78 1.95 1.83 1.75 1.66 1.45 0.96 0.58 0.74 1.861 0.654 1.055 0.546 0.494 0.636 1.112 1.112 2.165 0.204 0.250 0.773 0.675 2.045 0.497 10.064 10.050 10.074 10.073 10.076 10.071 10.057 10.058 10.050 10.288 10.326 10.160 10.073 10.059 10.664 10.664 10.699 10.083 10.129 10.188 10.059 10.704 10.692 10.684 10.677 10.666 11.640 11.641 11.673 11.603 11.645 11.656 11.648 44.368 44.334 44.361 44.357 44.455 44.354 44.353 44.338 44.334 44.124 44.106 44.098 44.178 44.139 44.128 44.263 44.263 44.194 44.081 44.164 44.170 44.194 44.196 44.200 44.202 44.201 43.653 43.663 43.696 43.594 43.612 43.614 43.620 Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Unit Macigno Unit Macigno Unit Macigno Helminthoid Flysch Helminthoid Flysch Pseudomacigno Apuan Cervarola Unit (Modino) (Modino) Unit Cervarola Cervarola Unit (Modino) (Modino) Unit Cervarola (Modino) Unit Cervarola (Modino) Unit Cervarola (Modino) Unit Cervarola (Modino) Unit Cervarola AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT S 1 S 3 S 4 SC 2 SU 1 CIM1 SM 3 CIM2 CIM3 CIM4 CIM5 CIM6 SILL1 SILL2 SILL3 SILL4 SILL5 SILL6 SILL7 SILL9 MSV 2 SILL10 VALD1 VALD2 VALD3 VALD4 03RE20 03GB07 VALD10 VALD11 VALD12 050320-1b 050320-1a

53 Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Ventura et al. (2001) Ventura Ventura et al. (2001) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) Thomson et al. (2010) 0.805 0.619 0.685 0.747 0.746 0.658 0.782 0.619 0.775 0.855 0.667 0.630 0.653 0.765 0.706 1.064 0.766 0.950 0.645 0.753 0.747 0.977 0.909 0.798 0.921 0.594 0.660 0.779 0.821 0.755 0.719 0.757 0.738 NA NA NA NA NA NA NA NA NA 1.40 0.70 0.70 0.80 0.08 0.05 0.50 0.80 1.10 1.20 0.50 0.90 0.50 0.80 0.60 1.20 1.10 0.80 0.60 1.10 0.70 1.00 0.60 0.70 4.70 9.20 6.40 3.90 6.83 4.43 5.90 6.88 8.84 8.58 6.70 5.00 2.60 3.90 8.60 3.10 9.80 3.30 6.80 4.10 2.70 5.70 4.90 5.90 6.50 5.00 3.00 5.40 5.20 6.20 4.70 7.40 8.50 1.1 1.2 1.2 1.15 0.88 0.85 0.51 0.32 0.35 0.700 0.700 0.500 0.600 0.500 0.780 0.641 0.605 0.980 0.950 0.680 0.700 1.250 0.630 0.610 0.625 0.360 0.830 0.650 0.850 0.625 0.884 0.700 1.000 9.949 9.386 11.492 11.952 11.504 11.396 11.659 11.656 11.315 11.651 11.684 11.652 11.002 11.025 10.683 11.191 10.777 10.807 11.038 10.864 10.758 10.913 11.044 10.919 11.503 11.012 11.044 10.932 11.039 10.929 11.207 11.204 11.597 44.069 43.819 44.037 44.121 43.620 43.621 44.097 43.604 43.626 43.646 44.113 44.014 44.246 44.143 44.417 44.223 44.001 44.028 44.021 44.223 44.060 44.049 44.068 44.013 44.004 44.095 44.041 44.111 44.021 44.115 44.731 44.106 44.064 Modino Macigno Unit Macigno Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Unit Macigno Unit Macigno Macigno Unit Macigno Cervarola Unit Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Cervarola Unit Cervarola Unit Cervarola Marnoso Arenacea Unit Arenacea Marnoso Marnoso Arenacea Unit Arenacea Marnoso Marnoso Arenacea Unit Arenacea Marnoso Marnoso Arenacea Unit Arenacea Marnoso Marnoso Arenacea Unit Arenacea Marnoso AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT C1 C3 C4 C5 C2 C6 C7 C8 C9 C37 C10 C34 C38 C11 C13 C40 C16 C17 C52 C22 C23 C29 1930 1929 1927 Ap 15 AP 10 AP 34 VALD5 VALD6 VALD7 VALD8 VALD9

54 Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) Zattin et al. (2002) 0.688 0.673 0.868 0.723 0.799 0.672 0.809 0.747 0.805 0.816 0.50 0.70 1.00 0.70 0.80 0.80 0.70 0.70 0.80 0.70 3.60 3.90 4.50 4.10 5.10 7.90 4.70 5.60 5.30 4.70 0.94 0.69 0.450 0.400 0.725 0.500 0.565 1.070 0.515 1.365 11.719 11.431 11.746 11.686 11.723 11.687 11.749 11.670 11.733 11.739 43.995 44.115 43.824 43.983 43.905 44.013 43.828 43.961 43.818 43.864 Marnoso Arenacea Marnoso Arenacea Marnoso Arenacea Marnoso Arenacea Marnoso Arenacea Unit Arenacea Marnoso Unit Arenacea Marnoso Unit Arenacea Marnoso Marnoso Arenacea Unit Arenacea Marnoso Marnoso Arenacea Unit Arenacea Marnoso Unit Arenacea Marnoso AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AP 9 AP44 AP47 AP 57 AP 54 AP 56 AP 52 AP 55 AP 43 AP 45

55 this study this study this study this study this study this study this study this this study this Reference Balestrieri (2000) Balestrieri (2000) Balestrieri Balesterieri et al. (2018) Balesterieri et al. (2018) Balesterieri et al. (2018) Balesterieri et al. (2018) Balesterieri et al. (2018) Balesterieri et al. (2018) Balesterieri et al. (2018) Malusa and Balestrieri (2012) Malusa and Balestrieri (2012) Malusa and Balestrieri (2012) Malusa and Balestrieri (2012) Malusa and Balestrieri (2012) Malusa and Balestrieri (2012) (km) 0.242 0.314 0.209 0.242 0.265 0.285 0.324 0.250 0.786 0.665 0.665 0.539 1.030 0.258 0.985 0.539 0.539 0.542 0.542 0.541 0.537 0.477 0.305 Mean Elevation* 0.50 0.70 1.10 0.90 0.80 0.70 0.95 3.10 1.00 (2σ) 1.09 1.53 1.15 0.85 1.00 1.05 2.50 0.80 0.70 0.70 0.70 2.50 1.00 1.20 Error Error 5.90 4.70 4.10 6.90 6.50 4.60 4.00 5.30 5.10 5.20 7.78 7.72 5.40 6.10 8.00 7.50 5.90 5.80 5.90 5.80 5.40 7.00 5.50 Age (Ma) 0.032 0.163 0.208 0.099 0.119 0.117 0.140 0.102 0.036 0.251 0.200 0.200 0.097 0.544 0.105 0.525 0.426 0.415 0.397 0.416 0.421 0.230 0.210 Sample Elevation (km) 9.851 9.647 9.584 9.925 9.887 10.413 11.027 10.758 10.120 11.126 10.491 10.491 10.560 10.760 10.693 10.306 11.450 11.451 11.455 11.455 11.453 11.462 11.428 Longitude 44.198 44.713 43.928 44.620 44.872 44.477 44.532 44.901 44.000 44.091 44.188 44.387 43.929 44.192 44.048 44.048 43.992 43.991 43.989 43.988 43.992 43.975 43.953 Latitude Ligurian Ligurian Lithology Macigno Unit Macigno Unit Ligurian/ EpiLigurian LowerFan delta sediment LowerFan delta sediment LowerFan delta sediment Fan Upper delta sediment Fan Upper delta sediment Fan Upper delta sediment Fan Upper delta sediment Plio-Pleistocene sediments Plio-Pleistocene sediments Ligurianand Macigno Units Ligurianand Macigno Units Macignoand Ligurian Units Cervarolaand Modino Units Ligurian/EpiLigurian/Macigno Ligurian/EpiLigurian/Macigno Ligurian/EpiLigurian/Macigno Cervarola/Modino/Macigno Units Cervarola/Modino/Macigno Units AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT AFT Method ID Compilation of detrital AFT ages and site characteristics from published studies. AFT Compilation of detrital Vara Enza Nure Taro LFD1 LFD2 LFD3 UFD1 UFD2 UFD3 UFD4 Lima1 Lima2 Pescia BARG1 BARG2 . Panaro Serchio Secchia Trebbia Bisenzio Magra1 Magra2 Table 4 Table 56 Reference Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Fellin et al. (2007) Abbate et al. et [1994] Abbate Abbate et al. et [1994] Abbate (km) 0.405 0.451 0.708 0.395 0.395 0.392 0.387 0.430 0.399 0.386 0.693 0.410 0.356 0.333 0.348 0.428 0.437 0.645 0.611 0.666 0.582 0.412 0.404 0.404 0.560 0.576 Mean Elevation* (2σ) 0.40 0.46 0.29 0.75 0.74 0.47 0.52 0.58 0.41 0.47 0.37 0.35 0.43 0.55 0.48 0.41 0.42 0.51 0.38 0.45 0.43 0.44 0.38 0.37 0.39 0.47 Error Error 5.7 Age 4.98 3.61 9.35 9.27 5.94 6.44 7.19 5.11 5.93 4.62 4.33 5.41 6.93 5.98 5.09 5.29 6.40 4.77 5.58 5.42 5.54 4.81 4.58 4.92 5.86 (Ma) (km) 0.650 0.170 0.958 0.756 0.756 0.925 0.080 0.125 0.505 0.810 0.890 0.305 0.080 0.600 0.440 0.670 0.799 0.810 0.510 0.915 1.500 0.500 0.845 0.845 1.450 0.450 Sample Elevation 10.264 10.199 10.325 10.068 10.068 10.139 10.161 10.175 10.179 10.194 10.259 10.308 10.248 10.277 10.330 10.303 10.253 10.327 10.324 10.200 10.186 10.155 10.243 10.243 10.265 10.375 Longitude 44.017 44.067 44.096 44.122 44.122 44.069 44.050 44.069 44.048 44.032 44.128 44.003 43.995 43.974 43.966 44.013 44.036 44.075 44.066 44.159 44.133 44.071 44.024 44.024 44.077 44.033 Latitude Lithology Macigno Unit Macigno Unit HercynianBasment Massa Unit Met. Mesozoic succ. Massa Unit HercynianBasement Massa Unit HercynianBasement Massa Unit HercynianBasement Massa Unit HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. HercynianBasement Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. PseudoMacignoUnit/Apuan autoch. Zhe Zhe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe ZHe Method ID Compilation of ZHe ages and site characteristics from published studies. CP3(4) FIO4z2 03AP38 03AP41 03AP42 03AP43 03AP45 03AP47 03AP58 03GB02 03GB04 03GB06 03GB12 03RE17 03RE21 03RE22 03RE24 03RE27 FO4A(4 . 03RE25A 020620-1 020620-3 APUANE-1z1 APUANE-1z2 G3(4) (G3A)a G3(4) 020620-3 rep 020620-3 Table 5 Table 57 3.3.2 Orogenic Wedge Model We model the Northern Apennines as an orogenic wedge, following the schematic figure and parameters illustrated in Figure 3. We assume a steady-state, asymmetric wedge, so that the incoming flux of accreted material to the prowedge is balanced by the outgoing flux of material from the retrowedge. The model presented here is more comprehensive than the kinematic model presented in Chapter 2, and includes: 1) quantitative constraints for the wedge geometry, 2) two modes of material accretion into the wedge, and 3) deformation within the orogenic wedge. This last point necessitates delineating three, rather than two spatial features in this model: 1) the orogenic wedge (Apennines), the subducting slab (Adria), and the distal upper plate (Eurasia).

The geometry of the wedge is defined by taper angles for the prowedge (αP and βP) and retrowedge (αR and βR).

The lengths of the prowedge (LP) and retrowedge (LR) are 60 km and 40 km, respectively, based on average widths measured from a SRTM 90 Digital Elevation Model (DEM). We estimate a maximum crustal thickness of 56 km (Spada et al., 2013), a maximum elevation of 2 km, and values of 10 km for both prowedge frontal accretion (h0) and basal accretion (h1) of incoming crust. We assume there is no retrowedge basal accretion (h2 = 0).

We prescribe a compressional prowedge and an extensional retrowedge, where horizontal velocities decrease along the prowedge and increase along the retrowedge, as a function of distance. The horizontal velocity of rock is therefore highest at the toes of the wedge, at the model boundaries, and lowest at the drainage divide between the prowedge and retrowedge. The vertical velocity of rock is also variable with depth, and is defined as the sum of the denudation rate and the motion to carry the rock along the surface.

Material is accreted to the prowedge through thrust slices in the upper plate (frontal accretion) or offscraped from the subducting plate at depth (underplating). The important distinction between these two mechanisms is that frontal accretion is characterized by predominantly horizontal motion of rock, and underplating is characterized by vertical motion of rock. The velocities in the model (Figure 3) are defined as follows: plate subduction velocity (VP), pro-wedge underplating velocity (UP), retro-wedge underplating velocity (UR), prowedge denudation velocity (eP), and retrowedge denudation velocity (eR). Material motion is constrained by balanced frontal and rearward fluxes, underplating, and denudation. Average denudation rates for the prowedge (0.40 mm/yr) and retrowedge (0.18 mm/yr) are based on available 10Be catchment-averaged denudation rates (Table 6), which are suggested to be valid over the last 900 ka (Cyr and Granger, 2008). The plate subduction velocity, or convergence rate, for the Northern Apennines is suggested to be driven entirely by slab retreat, so we use estimates of slab retreat to express parameter VP. Slab retreat rates are on the order of 6-9 mm/yr in this region of the Apennines from this study (Chapter 2) and published studies (Faccenna et al., 2014; Rosenbaum and Piana Agostinetti, 2015), so we run the model using these values as the acceptable range.

58 LR LP e eR P

V V P α P R αP h z 0 R hm Β h ΒR P 1 h 2 UR UP

Figure 3. Orogenic wedge schematic. Parameters are described in the text.

3.3.3 Vitrinite Reflectance As reset thermochron cooling ages can provide only a minimum burial depth from the closure temperature to the surface, we also compiled vitrinite reflectance data from the Northern Apennines (Reutter et al., 1983), in order to constrain the pattern of maximum burial depth across the orogen. Vitrinite reflectance values are expressed as the percent reflectance measurement (%RM), where higher %RM indicates vitrinite (kerogen) particles that have been subjected to increasingly higher temperatures. In the Northern Apennines, %RM values increase from northeast to southwest, with a maximum near the Ligurian coastline (Figure 4), and are associated with Miocene thrusting of the Ligurian Unit that buried the underlying Tertiary foredeep deposits (Reutter et al., 1983; Figure 1A).

59 Table 6. Compilation of catchment-averaged 10Be denudation rates from river around the Northern Apennines.

Basin [10Be] x 103 Area Erosion Rate 2 River/Location Latitude Longitude at/g quartz (km ) (mm/yr) I. Piemonte 1 Staffora 44.8926 9.0576 20.3 ± 2.7 264.2 0.250 ± 0.043 2 Scriviaa 44.7256 8.8598 19.8 ± 2.8 614.3 0.260 ± 0.046 II. Liguria and Tuscany 3 Entella 44.3518 9.3616 44.7 ± 2.5 296.8 0.105 ± 0.013 4 Vara 44.1950 9.8569 44.6 ± 4.8 555.4 0.102 ±0.016 5 Magra 44.1867 9.9258 20.3 ± 2.7 946.8 0.209 ± 0.031 6 Serchio at Filicaia 44.1360 10.3762 34.3 ± 2.9 251.7 0.187 ± 0.024 7 Serchio at Piaggone 43.9299 10.5060 20.9 ± 2.0 1160.5 0.273 ± 0.039 8 Lima 43.9993 10.5539 32.5 ± 2.0 316.2 0.183 ± 0.022 9 Bisenzio 43.9278 11.1258 35.0 ± 3.7 149.4 0.133 ± 0.020 III. Emilia Romagna 10 Trebbiaa 44.9061 9.5849 13.5 ± 5.7 917.6 0.431 ± 0.221 11 Nure 44.8327 9.6118 14.9 ± 2.3 308.3 0.369 ± 0.073 12 Taroa 44.6953 10.0913 9.0 ± 1.5 1248.9 0.595 ± 0.118 13 Baganza 44.6322 10.1692 21.6 ± 7.5 122.0 0.269 ± 0.109 14 Parma 44.5716 10.2381 14.3 ± 2.0 264.2 0.423 ± 0.074 15 Enzaa 44.6222 10.4083 11.2 ± 2.4 480.5 0.490 ± 0.121 16 Secchiaa 44.5322 10.7571 9.2 ± 2.8 992.1 0.633 ± 0.223 17 Panaro 44.4201 10.9240 20.8 ± 2.4 680.5 0.292 ± 0.045 18 Reno (Whole Basin)b 44.3923 11.2573 14.5 ± 1.8 989.9 0.351 ± 0.058 19 Reno at Fontanab 44.3826 11.2413 9.1 ± 1.5 668.3 0.566 ± 0.112 20 Reno at Poretta Termeb 44.1632 10.9743 26.8 ± 3.1 172.5 0.217 ± 0.035 21 Reno at Molino Pallone 44.1011 10.9621 58.6 ± 4.3 88.5 0.104 ± 0.013 22 Sambrob 44.2739 11.1959 9.5 ± 1.7 213.7 0.55 ± 0.116 23 Settab 44.3818 11.2475 5.97 ± 2.1 316.2 0.83 ± 0.345 24 Vergato 44.2820 11.1082 16.6 ± 1.9 51.6 0.296 ± 0.047 25 Limentrella di Tréppiob 44.0849 11.0445 31.9 ± 1.4 35.3 0.187 ± 0.021 26 Limentra di Sambb 44.1039 10.9998 31.0 ± 2.3 19.0 0.195 ± 0.024 27 Lamone at San Eufemiab 44.1698 11.6865 16.1 ± 0.8 179.2 0.303 ± 0.035 28 Lamone at Biforcob 44.0648 11.5981 17.7 ± 0.9 94.4 0.300 ± 0.035 29 Montone at San Benedettob 43.9817 11.6884 15.6 ± 2.4 55.7 0.372 ± 0.069 30 Montone at Davadolab 44.1187 11.8849 8.4 ± 1.3 190.1 0.570 ± 0.110 31 Senio at Casola Valseniob 44.2211 11.6213 10.2 ± 1.9 132.5 0.467 ± 0.097 b 32 Senio at Palazzuolo 44.109322 11.53721 14.9 ± 2.4 41.7 0.350 ± 0.068 a Samples collected and analyzed by Cyr et al. (2014) b Samples collected and analyzed by Whittmann et al. (2017)

60 2 6 Taro

1.5 4

1

2 Elevation (km) 0.5

0 0 Vitrinite Reflectance (%Rm)

2.5 6 Serchio 2 4 1.5

1 2 Elevation (km) 0.5

0 0 Vitrinite Reflectance (%Rm)

2.5 6 Bologna 2 4 1.5

1 2 Elevation (km) 0.5

0 0 Vitrinite Reflectance (%Rm)

2 6 Chianti

1.5 4

1

2 Elevation (km) 0.5

0 0 Vitrinite Reflectance (%Rm) 100 90 80 70 60 50 40 30 20 10 0 Distance from Mountain Front (km) Figure 4. Vitrinite Reflectance (%RM) values (red dots) shown along four profiles taken perpendicular to the strike of the orogen. Location of the profiles is given in Figure 6B. 61 3.4 Results

3.4.1 Thermochron Ages New detrital AFT (8) sample ages are given in Figure 5 and Tables 7 and 8. Central ages vary from 5.4 ± 0.6 Ma to 10.5 ± 0.7 Ma, and single grain ages show a wide range of values from 5.1 to 145.3 Ma. With the exception of sample Lima1, for which only 30 apatite grains could be dated, we counted a minimum of 77 grains and a maximum of 150 for each sample. All samples except Lima1 show at least two distinct age populations, with minimum age peaks between 5.1 and 8 Ma. All of the minimum age peaks are younger than the stratigraphic ages of lithologies within the catchment (Pialli et al., 2000; Cita Sironi et al., 2006; Delfrati et al., 2002).

In the five southern samples (Serchio, Magra, Lima1, Lima2, Pescia and Bisenzio), the youngest peaks represent the largest age populations and are similar within the age uncertainty to the sample central ages, whereas the three northern samples (Vara, Magra1, Magra2) have central ages older than the minimum peak ages, due to a large proportion of older grains. The three northern samples show two common age populations: at 5–6 Ma and at 12–13 Ma.

Table 7. Central Ages and AFT dataset details.

Spontaneous Induced AFt AFt Age Mount Rho-s Rho-i Rho-d age age 1 P( 2) disp Sample Sampling Site Lat Long # Ng (x 105 cm-2) Ns (x 105 cm-2) Ni (x 105 cm-2) (Ma) (Ma) (%) (%) 𝝈𝝈𝝈𝝈 𝜒𝜒𝜒𝜒 Vara Piana Battolla 44.1950° 9.8569° 1 74 1.13 283 30.7 7672 14.8 10.5 0.74 0 49 Vara Piana Battolla 44.1950° 9.8569° 2 76 1.83 432 39.0 9212 15 Magra2 Pontremoli 44.3873° 9.8868° 1 31 0.94 117 34.5 4285 14.3 6.94 0.6 0 Magra2 Pontremoli 44.3873° 9.8868° 2 69 1.17 220 41.8 7878 14.8 50 Magra1 Isola 44.1867° 9.9258° 1 127 0.95 642 33.9 22978 16.2 7.81 0.45 0 Magra1 Isola 44.1867° 9.9258° 2 23 0.67 72 30.9 3342 15.9 27 Serchio Piazza al Serchio 44.1920° 10.3016° 1 72 1.36 359 37.2 9834 12.8 8.08 0.51 0 Serchio Piazza al Serchio 44.1920° 10.3016° 2 28 0.96 78 34.6 2801 13.3 18 Lima2 Cutigliano 44.0907° 10.7596° 1 62 1.00 237 43.1 10202 15.0 6.63 0.46 0 Lima2 Cutigliano 44.0907° 10.7596° 2 38 1.15 152 41.8 5527 14.2 29 Lima1 Borgo a Mozzano 43.9993° 10.5540° 1 31 0.75 97 35.9 4616 14.5 5.41 0.59 89 0 Bisenzio Vaiano 43.9277° 11.1258° 1 87 1.02 215 41.2 8992 14.8 7.09 0.71 0 57 Pescia Pietrabuona 43.9294° 10.6933° 1 33 1.43 209 42.4 6192 13.8 8.95 0.69 0 Pescia Pietrabuona 43.9294° 10.6933° 2 44 0.29 242 3.5 6644 13.9 37 Ng: number of individual grains dated Rho-s: spontaneous track density Rho-i: Induced track density in external detector Rho-d: induced track density in external detector adjacent to dosimeter glass Age disp: Age Dispersion

62 SW NE 1 2 8 32 128 512 Scrivia*Trebbia*36 126 4.0 9 8 4 4 2 16 9.662

Nure* 6 4 10 Vara 4.1 6 13.0 37 2 4 1 Taro* 2 5.9 6 145.3 19 4 11 6 Magra2 4.6 56 2 5.2 4 2 Enza* 2 13.4 24 6 4.7 4 12 Magra1 6 2 8.2 4 3 Secchia* 2 5.1 12.3 41 6 6.5 4 13 7.5 Serchio 6 2 4 4 2 Panaro* 18.3 99.2 6 6.9 4 14 6 6.1 Lima2 2 4 5 1 2 8 32 128 512 2 17.4

Lima 6 5.4 4 6 2

8.0 Pescia 6 4 7 2 24.5 111.1 Bisenzio 6 5.3 4 8 2 29.4 1 2 8 32 128 512

Figure 5. Age-population plots for catchments across the primary drainage divide (vertical black line). Ages from Adriatic rivers (Malusà and Balestrieri, 2012) plot to the right of the vertical line and Ligurian Rivers to the left of the line (this study). Peak distribution curves (black curves) and frequency (y axis), and peak ages are shown for all catchments. Ligurian catchments also illustrate total PDFs (gray curves). Black circles represent projection of sampling location onto divide, and tapering lines show rivers whose headwaters have a common divide. Pescia sample has no black circle attached to the tapering line, as the headwaters of this catchment do not reach the drainage divide. 63 Table 8. Peak Ages with standard error and size of major peaks (%). Sample Sampling Site Peak Age (Ma) ± 1 and size of major peaks 1 10 tt 9 /11 () 1 / 09 () 1 /1 (1) 0 0 /0 (9) 1 /18 (1) 1 08 1 1 1 /1 (8) 8 /0 (0) 1 / (1) 0 10 8 /0 (9) 18 / () 99 /1 (1) 0 0 ti 1 /0 (9) 1 / 0 () 0 1 /0 (100) 0 /0 (90) 9 /0 (10) 0 8 88 80 /0 (9) / () 1111 / (1)

3.4.2 Long-term Denudation Rates Long-term denudation rates, initial, and final geothermal gradients are given in Tables 9-12. Most of the sample sites in the Northern Apennines do not have ages from multiple thermochronometers, so variable exhumation rates through time cannot be resolved, with the exception of age-elevation profiles. In order to invert age- elevation profiles for variable denudation rates through time with AGE2EDOT, different initial parameters for individual segments of the exhumation path should be assumed, because denudation rates derived from high and low elevation samples are valid over different time intervals. However, regardless of whether variable or constant initial parameters were used, the same pattern of denudation rates was produced for high- and low- elevation samples from the Northern Apennines, and this temporal pattern of denudations rates is consistent with the pattern reported in Thomson et al. (2010). Therefore, we report denudation rates using a single, initial geothermal gradient of 25˚ C/km. The final geothermal gradient ranges from 31–49˚C/km for AFT bedrock samples, 32–39˚C/km for AFT detrital samples, 27–53˚C/km for AHe samples, and 38˚C/km for the single ZHe sample included in the analysis.

64 Table 9. AFT bedrock denudation rates, and initial and final geothermal gradients. Samples from the Alpi Apuane were excluded from the analysis. Inital Final Geothermal Geothermal Denudation Rate Gradient Gradient ID Latitude Longitude (mm/yr) (℃/km) (℃/km) 1927 44.064 11.597 0.393 25 32 1929 44.037 11.504 0.557 25 35 1930 44.069 11.492 0.786 25 40 03GB07 44.124 10.059 0.498 25 34 03RE20 44.098 10.326 0.522 24.8 34 050320-1a 44.263 10.664 0.509 25 34 050320-1b 44.263 10.664 0.678 25 38 AP 10 44.121 11.396 0.764 25 39 Ap 15 43.819 11.952 0.389 25 32 AP 34 44.097 11.315 0.580 25 35 AP 43 43.818 11.733 0.574 25 36 AP 45 43.828 11.749 0.725 25 38 AP 52 43.905 11.723 0.613 25 36 AP 54 43.961 11.670 0.593 25 36 AP 55 44.013 11.687 0.511 25 34 AP 56 43.983 11.686 0.711 25 39 AP 57 43.995 11.719 0.792 25 40 AP44 43.824 11.746 0.684 25 38 AP47 43.864 11.739 0.794 25 40 BOR2 44.435 9.425 0.431 25 33 BORl 44.435 9.425 0.494 25 34 BT1 44.085 9.787 0.769 25 40 BT2 44.085 9.787 0.755 25 40 C1 44.113 11.002 0.482 25 34 C10 44.143 11.191 0.772 25 39 C11 44.001 10.807 0.400 25 32 C13 44.021 10.864 0.515 25 34 C16 44.060 10.913 0.952 25 44 C17 44.068 10.919 0.625 25 36 C2 44.004 11.012 0.565 25 35 C22 44.041 10.932 0.971 25 44 C23 44.021 10.929 0.676 25 37 C29 44.731 9.386 0.612 25 36 C3 44.014 11.025 0.683 25 37 C34 44.417 9.949 0.384 25 32 C37 44.246 10.683 0.924 25 44 C38 44.223 10.777 0.937 25 44 C4 44.028 11.038 0.883 25 42 C40 44.223 10.758 0.817 25 41 C5 44.049 11.044 0.572 25 35 C52 44.013 11.503 0.541 25 35 C6 44.095 11.044 0.632 25 37 C7 44.111 11.039 0.600 25 36 C8 44.115 11.207 0.548 25 35 C9 44.106 11.204 0.524 25 34 CAS1 44.206 10.446 0.439 25 33 CAS2 44.174 10.424 0.403 25 32 CAST2 44.105 10.415 0.355 25 31 CAST3 44.105 10.415 0.367 26 32 CB3 44.051 9.830 0.436 25 33 CB4 44.051 9.830 0.432 25 32 CB5 44.051 9.830 0.483 25 34 CH1 43.601 11.411 0.598 25.1 36 CH2 43.541 11.430 0.577 25.4 36 CH3 43.565 11.382 0.563 24.9 35 CH4 43.562 11.380 0.545 25.2 35 65 CIM1 44.194 10.699 0.486 24.6 33 CIM2 44.194 10.704 0.465 24.9 33 CIM3 44.196 10.692 0.475 25.5 34 CIM4 44.200 10.684 0.534 24.6 34 CIM5 44.202 10.677 0.491 25.3 34 CIM6 44.201 10.666 0.523 25.5 35 GOM2 44.125 10.642 0.482 25 33 GOM3 44.134 10.656 0.585 25 36 MG3 44.235 9.472 0.358 25 31 MS1 44.134 9.638 0.366 25 31 MS2 44.134 9.638 0.458 25 33 MS4 44.134 9.638 0.433 25 33 MS5 44.134 9.638 0.410 25 32 PR 11 44.463 9.930 0.427 25 33 PR 12 44.353 9.776 0.426 25 32 PR 15 44.472 9.966 0.529 25 34 PR 17 44.379 10.196 0.736 25 39 PR 18 44.380 10.194 0.668 25 37 PR 20 44.338 10.528 1.023 25 46 PR 22 44.331 10.564 1.151 25 49 PR 23.1 44.329 10.562 0.735 25 39 PR 25.1 44.320 9.995 0.454 25 33 PR 26 44.463 9.602 0.669 25 37 PR 27 44.525 9.824 0.683 25 37 PR 28.1 44.522 9.931 0.891 25 43 PR 28.2 44.522 9.931 0.756 25 39 PR 3 44.446 9.943 0.535 25 34 PR 5 44.456 9.804 0.458 25 33 PR 6.1 44.456 9.783 0.706 25 38 PR 7 44.550 9.940 0.586 25 36 RAM1 44.434 9.311 0.510 25 34 RAM3 44.427 9.312 0.606 25 36 RAM4 44.427 9.312 0.538 25 35 RAM5 44.422 9.311 0.532 25 35 RAM6 44.422 9.311 0.585 25 36 ROM1 44.002 11.472 0.673 25 37 ROM2 44.001 11.472 0.694 25 38 ROM3 44.000 11.475 0.813 25 41 ROM4 43.998 11.477 0.781 25 40 ROM5 43.994 11.477 0.525 25 34 S 1 44.178 10.160 0.528 25 34 S 3 44.139 10.073 0.546 25 35 S 4 44.128 10.059 0.705 25 38 SC 2 44.081 10.083 0.532 25 34 SILL1 44.368 10.064 0.536 25 35 SILL10 44.334 10.050 0.496 25 34 SILL2 44.361 10.074 0.480 25 34 SILL3 44.357 10.073 0.632 25 37 SILL4 44.455 10.076 0.690 25 37 SILL5 44.354 10.071 0.597 25 36 SILL6 44.353 10.057 0.590 25 35 SILL7 44.338 10.058 0.624 25 36 SILL9 44.334 10.050 0.629 25 37 SM 3 44.164 10.129 0.379 25 32 SU 1 44.170 10.188 0.542 25 35 TCGA 43.993 11.476 0.636 25 36 VALD1 43.594 11.603 0.676 25 37 VALD10 43.653 11.640 0.565 25 35 VALD11 43.663 11.641 0.680 25 38 VALD12 43.696 11.673 0.567 25 35 VALD2 43.612 11.645 0.466 25 33 66 VALD3 43.614 11.656 0.534 25 34 VALD4 43.620 11.648 0.639 25 36 VALD5 43.621 11.656 0.744 25 39 VALD6 43.620 11.659 0.529 25 35 VALD7 43.604 11.651 0.582 25 35 VALD8 43.626 11.684 0.470 25 33 VALD9 43.646 11.652 0.495 25 34 ZAT2 44.391 9.442 0.472 25 33

Table 10. Detrital AFT denudation rates, and initial and final geothermal gradients.

Inital Final Denudation Geothermal Geothermal Rate Gradient Gradient ID Latitude Longitude (mm/yr) (℃/km) (℃/km) Bisenzio 43.928 11.126 0.623 25 37 Lima1 44.000 10.560 0.631 25 36 Lima2 44.091 10.760 0.570 25 35 Magra1 44.188 9.925 0.650 25 37 Vara 44.198 9.851 0.572 25 36 Magra2 44.387 9.887 0.637 25 37 Pescia 43.929 10.693 0.455 25 33 Serchio 44.192 10.306 0.488 25 33 BARG1 44.048 10.491 0.469 25 33 BARG2 44.048 10.491 0.473 25 33 UFD1 43.988 11.455 0.583 25 36 UFD2 43.992 11.453 0.631 25 36 UFD3 43.975 11.462 0.508 25 34 UFD4 43.953 11.428 0.618 25 36 LFD1 43.992 11.450 0.572 25 36 LFD2 43.991 11.451 0.583 25 36 LFD3 43.989 11.455 0.572 25 36 Nure 44.872 9.647 0.756 25 40 Trebbia 44.901 9.584 0.777 25 40 Taro 44.713 10.120 0.707 25 38 Enza 44.620 10.413 0.690 25 38 Panaro 44.477 11.027 0.516 25 34 Secchia 44.532 10.758 0.532 25 35

67 Table 11. AHe denudation rates, and initial and final geothermal gradients. Samples from the Alpi Apuane were excluded from the analysis.

Inital Final Geothermal Geothermal Denudation Rate Gradient Gradient ID Latitude Longitude (mm/yr) (℃/km) (℃/km) C10 44.143 11.191 1.331 25 53 050320-1C 44.263 10.664 1.178 25 50 AP5C 44.189 11.501 1.024 25 45 C9 44.106 11.204 1.009 25 45 AP1 43.790 12.146 0.976 25 44 C40 44.223 10.758 0.962 25 44 AP33 43.919 11.792 0.949 25 44 CIM2 44.194 10.704 0.866 25 41 1926B 44.107 11.729 0.861 25 42 AP55 44.013 11.687 0.858 25 42 CIM3 44.196 10.692 0.856 25 41 AP9 44.115 11.431 0.852 25 41 AP57 43.995 11.719 0.841 25 42 AP8 44.147 11.449 0.840 26 42 AP47R1 43.864 11.739 0.826 25 41 AP30 43.895 11.779 0.806 25 41 CIM5R1 44.202 10.677 0.806 25 40 AP17 43.876 12.110 0.770 25 39 C8 44.115 11.204 0.761 25 39 CIM4 44.200 10.684 0.760 25 39 CIM1R1 44.194 10.699 0.758 25 39 CIM6 44.201 10.666 0.741 25 39 AP54 43.961 11.670 0.724 25 38 C6 44.095 11.044 0.723 25 38 C4 44.028 11.038 0.719 25 39 03RE7 44.200 10.676 0.677 25 37 C37 44.246 10.683 0.670 25 38 C7 44.111 11.039 0.668 25 38 AP52 43.905 11.791 0.653 25 37 AP2 43.79 12.15 0.587 25 35 AP3 43.815 12.149 0.537 25 35 C2 44.004 11.012 0.491 25 34 C17 44.068 10.919 0.472 25 33 C16 44.060 10.913 0.467 25 33 C34 44.417 9.949 0.464 25 33 03TH23C 44.124 10.628 0.460 25 33 AP37 44.015 11.951 0.453 25 33 C23 44.021 10.929 0.429 25 32 C3 44.014 11.025 0.428 25 32 C22 44.041 10.932 0.424 25 32 C13 44.021 10.864 0.413 25 32 C29 44.731 9.386 0.411 25 32 03RE20 44.098 10.326 0.401 25 32 C5 44.049 11.044 0.395 25 32 050320-2B 44.276 10.674 0.371 25 31 C1 44.113 11.002 0.371 25 32 03AP08AB 44.190 10.632 0.366 25 31 050320-3B 44.280 10.668 0.327 25 31 C11 44.001 10.807 0.326 25 30 AP5 44.189 11.501 0.219 25 29 AP36E 44.097 11.955 0.182 25 28 03TH13C 44.013 10.593 0.161 25 28 03RE14B 44.005 10.665 0.169 25 28 03TH18A 43.980 10.552 0.181 25 28 03RE12A 44.059 10.767 0.198 25 28 03AP52B 44.084 10.463 0.208 25 28 68 03RE12B 44.059 10.767 0.212 25 28 03AP23B 44.129 10.429 0.213 25 29 03RE02 44.148 10.438 0.231 25 29 03AP34 44.066 10.107 0.232 25 29 03GB10 44.177 10.156 0.243 25 29 VALD1a 43.594 11.603 0.245 25 29 03TH02 44.086 10.568 0.269 25 29 03AP12A 44.110 10.735 0.278 25 30 03AP28D 44.111 10.529 0.296 25 30 03AP51 44.014 10.380 0.302 25 30 AP53 43.934 11.656 0.323 25 31 03TH12B 44.080 10.600 0.329 25 31 03RE14A 44.005 10.665 0.356 25 31 03RE05B 44.188 10.480 0.357 25 31 03RE06A 44.201 10.488 0.360 25 31 03AP29A 44.130 10.542 0.367 25 31 C52A 44.013 11.503 0.383 25 32 VALD2R1 43.612 11.645 0.383 25 31 03GB07 44.124 10.059 0.386 25 32 03GB09 44.162 10.115 0.405 25 32 03RE05D 44.188 10.480 0.411 25 32 VALD4R1 43.620 11.648 0.414 25 32 AP43R1 43.818 11.733 0.418 25 32 VALD5R1 43.621 11.656 0.440 25 33 03AP31B 44.142 10.553 0.449 25 33 VALD6R1 43.620 11.659 0.458 25 33 VALD4a1 43.620 11.648 0.466 25 33 020620-3 44.122 10.068 0.538 25 35 VALD7a 43.604 11.651 0.545 25 35 VALD8R2 43.626 11.684 0.588 25 35 VALD10a 43.653 11.640 0.618 25 36 AP44R1 43.824 11.746 0.672 25 38 AP48R1 43.879 11.711 0.698 25 38 AP38 43.797 11.914 0.890 25 43 AP45R2 43.844 11.749 1.245 25 51 03AP23A 44.129 10.429 0.128 25 27 03AP28A 44.111 10.529 0.271 25 30 03AP28C 44.111 10.529 0.236 25 29 03AP31A 44.142 10.553 0.320 26 31 03AP51C 44.014 10.380 0.264 25 29 03AP52A 44.084 10.463 0.207 25 28 03AP52C 44.084 10.463 0.177 25 28 03RE05A 44.188 10.480 0.305 25 30 03RE05C 44.188 10.480 0.330 25 30 03RE05CD 44.188 10.480 0.214 25 29 03RE06B 44.201 10.488 0.329 25 31 03RE7R1 44.200 10.676 0.629 25 36 03TH02B 44.086 10.568 0.235 25 29 03TH13A 44.013 10.593 0.146 25 27 03TH23A 44.124 10.628 0.440 25 33 03TH23BD 44.124 10.628 0.390 25 32 050320-1D 44.263 10.664 0.837 25 41 050320-2C 44.276 10.674 0.361 25 31 050320-3A 44.280 10.668 0.218 25 28 050320-3C 44.280 10.668 0.304 25 30 1926 44.107 11.729 0.232 25 29 1926C 44.107 11.729 0.438 25 33 1926D 44.107 11.729 0.244 25 29 1929 44.037 11.504 0.469 26 34 AP43R2 43.818 11.733 0.377 25 32 AP45R1 43.844 11.749 0.532 25 34 69 AP48R2 43.879 11.711 0.698 25 38 AP5B 44.189 11.501 0.585 25 35 AP5D 44.189 11.501 0.477 25 33 CIM1 44.194 10.699 0.756 25 39 CIM3R1 44.196 10.692 0.660 25 37 CIM4R1 44.200 10.684 0.721 25 39 CIM5 44.202 10.677 0.699 25 38 CIM5A 44.202 10.677 0.768 25 39 CIM6R1 44.201 10.666 0.722 25 39 VALD2a 43.612 11.645 0.381 25 31 VALD4a2 43.620 11.648 0.328 25 31 VALD5a 43.621 11.656 0.435 25 33 VALD6a 43.620 11.659 0.428 25 33 VALD8R1 43.626 11.684 0.496 25 34

Table 12. ZHe denudation rates, and initial and final geothermal gradients. Samples from the Alpi Apuane were excluded from the analysis. Inital Final Geothermal Geothermal Denudation Rate Gradient Gradient ID Latitude Longitude (mm/yr) (℃/km) (℃/km) 020620-3 44.122 10.068 0.70 25 38 020620-3 rep 44.122 10.068 0.71 25 38

The spatial pattern of long-term denudation rates inverted from time-averaged AFT bedrock ages illustrates denudation rates that vary from 0.36–0.81 mm/yr on the Ligurian side and 0.38–1.15 mm/yr on the Adriatic side (Figure 6). Here, we report average denudation rates with one standard deviation. The highest denudation rates on the Ligurian side are located in the footwall (Macigno Unit) of the Alpi Apuane, whereas the highest rates on the Adriatic side are located in the Cervarola Unit near the drainage divide (Figure 1, Figure 6). AFT bedrock denudation rates across the divide are similar or slightly higher on the Adriatic side, with an average rate of 0.64 ± 0.16 mm/yr, relative to an average Ligurian denudation rate of 0.55 ± 0.12 mm/yr. The average AFT detrital denudation rates on the Adriatic (0.66 ± 0.11 mm/yr) and Ligurian (0.57 ± 0.06 mm/yr) sides are in good agreement with bedrock AFT rates (Figure 7), again highlighting the robustness of the method.

Denudation rates derived from AHe ages range from 0.161–1.245 mm/yr on the Ligurian side and from 0.182 –1.331 mm/yr on the Adriatic side. Average denudation rates on the Adriatic side are fairly consistent along strike (0.66 ± 0.25 mm/yr). One exception to this pattern is the portion of the upper Reno River Valley, where denudation rates are lower (Figure 6). On the Ligurian side, AHe denudation rates are 0.40 ± 0.21 mm/yr.

Figure 7 illustrates denudation rates along profiles A-D (locations given in Figure 6B) from either AFT bedrock samples (blue diamonds), AHe bedrock samples (red triangles), or AFT detrital samples (cyan diamonds or rectangles. AFT detrital data shown as rectangles represent samples collected from catchments, where the length of the rectangle indicates the distance over which the denudation rate is applicable. Samples with the same location, but different denudation rates, represent replicate analyses (as per Thomson et al., 2010.)

70 A 9°E 10°E 11°E 12°E AFT + ZHe 0.13 Denudation Rate (mm/yr) 1.33 45 °N Parma

Bologna

Genoa

La Spezia 44 °N

Florence

0 25 50 75 100 m

B AHe Denudation Rate (mm/yr) 0.13 1.33 45 °N Parma A

B C Bologna

Genoa D

La Spezia 44 °N

Florence

0 25 50 75 100 m C 10Be Denudation Rate (mm/yr) 0.13 1.33 45 °N Parma

Bologna

Genoa

La Spezia 44 °N

Pro le Line (Figure 10)

Florence 25 50 75 100 Km 9°E 10°E 11°E 12°E Figure 6. Time-averaged denudation rates converted from A) bedrock AFT ages (diamonds with black outline) and detrital AFT ages (diamonds with white outline), B) AHe ages (triangles), and C) 10Be concentrations (circles) overlaid on SRTM 90 DEM. The black, solid lines in each panel illustrate the primary drainage divide, and the white, hatched polygon shows the location of the Alpi Apuane metamorphic dome. B) shows the location of swath profiles given in Figure 7 and C) shows the location of the profile line in Figure 10. 71 0 1 A) Taro Profile AFT bedrock AHe bedrock AFT detrital 1 1

1 1

Elevation (km)Elevation 08 0 ()

0 0

0 0 80 0 0 0 0

1 B) Serchio Profile AFT bedrock AHe bedrock AFT detrital 1 0

1 1 0 10 Elevation (km)Elevation

0 ()

0 0

0 0 80 0 0 0 0 0 1 C) Bologna Profile AFT bedrock AHe bedrock AFT detrital 1 1

1 1 0 08 () Elevation (km)Elevation 0

0 0

0 0 80 0 0 0 0

0 1 D) Chianti Profile AFT bedrock AHe bedrock AFT detrital 1 1

1 1 0 08 Elevation (km)Elevation

0 ()

0 0

0 0 100 80 0 0 0 0 () Figure 7. A-D) Swath profiles illustrating mean elevation (thick black line), minimum, and maximum elevation (light gray lines). Locations of swath profiles are shown in Figure 6. 72 3.4.3 Orogenic Wedge Model Figure 8 illustrates the predicted horizontal velocities, uplift rates, and material paths through the wedge from the kinematic model. Horizontal velocities (Figure 8A) are equal to the slab retreat velocity at the wedge boundaries, and decrease to a minimum value of ~2.5 km/My in the core of the range at the drainage divide. To match the actual thermochron cooling ages with modeled cooling ages output from the kinematic model, we set the initial parameters to the following values: prowedge erosion rate (eP = 0.7 km/My), retrowedge erosion rate (eR = 0.1 km/My), and slab retreat rate (VP = 9 km/My). Uplift rates on the prowedge vary from 0.8 – 1 km/My, and from -0.4 – 0 km/My on the retrowedge (Figure 8B).

A) RETROWEDGE PROWEDGE 20

15

10

5 Horiontal Velocity (mmyr)

0

B) 2

1

0

-1 Uplift Rate (mmyr) -2

-3 C) 0

-20

Elevation (km) -40

-60 0 10 20 30 40 50 60 70 80 90 100 Distance (km)

Figure 8. Orogenic wedge model results. A) Predicted material horizontal velocities across the orogenic wedge and B) Predicted uplift rates across the wedge. C) Material motion paths (lines within wedge) and isoage dots (solid, colored circles)

73 Figure 9 illustrates material paths in the upper 10 km of the orogenic wedge. The closure depths at the model boundaries for ZHe (-5.8 km), AFT (-3.1 km), and AHe (-1.8 km) follow the surface topography and are illustrated as dashed, black lines. Only material added to the prowedge in the upper ~5 km reaches the surface (thicker black lines); predicted cooling ages for each thermchronometer range from ~2–13 Ma, and are illustrated as filled markers at the location where the material passes through the closure depth. Predicted cooling ages for each thermochronometer range rom 2.1 – 6.0 Ma (AHe, triangle symbols), from 4.1 – 8.0 Ma (AFT, diamond symbols), and from 9.4 – 12.8 Ma (ZHe, circle symbols). For samples that reach the surface on the prowedge side of the range, predicted cooling ages are similar, but decrease slightly near the divide. For samples that reach the surface on the retrowedge side of the range, cooling ages increase towards the model boundary and material paths lengthen.

5

0

AHe

AFT Elevation (km) -5 ZHe

-10 0 10 20 30 40 50 60 70 80 90 100 Distance (km)

2 3 4 5 6 7 8 9 10 11 12 Cooling Age (Ma) Figure 9. Close-up of material paths for the upper 10 km (below the surface) of the orogenic wedge. Dashed, black lines show closure depths for AHe, AFT, and ZHe thermochronometers along the wedge. Thick black lines illustrate cooling paths for material that reaches the surface within the model boundaries. For these material paths, triangles, diamonds, and circles illustrate predicted thermochronometer cooling ages at the location where material passes through the closure depth.

74 3.5 Discussion

3.5.1 Detrital apatite fission track ages Previous studies disagree on the timing of the onset of exhumation between 8 and 14 Ma (Balestrieri et al., 1996; Ventura et al., 2001), so it is not clear whether the 12–13 Ma old population in the Vara and Magra samples represent partially or completely reset cooling ages. However, these ages are consistent with high- elevation samples west of the Vara catchment that are partially reset and record slow cooling prior to 8 Ma. The 8.2 Ma age peak present in the Magra1, but absent in the Magra2 sample, likely reflects exhumation ages of the nearby Macigno Unit, which would have been eroded and redeposited into the Pliocene basins (Balestrieri, 2000; Fellin et al., 2007). The pulse of exhumation in the Northern Apennines between 6–4 Ma, when most of the Northern Apennines became sub-aerially exposed (Zattin et al., 2002; Balestrieri et al., 2003; Fellin et al., 2007), is consistent with the youngest peaks shown in the Vara, Magra, Lima, and Bisenzio Rivers.

The pattern of detrital AFT between the Ligurian (5 Ma) and Adriatic (6 Ma) sides (Malusà and Balestrieri, 2012) is consistent with the pattern of bedrock AFT ages, which show younging exhumation ages towards the northeast and the propagation of topographic relief during this exhumation pulse (Abbate et al., 1994; Balestrieri et al., 1996; Abbate et al., 1999). Overall, we find consistent results between the detrital AFT and bedrock AFT ages, reinforcing that the reset detrital ages illustrate a true exhumation signal, rather than an artifact of the technique. Fertility analysis of sediment from sampled Adriatic catchments also confirm the detrital samples are representative of the eroded bedrock (Malusà et al., 2016), in the absence of hydraulic sorting effects.

3.5.2 Age to Denudation Rate Inversion We consider an initial exhumation age of 10 Ma to be appropriate for the inversion, and as we assumed a spatially constant onset age of exhumation, ages older than 10 Ma were not included in our analysis. We note that using an initial exhumation age of 14 Ma would not change the pattern of exhumation, but would proportionally decrease all denudation rates in the Northern Apennines. As we incorporate only minimum reset ages from each sample, the 13 Ma-year age population would have no effect on the denudation rate calculations.

To invert age for denudation rate, we allowed the final geothermal gradients to vary together with the exhumation rate. The modeled, final AHe geothermal gradients and actual geothermal gradients from heat flow maps (Della Vedova et al., 2001; Pauselli et al., 2019) are broadly consistent, and all are less than a factor of 2 different (Figure 10). The modeled geothermal gradients tend to underestimate the actual geothermal gradients from the Della Vedova et al. (2001) map, but over-estimate the actual geothermal gradients from the Pauselli et al. (2019) map. The heat flow map of Della Vedova et al. (2001) is based on fewer geothermal well measurements relative to the Pauselli et al. (2019) map, so we consider the Della Vedova et al. (2001) interpolation to have higher uncertainties.

75 1:1 1:1.5 50 C/km)

40 1.5 :1

30

2 :1 AHe Actual Final Geothermal Gradient (

20 20 30 40 50 AHe Modeled Final Geothermal Gradient ( C/km) Figure 10. Comparison of modeled and actual AHe final geothermal gradients. Solid line shows 1:1 slope, and dashed lines show slopes corresponding to labels on figure.

3.5.3 Denudation Rate Patterns Here we compare bedrock AFT and AHe denudation rates using two different inversion methods: one collectively inverts samples at different elevations along a transect (AER) and the other inverts samples individually (simple inversion). The simple inversion integrates the denudation rate from the sample cooling age to the present, whereas the AERs resolve denudation rates during a specific time interval in the past, which is defined by the sample age range. On the Adriatic side, AERs from two locations constrain denudation rates between ~ 8 and 2 Ma and illustrate a two-fold increase in denudation rates between 4 and 5 Ma, from 0.22 ± 0.09 mm/yr to 0.58 ± 0.16 mm/yr (Mt Cimone) and from 0.29 ± 0.1 mm/yr to 0.58 ± 0.23 mm/yr (Mt Falterona; Thomson et al., 2010). The youngest ages at these locations indicate that high denudation rates might have persisted after 2 Ma. The simple inversion from the Mt Cimone age-elevation profile produces an increase in denudation rates from 0.53 ± 0.08 mm/yr (AFT) to 0.74 ± 0.70 mm/yr (AHe). For the entire Adriatic side, the simple inversion rates for AFT and AHe samples are difficult to compare, as the methods have sampled only some of the same regions (Figure 2). However, where the methods overlap, AFT and AHe denudation rates appear similar through time, but are spatially variable (Figure 6). Thus, our combined results from AER samples show that on the Adriatic side the high denudation rates between 4 and 5 Ma have likely been sustained up to the present, whereas the simple inversion at the regional scale cannot resolve this pattern. 76 Cooling ages and denudation rates from the Ligurian side are more complicated. The only AER (Valdarno) available on this side shows an apparent increase in denudation rates through time (Thomson et al., 2010). However, corrected for topographic and advection effects, this AER instead has a negative slope that could be interpreted as a decrease in denudation rates. This negative slopes was previously interpreted to reflect post-cooling tilting of the footwall fault block of an extensional fault, consistent with an overall increase in denudation rates at 4-5 Ma (Thomson et al., 2010). With the simple inversion, we observe a regional decrease in exhumation rates on the Ligurian side (Figure 6). However, this observation does not preclude localized increases in exhumation rates at 4 to 5 Ma. For instance, at 5 Ma, the Alpi Apuane records an increase in exhumation rates of the metamorphic dome that is the footwall block of a large extensional system (Fellin et al., 2007); this event is similar to the timing of the shift in the Valdarno AER. Overall, the denudation rates on the Ligurian side reflect different patterns at the local and regional scales, and vary in time likely due to extensional tectonics. Moreover, the AFT and AHe denudation rates illustrate different patterns across the divide: on the Adriatic side, locally high exhumation rates at 4-5 Ma have been likely sustained towards the present, whereas on the Ligurian side they seem to have decreased.

To further explore how denudation rates vary through space and time to the present, we can compare two detrital denudation datasets: AFT and cosmogenic 10Be catchment-averaged denudation rates (Figure 11). At the regional scale, short-term denudation rates consistently reflect higher rates on the Adriatic side (average of 0.40 mm/yr) relative to the Ligurian side (average of 0.18 mm/yr; Chapter 1). On the Adriatic side, AFT denudation rates are higher than modern denudation rates west of the Secchia River. East of the Secchia River, modern denudation rates are highly variable, especially in the Reno Valley, and are either higher or lower than AFT denudation rates. On the Ligurian side, AFT denudation rates are consistently higher than modern denudation rates by a factor of 2-3, which supports the interpretation of decreasing denudation rates through time.

The complex pattern of modern denudation rates on the Adriatic side is visible at the local scale in the Reno River valley. Modern denudation rates for the entire catchment (~0.35 mm/yr) and major tributaries (0.566 – 0.830 mm/yr) are up to a factor of 8 higher compared with denudation rates from small headwater tributaries (~0.1–0.2 mm/yr; Table 6). Similarly, AHe bedrock denudation rates in the Reno headwater tributaries are surprisingly low relative to AHe denudation rates downstream and at the regional scale. Although we cannot directly compare bedrock and detrital denudation rates, we note that the two shorter-term proxies available for denudation rates indicate similar, low denudation rates at high elevations of the Reno catchment. This pattern has previously been interpreted as a response to different lithologies and erodibilities (Cyr et al., 2014). This lithologic boundary corresponds to a fault that separates the Macigno and Ligurian Units (Bigi et al. 1983). Therefore, we propose that the denudation rates across this boundary reflect movement along the fault, but may not preclude an additional lithologic control.

77 Ligurian Side Adriatic Side

Erosion Rate (mm/yr) Erosion Rate (mm/yr) 0 0.3 0.6 0.9 1.2 0 0.3 0.6 0.9 1.2 0

20

40

60

80

100

120

Detrital AFT Erosion Rates 10Be Erosion Rates 140

160

180 Figure 11. Detrital denudation rates derived from 10Be concentrations and AFT ages for the Ligurian side the Adriatic side of the orogen. Error bars reflect one standard deviation in both cooling age and geothermal gradient.

3.5.4 Orogenic Wedge Kinematics The kinematic model presented here predicted the material path through the orogen and the distribution of thermochronometer dates across the orogen surface. Measured cooling ages across the Northern Apennines illustrate increasing ages from the prowedge to the retrowedge, and the kinematic model presented here replicates this pattern of cooling ages. Both AHe and AFT measured and predicted cooling ages show the youngest ages at the drainage divide. Because only one ZHe sample and replicate outside of the Alpi Apuane were reset (Table 5), we cannot extrapolate this observed pattern of ZHe samples, although the predicted ZHe cooling ages also follow the same pattern as the AHe and AFT samples. 78 Predicted thermochron cooling ages for samples that reach the surface on the prowedge show less variability compared with measured cooling ages, due to the similarity in predicted material cooling paths. Material paths that pass through the drainage divide are kinked (Figure 9), which reflects the change in surface denudation rates across the divide, and these retrowedge material paths experience a larger horizontal component of motion relative to prowedge samples. Measured AFT cooling ages (Figure 2A) are similar across the divide, but measured AHe cooling ages show an obvious shift towards older cooling ages on the retrowedge, a pattern that is captured by the predicted cooling ages for all thermochronometers.

To achieve the predicted pattern of cooling ages required setting denudation rates for the retrowedge (0.1 km/ My) that are a factor of 7 slower than prowedge rates (0.7 km/My). Short-term 10Be denudation rates for the retrowedge (average = 0.18 mm/yr) are similar to the value used in this kinematic model, but actual rates for the retrowedge are lower (average = 0.4 mm/yr) by almost a factor of 2 relative to the value used in the kinematic model. We limited the slab retreat rate (9 km/My) to the acceptable range from the kinematic model presented in Chapter 2 and recent estimates of slab retreat (Rosenbaum and Piana Agostinetti, 2015; Faccenna et al., 2014). Decreasing the slab retreat rate to values of 5-8 km/My produced material paths with a larger vertical component of motion and predicted retrowedge cooling ages that were too old compared with actual ages.

The spatial distribution of AFT and AHe ages has allowed recent studies (Fellin et al., 2007; Thomson et al., 2010) to make inferences about the regional kinematics of the Northern Apennines. Similar to other settings around the world that exhibit asymmetric topography across the orogen (Southern Alps of New Zealand, the Olympic Mountain of the USA, and Taiwan), the Northern Apennines are characterized by a shorter, steeper retrowedge and a longer, gentler prowedge. Assuming steady state, numerical models predict these settings should be strongly influenced by horizontal rock motion (Willett et al., 2001; Miller et al., 2007), and the spatial distribution of multiple thermochronometers in the Southern Alps and the Olympic Mountains confirms the numerical results (Willett and Brandon, 2002).

In the Northern Apennines, the positions of the AFT and AHe reset fronts were observed to vary along the strike of the orogen (Thomson et al., 2010). East of 11˚30’, the AHe front is offset 20 km to the northeast relative to the AFT front. West of 11˚30’, the two fronts coincide with one another (Thomson et al., 2010; Carlini et al., 2013), although the lack of AHe data west of 10˚30’ precludes the assumption that this pattern continues to the western boundary of the Northern Apennines. The intersection of reset fronts between two or more thermochronometers indicates that material was accreted at a deep enough level to reset all ages, so the primary mechanism for accretion is underplating. Where an offset between thermochronometer reset fronts exists, and only the lower temperature thermochronometer has reset ages, demonstrates that material was accreted at shallow levels insufficient to reset the higher temperature thermochronometer. In this scenario, the orogenic wedge would be strongly influenced by the horizontal motion of rock. The kinematic model presented here also produces offset AFT and AHe reset fronts, although the minimum predicted AHe (2.1 Ma) and AFT (4.1) cooling ages are older than measured AHe (0.79 Ma) and AFT (2.3 Ma) cooling ages. Similarly to the Thomson et al. (2010) kinematic model, our model was unable to produce coincident AFT and AHe reset fronts, despite also including underplating as a method for accreting material to the wedge. We note that using a model with only underplating is also unable to reproduce coincident AHe and AFT reset fronts.

79 This model illustrates predicted material cooling paths that have a larger horizontal component of motion on the retrowedge side of the range (Figure 9). This observation corroborates the results from the kinematic model presented Chapter 2. Ths model from Chapter 2 requires a greater component of horizontal motion on the retrowedge, in order to produce the observed asymmetry of the orogen under steady-state conditions and be consistent with 10Be denudation rates and geodetic rates of rock motion across the orogen. Additionally, predicted uplift rates (Figure 8B) for both the prowedge (0.8 – 1 km/My) and retrowedge (-0.4 – 0 km/My) match measured geodetic uplift rates for the Northern Apennines (D’Anastasio et al., 2006) and are within the acceptable range of uplift rates from the kinematic model presented in Chapter 2.

The kinematic model presented here predicts cooling ages that are consistent with the pattern of measured cooling ages for the thermochronometers analyzed in this study, and illustrates results consist with the model presented in Chapter 2. As further work for improving the fit between the observed reset fronts and thermochronometer ages in the Northern Apennines, we model both the kinematics and thermal field of the Northern Apennines (sensu Reiners et al., 2015), which will combine the kinetic parameters from the AGE2EDOT program (Willett and Brandon, 2013) with the kinematic model presented here. Ages will be calculated as a function of denudation rates, initial geothermal gradient and time. Importantly, the ages will depend on (1) the closure temperature, (2) the cooling rate, and (3) the advection of heat towards the surface during denudation. This latter process becomes important for denudation rates that exceed 0.3 mm/yr because it will change the geothermal gradient (Willett and Brandon, 2013). By implementing this thermal model with the existing kinematic model, we expect heat advection to result in higher denudation rates, which will translate to younger thermochronometric ages that should more closely resemble the measured AFT and AHe ages in the Northern Apennines.

3.6 Conclusion New detrital AFT ages from the Ligurian side presented here are consistent with AFT bedrock ages, illustrating the robustness of this method in our study region. Long-term denudation rates derived from new and existing bedrock and detrital low-temperature cooling ages illustrate variability in denudation rates through space and time in the Northern Apennines. Inverting cooling ages for long-term denudation rates of individual samples (simple inversion method) and of multiple samples along age-elevation transects (AER inversion) predict an increase in denudation rates on the Adriatic side, but a decrease in denudation rates on the Ligurian side. This interpretation is consistent with the pattern of decreasing denudation rates illustrated by comparing detrital AFT estimates and modern, catchment-averaged denudation from cosmogenic 10Be in detrital quartz sediment. Relative to modern denudation rates, we observe a temporal decrease in denudation rates on the Ligurian side by a factor of 3. On the Adriatic side, we observe a factor of 5 variability in denudation rates, and modern and long-term denudation rates are similar within uncertainties, so there is no obvious pattern between modern and long-term denudation rates. Given new constraints on modern denudation rates, convergence rates in the Northern Apennines, and both frontal accretion and underplating modes of crustal accretion, modeled cooling ages and paths for low-tempertaure thermochronometers agree with the pattern of measured ages. Furthermore, predicted uplift rates match geodetic uplift rates across the Northern Apennines, and the larger component of horizontal rock motion predicted for the retrowedge is consistent with the results from the kinematic model presented in Chapter 2 .

80 References

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Rosenbaum, G., and Piana Agostinetti, N., 2015, Crustal and upper mantle responses to lithospheric segmentation in the northern Apennines: Tectonics, v. 34, p. 648–661, doi: 10.1002/2013TC003498. Spada, M., Bianchi, I., Kissling, E., Agostinetti, N.P., and Wiemer, S., 2013, Combining controlled-source seismology and receiver function information to derive 3-D moho topography for italy: Geophysical Journal International, v. 194, p. 1050–1068, doi: 10.1093/gji/ggt148. Thomson, S.N., Brandon, M.T., Reiners, P.W., Zattin, M., Isaacson, P.J., and Balestrieri, M.L., 2010, Thermochronologic evidence for orogen-parallel variability in wedge kinematics during extending convergent orogenesis of the northern Apennines, Italy: Bulletin of the Geological Society of America, v. 122, p. 1160–1179, doi: 10.1130/B26573.1. Vai, F., and Martini, I.P., 2001, Anatomy of an orogen: the Apennines and adjacent Mediterranean basins: Springer. 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82 CHAPTER 4 Partitioning the denudation flux between silicate and carbonate physical erosion and chemical weathering in the Northern Apennines

Erica D. Erlanger, Jeremy K.C. Rugenstein, Aaron Bufe, Vincenzo Picotti, and Sean D. Willett

83 Abstract Denudation fluxes for major catchments in the Northern Apennines are partitioned into carbonate and silicate chemical weathering and physical erosion fluxes, using new and existing denudation estimates from 10Be concentrations, major dissolved ions from water chemistry, the percent of carbonate sand from each catchment, and annual discharge measurements. Results illustrate that denudation is dominated by physical erosion of both silicate and carbonate lithologies, chemical weathering fluxes are dominated by carbonate dissolution, and carbonate physical erosion is controlled by lithology. Denudation is negatively correlated with runoff, which may explain the overall lack of scaling between weathering and denudation in our samples. Total weathering fluxes in the Northern Apennines are on the upper end of weathering flux estimates from other orogens. Relative to the Southern Alps of New Zealand, total erosional fluxes are similar, although total weathering fluxes in the Northern Apennines are higher, and can be attributed to larger carbonate weathering fluxes. As existing global data are from silicate-rich settings, the higher carbonate weathering fluxes measured here are consistent with the mixed lithologies present in the Northern Apennines.

4.1 Introduction Physical erosion and chemical weathering are responsible for shaping landscapes, producing soil, and delivering sediment and solutes to the oceans. These processes are strongly coupled because chemical weathering depends on the availability of fresh rock at the surface, and physical erosion depends on relief and elevation, as well as the weakening of rock by chemical weathering (Stallard and Edmond, 1983; Gaillardet et al., 1999; Millot et al., 2002; Lyons et al., 2005; von Blanckenburg, 2006; Murphy et al., 2016). In tectonically active orogens, uplift and exhumation produce increased relief and enhance erosion through processes such as mass wasting and fluvial incision, and the rapid exposure of fresh rock through these processes can also enhance chemical weathering rates (Stallard and Edmond, 1983; Riebe et al., 2004). Therefore, tectonically active mountain systems are believed to dominate both chemical weathering and physical erosion processes (Raymo et al., 1988; Raymo and Ruddiman, 1992; Jacobson et al., 2002; Wittmann et al., 2007; Larsen et al., 2014; Caves Rugenstein et al., 2019).

Denudation is the overall surface lowering due to both chemical weathering and physical erosion processes. Chemical weathering of rocks is one of the major components of Earth’s carbon cycle, and the impact of weathering on the global CO2 budget depends on the lithology undergoing weathering, as it influences the assemblage of minerals at the surface (Stallard and Edmond, 1983; Meybeck, 1987; Galy and France-Lanord, 1999). Here we distinguish between the weathering of silicate rocks, carbonate rocks, and evaporites. Silicate weathering—coupled with the precipitation of CaCO3 in the ocean—sequesters CO2 in the solid Earth (Raymo et al., 1988; Riebe et al., 2001, 2004; Lyons et al., 2005; West et al., 2005). In contrast, on timescales longer than the carbonate compensation time in the ocean (the timescale for the fluxes of alkalinity to return to steady- state; Zeebe and Westbroek, 2003), carbonate weathering by carbonic acid is CO2 neutral. However, carbonate weathering by sulfuric acid has the potential to release CO2 from the solid Earth into the ocean-atmosphere system (Jacobson et al., 2002; Spence and Telmer, 2005; Lerman et al., 2007; Li et al., 2008; Torres et al., 2014; Burke et al., 2018). Because carbonate weathering typically dominates total solute fluxes, even in regions where carbonate is only a minor constituent of the bedrock, understanding the impact of weathering on the global carbon cycle requires an understanding of the ratio of silicate to carbonate weathering (Galy and France- Lanord, 1999; Jacobson et al., 2003; Quade et al., 2003; Lyons et al., 2005; Gaillardet et al., 2018).

84 Carbonate and silicate weathering have been studied in silicate rocks (e.g. granites and metapellites) in a number of orogenic settings (Brandon and Vance, 1992; Granger et al., 1997; Kirchner et al., 2001; Jacobson et al., 2002; Jacobson and Blum, 2003; Matmon et al., 2003; Cyr and Granger, 2008; Moore et al., 2013; Carretier et al., 2015; Emberson et al., 2017), whereas many active landscapes are comprised of mixed siliciclastic- carbonate lithologies. In young orogens, the surface geology is often characterized by marine sedimentary sequences that can host significant volumes of carbonate—often as massive platform carbonates—in addition to silicates. For example, the entire Alpine-Zagros-Himalayan orogenic complex has a common genesis during the Mesozoic because marine sedimentary deposits, composed of carbonate platforms, have been subsequently uplifted during the ongoing closure of the Tethys Ocean (Dercourt and Vrielynck, 1993; Philip et al., 1996). In these ranges, marine carbonates often cap the highest peaks; this is such a ubiquitous observation, to quote John McPhee:

“this one fact is a treatise in itself on the movements of the surface of the earth. If by some fiat I had to restrict all this writing to one sentence, this is the one I would choose: The summit of Mt. Everest is marine limestone.“ (McPhee, 1981)

Yet, the processes that preserve carbonates at high elevation and that modulate their denudation are poorly known, and there is reason to suspect that these processes, particularly those that chemically weather rocks, differ fundamentally from those that act on silicate rocks. First, carbonate dissolution is controlled by reaction kinetics, which are approximately 3 orders of magnitude faster than for silicates, and is controlled by the chemical equilibrium between a primary and secondary weathering phase (Maher, 2011). However, most chemical models have focused on silicate weathering and are typically parameterized with a kinetic, rather than an equilibrium control, and therefore may not by suitable for landscapes where carbonates are prominent lithologies. Secondly, carbonate weathering is more tightly linked to soil and sub-surface CO2 concentrations than silicate weathering (Calmels et al., 2011; Gaillardet et al., 2018; Romero-Mujalli et al., 2018). Thus, it remains unclear if the same processes that control the partitioning of denudation between erosion and weathering in actively uplifting, silicate-rich lithologies are also active in orogens comprised of mixed carbonate-silicate lithologies. Hence, the partitioning of denudation between physical erosion and chemical weathering in mixed silicate-carbonate landscapes remains a fundamental knowledge gap that has implications for landscape development and the carbon cycle.

4.1.1 Approach To address this knowledge gap, we pose three key questions: (1) how is the total denudation separated into carbonate and silicate fluxes, (2) how is carbonate denudation partitioned into erosion and weathering in an active orogenic setting, and (3) what are the impacts of silicate and carbonate weathering and erosion on landscape development, and how does this affect the carbon cycle? To resolve these questions, we test three hypotheses regarding the relationship between denudation, physical erosion, and chemical weathering. First, we hypothesize that (1) physical erosion of carbonate rocks is positively correlated with total denudation fluxes. Secondly, chemical weathering fluxes for carbonates and silicates can have substantially different reaction kinetics. However, in carbonate-rich terranes, weathering fluids saturate rapidly, and dissolved carbonate concentrations are controlled by the thermodynamic equilibrium and the amount of acid available for weathering, rather than kinetic controls and the supply of material (Calmels et al., 2011; Gaillardet et al., 2018). Consequently, we hypothesize that (2) carbonate chemical weathering is decoupled from carbonate physical 85 erosion, due to equilibrium limits on carbonate dissolution. Finally, following published studies that have observed a strong coupling between denudation and chemical weathering fluxes in silicate lithologies (Stallard and Edmond, 1983; Gaillardet et al., 1999; Millot et al., 2002; Lyons et al., 2005; von Blanckenburg, 2006), we hypothesize that (3) total chemical weathering scales with denudation fluxes in mixed lithology catchments.

We address these hypotheses by investigating and quantifying denudation processes in catchments that drain the northern Apennines (Figure 1), a mixed siliciclastic-carbonate orogen. As rivers integrate sediments and solutes that are eroded and weathered from upstream hillslopes, they allow studying denudation at the scale of entire orogens. To this end, we use new and existing 10Be catchment-averaged denudation rates from Chapter 2 and from the literature (Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016), dissolved solutes, and the fraction of carbonate sand in sediments to derive a full decomposition of the denudation flux into physical erosion and chemical weathering for carbonates and silicates. Here, we assume that partitioning between silicate and carbonate fluxes in the sand fraction is representative of total, partitioned denudation. We also explore the spatial patterns and relationships between denudation, weathering, and erosional fluxes in the Northern Apennines, and place our results within the context of previous studies that focused on silicate-rich lithologies within other orogens.

9E 10E 11E 12E N

Po River N 5 4

1 Parma

0 200 12 Km 2 11 13 14 15 Bologna 3 16 19a Genoa 5 17 7 6 18 8a 19 19b 20 La Spezia 8 9 22

N 21

10 4 4

Florence

0 25 50 75 100 m Figure 1. Location map of sampled rivers and tributaries. (1) Staffora R., (2) Scrivia R., (3) Bisagno R., (4) Recco R., (5) Entella R., (6) Vara R., (7) Magra R., (8) Serchio R. at Piaggione, (8a) Serchio R. at Filicaia, (9) Lima R., (10) Bisenzio R., (11) Trebbia R., (12) Nure R., (13) Taro R., (14) Baganza R., (15) Parma R., (16) Enza R., (17) Secchia R., (18) Panaro R., (19) Reno R., (19a) Vergato R., (19b) Setta R., (20) Senio R., (21) Lamone R. at Biforco, and (22) Montone R. 86 4.2 Setting The Northern Apennine Mountains of Italy are a young orogen that forms part of the Alpine orogenic belt, which were uplifted and sub-aerially exposed between ~ 4–5 Ma (Le Pichon et al., 1971; Fellin et al., 2007). This mountain range is a type case for the initial stages of orogenesis, characterized by an intact sedimentary cover of mixed siliciclastic-carbonate lithologies, with little to no metamorphism and relatively low relief. The lithologies present in the Northern Apennines are dominated by syn-orogenic, marine sedimentary sequences deposited as turbidites, which are divided into the Macigno, Cervarola, and Marnoso Arenacea Units (Figures 2 and 3). Overlying these deposits is the Ligurian Unit, which represents remnants of the Ligurian Tethys Ocean, and is comprised of pelagic successions and turbidites (Vai and Martini, 2001). Based on differences in lithology, we divide this structural unit into the External and Internal Ligurian Units, and delineate the boundaries based on the geologic map of Molli (2008). The Internal Ligurian is comprised of shales and siliciclastic turbidites and is found only on the Ligurian side, whereas the External Ligurian Unit is found primarily on the Adriatic side and is comprised of pelagic limestone and sandstone and minor ophiolites (Ricci Lucchi, 1986) (Figures 2 and 3).

Because we focus on denudation fluxes from sand-sized grains, we summarize the dominant lithologies in each studied catchment from detrital modes of modern river sand (Table 1) (Garzanti et al., 1998, 2002).

9E 10E 11E 12E Marnoso Arenacea Unit- Miocene foredeep turbidites Macigno/Cervarola Units- Mid-Miocene foredeep turbidites N

Ligurian Unit - Allochthonous Jurassic to early

5 EL Cenozoic deepwater sedimentary mélange and 4 IL ophiolites Parma Epiligurian - marine and continental deposits

Tuscan metamorphic rocks

Bologna

Genoa N

La Spezia 4 4

Ligurian Sea

0 25 50 75 100 Florence m

Figure 2. Geologic Map of the Northern Apennines. Dark green color shows Internal Ligurian Unit (IL) and light green color shows External Ligurian Unit (EL). Gray areas are not mapped and white area represents the Ligurian Sea. 87 Table 1. Detrital modes for river sand at each sampling site. 1

Avg. Grain Sampling Quartz Upstream Limestone Size used Name Latitude Longitude Elevation Grains* 2) Grains* (%) for analysis (m) Area (km (%) (µm)* Baganza 1 44.498 9.988 820 14 Baganza 2 44.486 9.978 877 10 Baganza 3 44.525 10.007 717 27 Baganza 4 44.568 10.041 665 2 63 6 310 Baganza 5 44.606 10.123 381 106 Baganza 6 44.621 10.171 408 120 Baganza 7 44.684 10.213 213 153 Bisagno 1 44.408 8.950 12 90 Bisagno 2 44.438 8.963 34 75 Bisagno 3 44.436 9.017 281 48 48 10 450 Bisagno 4 44.452 9.033 134 46 Bisagno 5 44.441 9.045 168 34 Bisagno 6 44.444 9.085 287 13 Bisenzio 43.9278 11.1258 118 149 NA NA NA Entella 44.351 9.362 17 297 6 8 620 Enza 44.6267 10.4133 146 481 46 10 235 Lamone 44.0651 11.6009 187 179 NA NA NA Lima 43.9993 10.5539 102 316 NA NA NA Magra 44.187 9.926 28 947 20 20 Montone 44.1210 11.8853 123 190 NA NA NA Nure 1 44.882 9.653 188 342 Nure 2 44.812 9.615 278 296 Nure 3 44.751 9.597 358 240 Nure 4 44.706 9.566 442 194 48 5 310 Nure 5 44.676 9.568 476 161 Nure 6 44.647 9.501 612 71 Nure 7 44.673 9.590 522 29 Nure 8A 44.657 9.581 531 27 Panaro 44.4196 10.9248 161 680 46 26 310 Parma 44.5688 10.2370 347 264 69 12 140 Recco 1 44.411 9.153 259 1 Recco 2 44.395 9.168 108 7 Recco 3 44.382 9.159 28 12 36 16 570 Recco 4 44.379 9.153 21 13 Recco 5 44.374 9.152 28 19 Reno Sasso Marconi 44.3923 11.2573 94 990 24 31 580 Scrivia 44.719 8.861 199 615 31 18 310 Secchia 44.5431 10.7672 105 1010 39 26 190 Senio 44.2266 11.6324 155 136 NA NA NA Serchio at Piaggione 43.9299 10.5060 318 1160 15 35 NA Staffora 44.8930 9.0569 206 264 48 16 300 Taro 44.698 10.093 133 1250 40 3 570 Trebbia 44.909 9.589 132 918 39 2 400 Vara 44.190 9.858 30 555 6 13 540 * Data from Garzanti (1998) † Data from Garzanti et al. (2002)

Carbonate Weathering Site Metrics.xlsx

88 A) B)

C) D)

Figure 3. Outcrops of interbedded sandstone and marl/shale from A) Macigno Unit, B) Cervarola Unit, and C) Marnoso Arenacea Unit. Rock hammer or Professor Vincenzo Picotti for scale. D) Gravel in riverbed at Nure 8 sampling location, showing abundance of green clasts comprised of ophiolites (serpentine) derived from the External Ligurian Unit.

Here, we summarize the findings of Garzanti et al. (1998, 2002), who assessed the general composition of river sand through statistical measurements of grain proportions. We focus on the percent limestone and percent quartz (monocrystalline and polycrystalline) for catchments included in this study. Northern Apennine rivers show a high degree of variability in percent limestone grains. The tributaries of the Po River, which include all rivers on the Adriatic side, except the Senio, Lamone, and Montone Rivers (Num. 20–22 on Figure 1), are rich in limestone grains (24–63%), consistent with a source in the marly-calcareous turbidites of the External Ligurian Unit. A high percentage of limestone grains (36–48%) can also be found in rivers on the Ligurian side that drain the Internal Ligurian Unit (i.e. the Bisagno and Recco Rivers, Num. 3–4 on Figure 1), which is dominated by calcareous flysch sequences (Vai and Martini, 2001). East of the Bisagno River, the majority of rivers that drain 89 the Ligurian side (Vara, Magra, and Serchio Rivers, Num. 6–9 on Figure 1), have a relatively low percentage of limestone grains (6–20%). For all rivers except the Entella, Vara, Reno, and Serchio catchments (Num. 5, 6, 8, 19 on Figure 1), the percent limestone outnumbers the percent of quartz grains (Table 1). In Adriatic rivers, the proportion of quartz grains varies from east to west. The Trebbia, Nure, Taro, and Baganza Rivers (Num. 11–14 on Figure 1) have a low proportion of quartz (Q = 1–8%). Moving east, the proportion of quartz generally increases from the Parma (Q =13%), Enza (Q = 14%), Secchia (Q = 31%), Panaro (Q = 14%), and Reno Rivers (Q = 34%) (Num. 15–19 on Figure 1). The Scrivia and Staffora Rivers (Num. 1–2 on Figure 1) to the west have similar quartz proportions to these rivers (Q = 18–19%).

Lithic metamorphic grains (Lm) are the other major component of river sand in the Northern Apennines, and are primarily comprised of either serpentine minerals or metapellites sourced from ophiolite deposits (Garzanti et al., 1998, 2002). On the Ligurian side, Lm varies from 12-20%, with a maximum in the Entella and Recco Rivers (Num. 4 and 5 on Figure 1), and is similarly divided into metapelite and serpentine grains. On the Adriatic side, Lm is significantly lower (1—8%), but is heavily dominated by serpentine grains (Figure 2).

In the Northern Apennines, evaporites are another prominent pre- and syn-orogenic lithology. The primary source of evaporites of interest to this study are the extensive Triassic gypsum springs and caves that have been mapped around the Northern Apennines, notably in the Secchia River Valley (Poiano Springs, Num. 17 on Fig. 1), the Senio River Valley (Re Tiberio Cave system (Num. 20 on Fig. 1), the Savena River Valley (Spipollo Cave system) and the Magra River Valley (Num. 7 on Fig. 1) (Boschetti et al., 2005, 2011; Cortecci et al., 2008b; Chiesi et al., 2010; D’Angeli et al., 2017). In the Magra and Secchia River Valleys, these gypsum deposits are enriched in halite (NaCl), and the waters draining these deposits in the Secchia River have a salinity (NaCl) of 68% (Boschetti et al., 2005, 2011; Chiesi et al., 2010; De Waele et al., 2017). For comparison, the salinity of seawater is 3.5%. While these evaporites may only constitute a small proportion of the bedrock, the reaction kinetics of evaporite dissolution is 40–80 times faster than silicates (Meybeck, 1987; Gaillardet et al., 2013), so we consider evaporates as a potentially important influence on riverine dissolved ions in the above-mentioned rivers. Messinian evaporites are also present in the Northern Apennines and exposed at the mountain front near Bologna and in the foothills of the Marnoso Arenacea Formation (Figure 2). However, these lithologies are located outside of our sample area, so we exclude them as a possible influence on riverine dissolved ions.

4.2.1 Climate The climate in the Northern Apennines is characterized as Mediterranean, with average temperatures of ~10°C. Average January temperatures are 0°C in the mountains and up to 10°C on the Ligurian coastlines, compared with a more spatially consistent July average of 15–20°C (Brunetti et al., 2014). Precipitation primarily falls as snow or winter rain, with a maximum (300–800 mm) during the months of September through February, and minimum (150–300 mm) during the months of June through August (Crespi et al., 2018). Similarly, the maximum river discharge occurs during the months of November to March. Discharge data from the last five available years generally shows an order of magnitude difference in discharge between March and July, the months during which we sampled rivers in the Northern Apennines (Table 2).

90 Table 2. Average monthly discharge over the last five available years of data for each catchment. Note the Entella River had only two years of available data.

Upstream Discharge (m3/s) River Month Lat Long Area 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 (km2)

March 34.18 17.89 Entella 44.347 9.341 295 July 1.64 12.23 March 7.38 13.75 4.84 28.39 15.59 Vara 44.280 9.653 205 July 1.21 1.34 0.72 1.19 7.17 March 47.68 18.37 42.39 32.44 97.98 Magra 44.198 9.951 937 July 13.33 3.41 6.36 5.63 6.49 March 1.75 3.47 2.93 2.38 1.05 Serchio F 44.163 10.338 178 July 1.09 0.50 2.97 1.32 1.13 March 17.74 9.29 18.06 10.83 26.70 Serchio P 43.929 10.514 1161 July 7.28 4.27 3.87 4.08 5.84 May 21.27 6.46 20.48 4.16 4.70 Lima 44.007 10.564 302 July 3.54 2.10 4.98 2.71 2.83 March 5.74 7.25 4.29 3.13 10.58 Bisenzio 43.923 11.127 152 July 0.87 0.25 0.00 0.01 0.00 March 22.98 12.33 27.63 9.81 9.51 Scrivia 44.721 8.860 615 July 3.99 0.72 1.88 0.35 0.52 March 28.40 19.75 46.26 41.03 40.65 Trebbia 44.900 9.582 915 July 4.94 3.32 6.55 9.19 4.11 March 7.62 13.24 11.39 2.00 4.84 Nure 44.712 9.570 198 July 1.21 0.68 0.02 0.16 5.54 March 7.61 6.54 1.77 9.07 8.12 Baganza 44.635 10.171 129 July 1.00 0.25 0.22 0.64 0.02 March 54.43 33.64 85.55 18.64 113.16 Taro 44.823 10.225 1374 July 0.58 0.90 2.03 0.62 1.66 March 26.98 14.32 45.79 26.02 37.88 Parma 44.805 10.325 423 July 1.83 0.05 2.30 3.37 1.32 March 16.03 22.12 29.61 10.71 25.00 Enza 44.524 10.353 297 July 3.22 1.74 3.83 2.09 0.00 March 43.59 47.51 47.72 20.49 71.66 Secchia 44.433 10.654 695 July 8.82 3.33 4.56 2.15 2.93 March 28.15 30.13 25.16 10.21 17.17 Panaro 44.357 10.923 584 July 5.88 2.82 3.37 2.81 37.92 May 6.72 7.86 8.64 4.91 8.63 Reno 44.476 11.281 10 July 2.61 1.67 1.59 0.44 2.06 March 5.20 14.75 3.46 22.10 12.05 Idice 44.507 11.470 236 July 0.34 0.24 0.09 0.30 0.86 Vergato May 44.288 11.113 17.49 2.57 15.55 21.77 7.37 March 3.31 8.38 7.98 1.14 14.33 Senio 44.227 11.632 136 July 0.13 0.28 0.24 0.15 0.26 March 5.50 1.59 10.44 10.71 25.00 Lamone 44.077 11.614 105 July 0.09 0.14 0.25 2.09 0.00 March 6.75 1.92 8.32 7.08 16.07 Montone 44.170 11.949 236 July 0.23 0.35 0.98 1.07 0.95

4.3 Methods

4.3.1 Sampling Sampling sites were chosen based on locations with existing constraints on catchment-averaged denudation. At these locations, we sampled sediment from active channels and overbank deposits during March and July of 2018. Moreover, over the course of two years (May 2017, July 2017, March 2018, and July 2018) three 30 mL bottles of river water were collected at each sample location (Figure 4). Water samples were also collected 91 from pipes or water seeps at the base of river terrace deposits in the Reno River Valley. We additionally sampled sediment and water along stream transects in two Ligurian Rivers (Recco and Bisagno) and two Adriatic Rivers (Baganza and Nure). All water samples were filtered through 0.2 μm filters. Cation samples were acidified with acid to a pH of 2, while anion samples were kept cool and away from light until analysis. Alkalinity was measured in the field (maximum 24 hours after collection) for most samples using either end-point or Gran titration techniques.

9E 10E 11E 12E N 5 4 Genoa Parma

Elevation (m) 2165 Bologna

0 Genoa

N La Spezia 4 4

Florence

0 25 50 75 100 m Figure 4. Sample locations for sediment and water. Inset image shows close-up of sampling points near Genoa.

4.3.2 Analysis Sediment samples were cleaned to remove organic matter and sieved to obtain the 250—500 μm fraction. We used grain sizes within the range used for 10Be denudation rates reported in the literature (125—700 μm), in order to avoid potential grain size bias when comparing silicate and carbonate physical erosion fluxes. Approximately 50 g of sand (or the available mass) was weighed and dissolved in HCl. The remaining mass was rinsed, dried, and weighed. The difference between the original and final mass was calculated to represent the percent carbonate sand in the sample.

At the ETH Zürich, cation concentrations of water samples were analyzed on a Thermo-Fischer Element XR sector-field inductively-coupled-plasma (ICP) mass spectrometer, and anion concentrations and cation replicates were analyzed on a Dionex DX-120 ion chromatography (IC) instrument. A subset of these samples was additionally analyzed for 14C using a MICADAS Accelerator Mass Spectrometer (AMS), and a small subset of samples was analyzed for δ13C using a GasBench coupled to a Delta V ThermoFischer IRMS. Values 92 4.3.2 Analysis Sediment samples were cleaned to remove organic matter and sieved to obtain the 250—500 µm fraction. We used grain sizes within the range used for 10Be denudation rates reported in the literature (125—700 µm), in order to avoid potential grain size bias when comparing silicate and carbonate physical erosion rates. Approximately 50 g of sand (or the available mass) was weighed and dissolved in HCl. The remaining mass was rinsed, dried, and weighed. The difference between the original and final mass was calculated to represent the percent carbonate sand in the sample.

At the ETH Zürich, cation concentrations of water samples were analyzed on a Thermo-Fischer Element XR sector-field inductively-coupled-plasma (ICP) mass spectrometer, and anion concentrations and cation replicates were analyzed on a Dionex DX-120 ion chromatography (IC) instrument. A subset of these samples was additionally analyzed for 14C using a MICADAS Accelerator Mass Spectrometer (AMS), and a small subset of samples was analyzed for d13C using a GasBench coupled to a Delta V ThermoFischer IRMS. Values of 14C were calculated as percent modern carbon (pMc), where a value of 100 indicates a sample containing 100% modern carbon (i.e. carbon is sourced primarily from the atmosphere), and a value of 0 indicates a sample with carbon derived from a radiocarbon-dead lithospheric source (Stuiver and Polach, 1977). of 14C were calculated as percent modern carbon (pMc), where a value of 100 indicates a sample containing

100% modern carbon (i.e. carbon is sourced primarily from the atmosphere), and a value of 0 indicates a sample with carbonTo assess derived whether from saecondary radiocarbon-dead carbonate lithospheric precipitation source is an(Stuiver important and Polach, influence 1977). on water chemistry, we calculated the calcite saturation index (SI). The SI of a solution is defined as To assess whether(Langmuir, secondary 1971) carbonate: precipitation is an important influence on water chemistry, we calculated the calcite saturation index (SI). The SI of a solution is defined as (Langmuir, 1971):

(1)(1)

SIcalcite= log(IAPcalcite/Kcalcite) (2)(2) -pH IAPcalcite= aCa∙ aHCO3∙ K2/10 where IAP is the ionic activity product, K is the equilibrium constant for carbonate, aCa and aHCO3 are the where2+ IAP is the- ionic activity product, K is the equilibrium constant for carbonate, aCa and activities of Ca and HCO3 (Davies and Shedlovsky, 1964), and K2 is the second dissociation constant of 2+ - H2CO3. We usedaHCO3 temperature-dependent are the activities of Ca equilibrium and HCO 3constants (Davies (calculatedand Shedlovsky, using 1964)the van‘t, and Hoff K2 is equation) the secon withd the temperaturedissociation of stream watersconstant measured of H2 COin the3. field,We used which temperature assumes that-dependent they are representative equilibrium ofconstants subsurface temperatures.(calculated At SI values using of zero,the van‘t the solutionHoff equation) is at equilibrium with the temperature (i.e. saturated). of stream For values waters above measured zero, in the solution is oversaturated with respect to calcite, and the solution is undersaturated at values less than zero. the field, which assumes that they are representative of subsurface temperatures. At SI values 122 4.3.3 Data Corrections We corrected our water chemistry data for either rainwater input or dust pollution. In the absence of evaporites, Cl- from unpolluted rivers is frequently used as an index for atmospheric inputs to rivers. For large rivers, global water chemistry data suggest that atmospheric Cl- concentrations should be lower than 30 μmol/L (Gaillardet et al., 1999). Due to the proximity of the Northern Apennines to the Ligurian and Adriatic Seas, the seawater contribution of Cl- may be higher than the global estimate of Gaillardet et al. (1999). As our lowest concentration of Cl- is 64.6 μmol/L, we assume this value is representative of the rainwater contribution and adjust all river Cl- concentrations by this amount. To correct other dissolved species for rainwater inputs, we assume that rainwater contains the same stoichiometry as seawater, except for sulfate, where we assume the stoichiometry is double that of seawater, following Stallard and Edmond (1981). The remaining chlorine concentration is associated with dust and anthropogenic atmospheric sources of Cl-. In the Northern Apennines, excess Cl- has been attributed to atmospheric pollution from the Po Plain (Panettiere et al., 2000), agriculture, sewage, and deposition from Saharan dust (Cortecci et al., 2008; Vittori Antisari et al., 2010). To account for the potential dust and pollution load to rivers, we alternatively corrected our data with major element concentrations from Bologna precipitation data, which has a polluted atmosphere (Panettiere et al., 2000).

We performed a series of corrections on the Magra, Secchia, and Serchio samples, as the presence of Messinian 2+ 2- + and Triassic evaporites in the Northern Apennines constitute a significant source of Ca , SO4 , Na , and Cl- ions (Cortecci et al., 2008; Chiesi et al., 2010; Boschetti et al., 2011), in addition to the concentrations attributable to silicate and carbonate weathering. Based on the literature, we assume that all evaporites are either gypsum or halite, and we performed a number of corrections to account for these evaporites on water solutes, following a similar approach to Li et al. (2008). As atmospheric inputs of Cl- are fairly small, and Cl- is absent from silicates, any excess Cl- should be due to evaporitic sources.

93 of zero, the solution is at equilibrium (i.e. saturated). For values above zero, the solution is oversaturated with respect to calcite, and the solution is undersaturated at values less than zero.

4.3.3 Data Corrections We corrected our water chemistry data for either rainwater input or dust pollution. In the absence of evaporites, Cl- from unpolluted rivers is frequently used as an index for atmospheric inputs to rivers. For large rivers, global water chemistry data suggest that atmospheric Cl- concentrations should be lower than 30 µmol/L (Gaillardet et al., 1999). Due to the proximity of the Northern Apennines to the Ligurian and Adriatic Seas, the seawater contribution of Cl- may be higher than the global estimate of Gaillardet et al. (1999). As our lowest concentration of Cl- is 64.6 µmol/L, we assume this value is representative of the rainwater contribution and adjust all river Cl- concentrations by this amount. To correct other dissolved species for rainwater inputs, we assume that rainwater contains the same stoichiometry as seawater, except for sulfate, where we assume the stoichiometry is double that of seawater, following Stallard and Edmond (1981). The remaining chlorine concentration is associated with dust and anthropogenic atmospheric sources of Cl-. In the Northern Apennines, excess Cl- has been attributed to atmospheric pollution from the Po Plain (Panettiere et al., 2000), agriculture, sewage, and deposition from Saharan dust (Cortecci et al., 2008; Vittori Antisari et al., 2010). To account for the potential dust and pollution load to rivers, we alternatively corrected our data with major element concentrations from Bologna precipitation data, which has a polluted atmosphere (Panettiere et al., 2000).

We performed a series of corrections on the Magra, Secchia, and Serchio samples, as the presence of Messinian and Triassic evaporites in the Northern Apennines constitute a 2+ 2- + - significant source of Ca , SO4 , Na , and Cl ions (Cortecci et al., 2008; Chiesi et al., 2010; Boschetti et al., 2011), in addition to the concentrations attributable to silicate and carbonate weathering. Based on the literature, we assume that all evaporites are either gypsum or halite, and we performed a number of corrections to account for these evaporites on water solutes, following a similar approach to Li et al. (2008). As atmospheric inputs of Cl- are fairly small, and Cl- is absent from silicates, any excess Cl- should be due to evaporitic sources. Na+ in water is the sum of contributions from silicate weathering, atmospheric, anthropogenic, Na + in water is the sum of contributions from silicate weathering, atmospheric, anthropogenic, and evaporite sources: (3) and evaporite sources: (3)

+ Na in water is the sum of[C l]contributionsriv = [Cl]anthro from + [C silicatel]evaporite weathering,+ [Cl]atm atmospheric, anthropogenic, Na+ in water is the sum of contributions from silicate weathering, atmospheric, anthropogenic, and (4)evaporite and evaporite sources: 123 sources: + + + + + (4) riv sil atm anthro evaporite [Na+ ] =[Na+ ] + [Na+ ] +[Na+ ] +[Na+ ] [Na ]riv=[Na ]sil+ [Na ]atm+[Na ]anthro+[Na ]evaporite We assume that all halite is due to evaporites in rivers on which we perform this correction. (4) (4) We assume that all halite is due to evaporites in rivers on which we perform- this correction. So, based on the stoichometry+ + of halite+ weathering,+ every mole+ of Cl weathered from So, based on the[N stoichometrya ]riv=[Na ]silof+ halite [Na ]weathering,atm+[Na ]anthro every+ [Nmolea ] evaporiteof Cl- weathered from In riversevaporites on which iswe balanced perform by this one correction, mole of Na we+; thus,assume we that assume all halite the same is due amount to evaporites. of Na+ is Baseddue to on the evaporitesWe assume is balanced that all haliteby one is mole due ofto Naevaporites+; thus, we in assumerivers on the which same weamount perform of Na this+ is correction.due to stoichiometryevaporitic of halite sources. weathering, Having every corrected mole offor Cl -the weathered atmospheric from evaporitescontribution is balancedof Cl-, theby onetotal mole of - - + evaporiticSo, based sources. on the Havingstoichometry corrected +of halitefor the weathering, atmospheric every contribution mole of ofCl Clweathered, the total from Na ; thus,concentration we assume the becomes same amount of Na is due to evaporitic sources. Having corrected for the atmospheric contributionconcentrationevaporites of Cl-, the is becomes balancedtotal concentration by one mole becomes of Na+; thus, we assume the same amount of Na+ is due to

evaporitic sources. Having corrected for the atmospheric contribution of Cl-, the total (5) (5) concentration becomes - - - (5) riv atm evaporite [Cl- ] – [Cl- ] = [Cl- ] riv atm evaporite + [C- l ] – [Cl ] = [Cl ] We correctedWe corrected Na concentrations Na+ concentrations by Cl concentrations, by the same amount where: as the Cl- concentrations, where: We corrected Na+ concentrations by the same amount as the Cl- concentrations, where: (5) - - - riv atm evaporite (6) [Cl ] – [Cl ] = [Cl ] (6) We corrected Na+ concentrations by the same amount as the Cl- concentrations, where: (6) evaporite evaporite We have no way to account for anthropogenic [Na] inputs of = [CCa2+ andl] Mg2+, but given previous studies, anthropogenic [Na]evaporite = [Cl]evaporite Ca2+ inputsWe arehave negligible no way (Royto account et al., for1999; anthropogenic Cortecci et al.,inputs 2002; of CaCortecci2+ and etMg al.,2+ , 2008).but given For previousMg2+, chemical We have no way to account for anthropogenic inputs of Ca2+ and Mg2+, but given previous (6) analysess tudies,of specific anthropogenic rock types Ca in2+ each inputs catchment are negligible would be(Roy required et al., to 1999; distinguish Cortecci between et al., lithologic2002; and studies, anthropogenic Ca2+ inputs are negligible (Roy et al., 1999; Cortecci et al., 2002; anthropogenicCortecci inputs, et al., which 2008) is. beyondFor Mg 2+the, chemicalscope [Na]evaporite of thisanalyses study.= [C ofl] evaporite specific rock types in each catchment Cortecci et al., 2008). For Mg2+, chemical analyses of specific rock types in each catchment wouldWe have be required no way to to distinguish account for between anthropogenic lithologic inputsand anthropogenic of Ca2+ and inputs,Mg2+, whichbut given is beyond previous In the absencewould beof requiredevaporite to deposits, distinguish the betweentotal amount lithologic of Ca and2+ and anthropogenic Mg2+ in water inputs, samples which reflect is beyond weathering of thestudies, scope ofanthropogenic this study. Ca2+ inputs are negligible (Roy et al., 1999; Cortecci et al., 2002; both silicates and carbonates. However, evaporites such as gypsum (CaSO ) can represent another significant the scope of this study. 2+ 4 source of Corteccidissolved, et riverine al., 2008) Ca2+. For(Meybeck, Mg , chemical1987). In riversanalyses known of specific to drain rockevaporites, types inHCO each- has catchment been found 3 to be a moreInwould the reliableabsence be require tracerof evaporited tofor distinguish carbonate deposits, betweeninputs the total (Stallard lithologic amount and of and CaEdmond, anthropogenic2+ and Mg 1983).2+ in Mgwaterinputs,2+ issamples whichalso a reflectisuseful beyond tracer In the absence of evaporite deposits, the total amount of Ca2+ and Mg2+ in water samples reflect for carbonateweatheringthe scope input, of in thisboth the study. silicates absence and of alkalicarbonates. earth silicatesHowever, (e.g. evaporites k-feldspars). such as As gypsum sediment (CaSO in the4) can Northern weathering of both silicates and carbonates. However, evaporites such as gypsum (CaSO4) can Apenninesrepresent is generally another poor significant in K-feldspars source (Garzanti of dissolved, et al., riverine 1998), weCa 2+suggest (Meybeck, that alkali1987) earth. In riverssilicates are 2+ 2+ not a significantrepresent anothersource ofsignificant Mg in Apenninic source- of surface dissolved, waters. riverine The ratioCa2+ of(Meybeck, Ca/Mg2+ in 1987)pure .silicates In rivers is 1.45 ± knownIn the absenceto drain ofevaporites, evaporite HCO deposits,3 has the been total found amount to be of aCa more and reliable Mg intracer water for samples carbonate reflect 0.75, while our Ca/Mg ratios are generally- between 1–10, indicating a carbonate composition that is a mixture known to drain evaporites, HCO3 has been2+ found to be a more reliable tracer for carbonate inputsweathering (Stallard of bothand Edmond,silicates and 1983 carbonates.). Mg is However,also a useful evaporites tracer for such carbonate as gypsum input, (CaSO in the4) can between calcite and dolomite (Gaillardet et al., 2018).2+ Therefore, we assume that the primary cation affected inputs (Stallard and Edmond, 1983). Mg is also a useful tracer 2+for carbonate input, in the by evaporiteabsencerepresent weathering of alkalianother is earth Ca significant2+ . silicates source(e.g. k -feldspars).of dissolved, As riverinesediment Ca in the (Meybeck, Northern Apennines1987). In riversis absence of alkali earth silicates (e.g. k-feldspars). As sediment in the Northern Apennines is - generallyknown to poor drain in Kevaporites,-feldspars HCO(Garzanti3 has et been al., 1998),found weto besuggest a more that reliable alkali earthtracer silicates for carbonate are generally poor in K-feldspars (Garzanti et al., 1998), we suggest that alkali earth silicates are 2+ 2+ In carbonatenotinputs a catchments, significant (Stallard thesourceand expected Edmond, of Mg stoichiometric 1983 in Apenninic). Mg ratio is surfacealso of (Caa usefulwaters. + Mg)/HCO tracer The forratio3 is carbonate0.5 of inCa/Mg pristine input, in water pure in the(Sarin not a significant source of Mg2+ in Apenninic surface waters. The ratio of Ca/Mg in pure et al., 1989;silicatesabsence Perrin is of et1.45 alkalial., 2008).± 0.75earth ,Previous whilesilicates our studies (e.g. Ca/Mg khave-feldspars). ratios found are ratios As generally sediment of 1.09 between ± in0.1 the along Northern1— 10,the Brahmaputraindicating Apennines a Riveris (Sarin etsilicates al., 1989), is 1.45 and ratios± 0.75 between, while our1–2 Ca/Mgfor small ratios catchments are generally in France between that were 1— cultivated10, indicating with anitrogen generally poor in K-feldspars (Garzanti et al., 1998), we suggest that alkali earth silicates are fertilizers (Perrin et al., 2008). The average (Ca + Mg)/HCO ratio and 2σ errors for our river samples are 2+ 3 124 1.21 ± 0.32 not (R a2 significant= 0.76) (Figure source 5), ofexcept Mg for in theApenninic Serchio, surfaceSecchia, waters. and Magra The rivers,ratio ofwhich Ca/Mg have 124in ratios pure far greater thansilicates this value, is 1.45 indicating ± 0.75 ,an while excess our of Ca/Mg calcium. ratios We attributeare gen erallythis to betwethe presenceen 1— 10,of gypsumindicating bedrock a in these catchments. To correct for evaporite weathering, we use this average ratio (1.21 ± 0.32) to correct for excess Ca2+ in the Serchio, Secchia, and Magra rivers. We incrementally decrease Ca2+ concentrations124 until the ratio of (Ca + Mg)/HCO3 converges on the closest acceptable value. Based on the stoichiometry 2+ 2- of CaSO4, an equal number of moles of Ca and SO4 should be attributable to the dissolution of gypsum. 94 Using this relationship, we correct SO4 concentrations by subtracting the difference between the original and 2+ 2- adjusted Ca concentrations. For some samples, the adjusted SO4 concentration became negative, probably of relatively high Mg2+ concentrations, for which we made no correction. For those samples, we assumed all 2- 2+ sulfate was derived from evaporites, set the SO4 concentration to zero, and corrected the Ca concentration by this amount. For clarity, we schematically illustrate this procedure in Figure 6.

4000

R2 = 0.76

3000 y = 1.21x

6000

mol/L) Reno μ Serchio P ( Secchia 2+ 2000

4000 + Mg

2+ Magra mol/L) μ ( Ca 2+ R2 = 0.15 + Mg 2+

Ca 2000 1000

0 0 1000 2000 3000 HCO3- (μmol/L) 0 0 1000 2000 3000 4000 - HCO 3 (μmol/L)

- 2+ 2+ 2 Figure 5. Plot of HCO3 against Ca + Mg concentrations. Linear regression is given by the solid, black line and R value. Red, dashed line and equation illustrate average ratio of HCO3/(Ca+Mg) for all data. Inset figure shows same plot and includes outlier samples (Serchio at Piaggione, Magra, Secchia, and Reno at Vergato). Linear regression is given by the solid, black line and R2 value. Including the outliers reduces the correlation between ions, as the outliers are enriched in Ca2+ relative to other rivers.

95 Flow Chart for Evaporite Contribution Correction

No correction Evaporite-bearing No catchment? required.

Yes

Use Ca+Mg/HCO3 ratio to correct Ca2+

Set Ca2+ concentration Is adjusted Ca2+ to 0 μmol/L and concentration ≥ 0 μ No subtract original Ca2+ mol/L? concentration from SO42- concentration.

Yes

Assume gypsum stoichiometry (1 mol Figure 5. Plot of HCO3of Ca- against2+ per mol Ca2+ of + Mg2+ concentrations. Linear regression is given by the solid, black line and R2 value.2- Red, dashed line2- and equation illustrate average ratio of HCO3/(Ca+Mg) for SO4 ) and correct SO4 all data. Inset figure concentrationshows same byplot and includes outlier samples (Serchio at Piaggione, Magra, Secchia, and Reno sameat Vergato). amount asLinear Ca2+ regression is given by the solid, black line and R2 value. Including the outliers reduces the correlation between ions, as the outliers are enriched in Ca2+ relative Figure 6. Flowto other chart rivers. for evaporite contribution correction.

4.3.4 Calculation of weathering fluxes 4.3.4 WeCalculation calculated of weathering weathering fluxes fluxes using the methods of West et al. (2005) on 21 sampling sites We calculatedfor which weathering we have fluxes solute usingfluxes, thedischarge methods measureme of Westnts, et and al. denudation (2005) onrates 21 derived sampling from sites forwhich we have solute fluxes, discharge measurements, and denudation rates derived from 10Be concentrations for 10Be concentrations for comparison. For silicate weathering, the contribution of major comparison. For silicate weathering, the contribution of major dissolved ions was calculated by summing the dissolved ions was calculated by summing the concentrations of the following species, concentrations of the following species, expressed as the total dissolved solids (TDS) derived from silicate 3 rocks (in kg/mexpressed3 or g/L): as the total dissolved solids (TDS) derived from silicate rocks (in kg/m or g/L):

(8)

2+ 2+ 2+ 2+ + + (8) As Ca and Mg can be TDSderivedsil= from[Ca bothsil]+ carbonateåMg silé+ and[Na silicate]+[K weathering,] we partition the

concentrations based on molar ratios of Casil/Na (0.35) and Mgsil/Na (0.24) in global stream126 As Ca2+ and Mg2+ can be derived from both carbonate and silicate weathering, we partition the concentrations chemistry (Gaillardet et al., 1999). The silicate chemical weathering flux (in g/m2/yr) was then based on molar ratios of Casil/Na (0.35) and Mgsil/Na (0.24) in global stream chemistry (Gaillardet et al., 1999). The silicatecalculated chemical using weathering the following flux equation:(in g/m2/yr) was then calculated using the following equation:

(9) Qriv*TDSsil Qriv (9) Flux= Ariv = Ariv * + + 2+ 2+ ìMNa*[Na ]sil+MK*[K ]sil+ MMg*[Mg ]sil+MCa*[Ca ]sil+(MSi+2*Mo)*[SiO2]î where Mx is the molar mass (g/mol) of the compound, and Qriv/Ariv is the runoff, expressed as the time-integrated where Mx is the molar mass (g/mol) of the compound, and Qriv/Ariv is the runoff, expressed as water discharge (m3/y) divided by the upstream drainage area (m2). the time-integrated water discharge (m3/y) divided by the upstream drainage area (m2). 96 2+ 2+ For carbonate weathering, the concentrations of Ca carb and Mg carb are assumed to be the difference between the total concentrations and the amount derived from silicates:

(10) 2+ 2+ 2+ 2+ TDScarb= åCa carb+Mg carbé = å C a total+ Mg totalé 2+ 2+ - åCa sil+Mg silé and the flux, is calculated as with . We compiled available daily discharge data

over the last 5Flu yearsxcarb from rivers around theFlu Northernxsil Apennines (Supplementary Figure 2). As the 10Be denudation rates and discharge data are integrated over different timescales, we averaged the daily rates over the entire year (Supplementary Table 3).

Total denudation (D) represents the sum of chemical weathering (TDS due to both silicates and carbonate) and physical erosion (E). Denudation rates from 10Be concentrations were calculated for rivers around the Northern Apennines in this study (Chapter 1) and previous studies (Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016). Although 10Be concentrations reflect the denudation of quartz, we assume this rate applies to the entire rock (i.e., both silicate and carbonate lithologies). Unlike the Central Apennines, there are no steep carbonate cliffs or large areas that drain exclusively quartz or carbonate lithologies, so we assume that the landscape is lowering at a uniform rate. The total physical erosion rate is then expressed as:

127

As Ca2+ and Mg2+ can be derived from both carbonate and silicate weathering, we partition the

concentrations based on molar ratios of Casil/Na (0.35) and Mgsil/Na (0.24) in global stream chemistry (Gaillardet et al., 1999). The silicate chemical weathering flux (in g/m2/yr) was then calculated using the following equation:

(9) Qriv*TDSsil Qriv Flux= Ariv = Ariv * + + 2+ 2+ ìMNa*[Na ]sil+MK*[K ]sil+ MMg*[Mg ]sil+MCa*[Ca ]sil+(MSi+2*Mo)*[SiO2]î

where Mx is the molar mass (g/mol) of the compound, and Qriv/Ariv is the runoff, expressed as the time-integrated water discharge (m3/y) divided by the upstream drainage area (m2).

2+ 2+2+ 2+ For carbonateFor carbonate weathering, weathering, the concentrations the concentrations of Ca carb andof Ca Mg carbcarb andare assumedMg carb areto be assumed the difference to be thebetween the total concentrationsdifference between and the the amount total concentrations derived from andsilicates: the amount derived from silicates:

(10) (10) 2+ 2+ 2+ 2+ TDScarb= åCa carb+Mg carbé = å C a total+ Mg totalé 2+ 2+ - åCa sil+Mg silé and the flux, is calculated as with . We compiled available daily discharge data and the flux, Fluxcarb, is calculated as with Fluxsil. We compiled available daily discharge data over the last 5 years fromover rivers the aroundlast 5Flu years thexcarb Northernfrom rivers Apennines around the (FigureFlu Northernxsil 7). As Apennines the 10Be denudation(Supplementary rates Figureand discharge 2). As data are integratedthe 10 overBe denudationdifferent timescales, rates and we discharge averaged datathe dailyare integratedrates over theover entire different year (Tabletimescales, 3). we averaged the daily rates over the entire year (Supplementary Table 3). Total denudation (D) flux represents the sum of chemical weathering (TDS due to both silicates and carbonate) and physical erosion (E). Denudation rates from 10Be concentrations were calculated for rivers around the Total denudation (D) represents the sum of chemical weathering (TDS due to both silicates and Northern Apennines in Chapter 2. Although 10Be concentrations reflect the denudation of quartz, we assume carbonate) and physical erosion (E). Denudation rates from 10Be concentrations were this rate applies to the entire rock (i.e., both silicate and carbonate lithologies). Unlike the Central Apennines, there are calculatedno steep carbonate for rivers cliffs around or large the Northernareas that Apennines drain exclusively in this studyquartz (Chapteror carbonate 1) and lithologies, previous so we 10 assume thatstudies the landscape (Cyr and is Granger, lowering 2008;at a uniform Cyr et rate. al., The2014; total Wittmann physical eterosion al., 2016) rate is. Althoughthen expressed Be as: concentrations reflect the denudation of quartz, we assume this rate applies to the entire rock (11) (i.e., both silicate and carbonate lithologies). Unlike the Central Apennines, there are no steep(11)(11) (11) sil carb E=D-(TDSsil + TDScarb ) carbonate cliffs or large areas E=DthatE=D -drain(TDS-(TDS exclusively+ Tsil+ TDSDScar )quartzb) or carbonate lithologies, so we To partitionTo the partition erosion the flux erosion into carbonatesflux into carb andonates silicates, and silicates,we use the we percent use the carbonate percent carbonatesand determined sand for assumeToTo partition partition that the the the landscape erosion erosion flux isflux lowering into into carb carb atonates aonates uniform and and silicates, rate. silicates, The we total we use usephysical the the percent percent erosion carbonate carbonate rate is thensand sand each river todetermined obtain the for carbonate each river erosion to obtain rate the carbonate erosion rate expresseddetermineddetermined as: for for each each river river to toobtain obtain the the carbonate carbonate erosion erosion rate rate

(12) (12)(12) (12) carb total carb 127 Ecarb =Etotal * %Sandcarb E Ecarb=E=Etotal* %Sand* %Sandcarb The silicateThe erosion silicate rate erosion is then rate the isremaining then the mass.remaining mass. TheThe silicate silicate erosion erosion rate rate is isthen then the the remaining remaining mass. mass.

(13) (13) (13)(13) sil total carb Esil =Etotal - Ecarb We propagate errors for all calculations usingE E =Esil the=E root-mean-squaretotal- E- Ecarb error. For reporting denudation and We propagate errors for all calculations using the root-mean-square error. For reporting weatheringWe We fluxes, propagate propagate we errors ignoreerrors for forthe all all terracecalculations calculations seeps using usingand the streamsthe root root-mean - meanfrom-square- square the error.Reno error. River, For For reporting asreporting we can only resolve denudation rates and weathering fluxes, we ignore the terrace seeps and streams from the Reno denudationdenudation fluxesdenudation from rates rates catchments and and weathering weathering that contain fluxes, fluxes, estimates we we ignore ignore of thedisc the terraceharge. terrace seeps seeps and and streams streams from from the the Reno Reno River, as we can only resolve denudation fluxes from catchments that contain estimates of River,River, as as we we can can only only resolve resolve denudation denudation fluxes fluxes from from catchments catchments that that cont containain estimates estimates of of discharge. discharge.discharge.

97

128 128128 2.19E+08 5.30E+08 2.88E+08 2.81E+08 5.81E+07 6.47E+088.17E+08 3.90E+08 8.97E+08 4.05E+08 3.66E+08 3.78E+08 4.44E+08 4.80E+08 1.00E+09 1.58E+087.82E+08 9.14E+07 2.28E+08 1.21E+082.26E+08 5.35E+07 2.81E+08 1.36E+08 1.41E+08 9.51E+07 3.67E+08 1.12E+08 6.82E+052.69E+08 2.92E+08 9.77E+08 3.26E+08 4.91E+08 2.29E+08 3.92E+08 5.76E+08 4.15E+08 2.29E+08 7.18E+08 1.44E+09 4.19E+081.89E+09 7.62E+08 8.21E+08 7.44E+08 9.82E+08 1.33E+09 8.89E+08 1.09E+09 Year 5.37E+08 5.11E+08 7.69E+08 3.66E+074.46E+07 5.10E+07 4.21E+07 9.61E+07 9.73E+07 9.61E+07 1.21E+08 7.34E+07 7.71E+07 1.60E+08 6.04E+07 1.73E+08 1.62E+08 4.37E+08 6.34E+08 9.11E+08 1.20E+093.21E+08 5.93E+08 4.15E+08 5.15E+08 5.69E+08 3.17E+08 6.67E+08 2.95E+08 4.05E+08 7.09E+08 6.15E+08 1.45E+08 7.13E+07 6.81E+07 1.15E+08 3.25E+08 2.08E+08 2.25E+08 3.06E+08 4.41E+08 6.27E+07 1.04E+08 4.60E+07 3.40E+07 1.11E+08 1.60E+08 1.57E+09 1.32E+09 8.67E+08 7.64E+08 1.27E+09 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 ) 2 52 1056 Area (km Upstream Longitude 44.07744.170 11.614 11.949 105 236 44.227 11.632 136 44.507 11.470 236 44.288 11.113 44.476 11.281 44.357 10.923 584 44.721 8.860 615 44.900 9.582 915 44.433 10.654 695 44.00743.923 10.564 11.127 302 44.712 152 44.635 9.57044.823 10.17144.805 10.22544.524 198 10.325 129 10.353 1374 423 297 44.16343.857 10.338 10.507 178 1251 9.91E+07 1.06E+08 1.66E+08 6.14E+07 7.41E+07 44.198 9.951 937 44.280 9.653 205 44.347 9.341 295 Latitude Average annual discharge over the last five available years of data for each catchment. Note Entella River had only two data. annual discharge Average . Vara Idice Lima Nure Taro Enza River Reno Senio Magra Scrivia Parma Panaro Entella Trebbia Secchia Lamone Vergato Bisenzio Baganza Montone Serchio F Serchio P Table 3 Table 98 9E 10E 11E 12E N 5 4

N 4 4

Florence

0 25 50 75 100 m

Figure 7. Location of Northern Apennine discharge stations for studied rivers.

4.4 Results

4.4.1 Physical carbonate erosion Catchment-wide percent carbonate sand varies from 17–76% (Figure 8, Tables 4) and longituinal samples have percent carbonate sand contents up to 85% (Baganza 3 sample, Table 5). The catchments with the lowest percent carbonate sand (Entella and Vara Rivers; Num. 5–6 on Figure 8) primarily drain the Internal Ligurian Unit (Figure 2). In contrast, the adjacent Bisagno and Recco Rivers (Num. 3–4 on Figure 8) drain the External Ligurian Unit and display carbonate sand contents that are up to a factor of 3 higher than the Entella and Vara Rivers. Other rivers on the Adriatic side also drain the External Ligurian Unit, and record carbonate sand values as high or higher than the Bisagno and Recco Rivers (Num. 3–4 on Figure 8).

The Nure, Baganza, and Parma catchment-wide samples (Num. 12, 14, 16 on Figure 8) record the highest carbonate sand fractions (67—71%). Rivers that primarily drain the Macigno and Cervarola Units (Magra, Serchio, Lima, and Bisenzio catchments; Num. 7–10 on Figure 8) have relatively low carbonate sand content, ranging from 25–28%. The rivers that drain the Marnoso Arenacea Unit (Senio and Lamone Rivers; Num 20–21 on Figure 8) record carbonate sand percentages similar to those in the Macigno and Cervarola Units (21–25%), except for the Montone River (42%) (Num. 22 on Figure 8).

99 Finally, we find that percent carbonate sand is weakly correlated (R2 = 0.27) with the pattern of 10Be denudation rates (Figure 9) from rivers around the Northern Apennines (Chapter 2; Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016).

For the longitudinal samples collected along the Bisagno, Baganza, and Nure Rivers (Table 5), there is no correlation or a weak positive correlation with increasing percent carbonate sand in the downstream direction (Figure 10). Along the Recco River, three of the sampling locations had only bare bedrock in the stream bed, so the strong correlation between percent carbonate sand and distance downstream (R2 = 0.70) is only applicable over a short stream distance of less than 5 km.

Table 4. Sampling locations, basin area, and percent carbonate sand for catchments in the Northern Apennines.

% Carbonate Basin Area Sand River/Location Latitude Longitude (km2) (250-500 µm) I. Piemonte 1 Staffora 44.893 9.057 264 49 2 Scrivia 44.719 8.861 615 57

II. Liguria and Tuscany 3 Bisagno 44.408 8.950 90 55 4 Recco 44.374 9.152 18 43 5 Entella 44.351 9.362 297 17 6 Vara 44.190 9.858 555 18 7 Magra 44.187 9.926 947 25 8 Serchio at Piaggone 43.930 10.506 1160 24 9 Lima 43.999 10.554 317 20 10 Bisenzio 43.928 11.126 149 28

III. Emilia Romagna 11 Trebbia 44.909 9.589 918 60 12 Nure 44.882 9.653 343 67 13 Taro 44.698 10.093 1250 63 14 Baganza 44.684 10.213 153 76 15 Parma 44.569 10.237 264 71 16 Enza 44.627 10.413 481 60 17 Secchia 44.543 10.767 1010 47 18 Panaro 44.420 10.925 680 38 19 Reno 44.392 11.257 990 36 20 Senio at Casola Valsenio 44.227 11.632 136 25 21 Lamone at Biforco 44.065 11.601 179 21 22 Montone at Davadola 44.121 11.885 190 42

100 Table 5. Sampling locations, basin area, and percent carbonate sand for longitudinal samples collected along the Bisagno, Recco, Baganza, and Nure Rivers.

% Carbonate Upstream Sand Basin Area (250-500 River/Location Latitude Longitude (km2) µm) II. Liguria and Tuscany 1 Bisagno 1 44.408 8.950 90.2 56 2 Bisagno 2 44.438 8.963 75.0 58 3 Bisagno 3 44.436 9.017 48.0 NA 4 Bisagno 4 44.452 9.033 45.8 81 5 Bisagno 5 44.441 9.045 34.4 55 6 Bisagno 6 44.444 9.085 13.2 38 7 Recco 1 44.411 9.153 1.4 NA 8 Recco 2 44.395 9.168 7.4 NA 9 Recco 3 44.382 9.159 12.3 38 10 Recco 4 44.379 9.153 12.6 38 11 Recco 5 44.374 9.152 19.0 43 III. Emilia Romagna 12 Baganza 1 44.498 9.988 14.2 63 13 Baganza 2 44.486 9.978 9.5 76 14 Baganza 3 44.525 10.007 27.1 85 15 Baganza 4 44.568 10.041 1.7 77 16 Baganza 5 44.606 10.123 105.8 77 17 Baganza 6 44.621 10.171 120.1 77 18 Baganza 7 44.684 10.213 153.3 76 19 Nure 1 44.882 9.653 342.2 67 20 Nure 2 44.812 9.615 295.8 65 21 Nure 3 44.751 9.597 239.8 62 22 Nure 4 44.706 9.566 193.8 60 23 Nure 5 44.676 9.568 160.7 56 24 Nure 6 44.647 9.501 71.2 57 25 Nure 7 44.673 9.590 28.6 65 26 Nure 8 44.657 9.581 27.4 42

101 9E 10E 11E 12E 2165 Elevation (m) N

0 5 4 Carbonate 17 76 1 Content (%)

12 2 14 11 13 3 15 16 4 5 17 6 7 18 19 8 20 22 9 21

N 10

4 4

0 25 50 75 100 m Figure 8. Percent carbonate sand for catchment around the Northern Apennines. Numbers correspond to catchment locations and details given in Table 4. 80 (14) R2 = 0.23 (15) (12) (13) 60 (11) (2)

(1) (17)

(22) 40 (18) (19)

% Carbonate Sand (10) (20) (7) (8) (21) 20 (9) (6) (5)

0 0 0.2 0.4 0.6 0.8 1 10Be Denudation Rate (mm/yr) Figure 9. 10Be Catchment-averaged denudation rates with 1 standard deviation, plotted against percent carbonate sand. Numbers in parentheses correspond to numbers given in Figure 8 and Table 4. Linear regression is given by the solid, black line and R2 value. 102 90

80 .

0 .3

0 ()

0 .

. 0

0 0 0 0 10 0 () Figure 10. Distance from outlet at Ligurian Sea (Bisagno and Recco Rivers) or mountain front (Nure and Baganza Rivers) plotted against percent carbonate sand in the upstream catchment area. Linear regressions are given by the solid lines, with colors corresponding to the data points, and R2 values.

4.4.2 River Water chemistry Rivers in the Northern Apennines are located in a similar climate, so we expect the evapotranspiration and precipitation to be similar between catchments. Therefore, absolute concentrations of major dissolved ions should reflect weathering rates in the catchments. In the following section, we present only uncorrected data (Tables 6-7) for all figures and plots as the corrections for rainwater, anthropogenic, and dust inputs result in only a small change in the concentration data.

103 Table 6. Uncorrected major dissolved ions concentrations for all sampling sites. Measure 23 39 43 28 88 ment Na K Ca Mg Cl SO4 HCO3 Si Sr River Type for ( mol/L) ( mol/L) ( mol/L) ( mol/L) ( mol/L) ( mol/L) ( mol/L) ( mol/L) ( mol/L) Cations Baganza ICPMS 𝞵𝞵𝞵𝞵736.55 𝞵𝞵𝞵𝞵43.30 1254.27𝞵𝞵𝞵𝞵 𝞵𝞵𝞵𝞵510.31 𝞵𝞵𝞵𝞵318.34 𝞵𝞵𝞵𝞵344.66 𝞵𝞵𝞵𝞵1490 𝞵𝞵𝞵𝞵123.58 𝞵𝞵𝞵𝞵12.89 Baganza ICPMS 142.69 12.06 1361.84 253.45 104.48 105.67 1260 96.12 4.30 Baganza ICPMS 129.27 9.64 1224.89 206.98 95.79 99.55 1200 89.91 3.61 Baganza ICPMS 261.51 24.57 1777.78 302.74 93.18 100.71 1940 82.09 10.93 Baganza ICPMS 367.79 34.58 1882.41 429.68 163.95 227.55 1920 105.43 12.67 Baganza ICPMS 353.00 34.50 1719.78 419.97 167.85 223.88 2020 90.61 11.01 Baganza ICPMS 444.87 46.85 1876.64 469.94 202.94 240.61 2040 120.75 11.46 Baganza ICPMS 178.46 16.34 1465.30 260.70 139.38 152.66 1580 86.59 5.99 Baganza ICPMS 205.07 27.94 2139.55 354.38 105.22 359.12 1880 83.59 12.84 Bisagno ICPMS 307.06 21.80 1625.90 165.46 361.89 140.55 1580 90.95 6.32 Bisagno ICPMS 271.02 19.82 1534.21 160.70 254.57 93.87 1400 84.51 5.90 Bisagno ICPMS 211.62 20.55 1391.51 143.23 321.55 106.65 1210 73.57 5.19 Bisagno ICPMS 242.62 15.31 1426.54 122.25 546.08 136.81 1160 81.89 4.97 Bisagno ICPMS 279.99 18.58 1607.41 165.77 385.59 143.68 1200 86.37 5.51 Bisagno ICPMS 239.24 16.83 1656.14 141.66 458.16 98.93 1300 83.16 5.75 Bisagno ICPMS 491.39 59.73 2147.46 282.11 698.75 243.13 1730 128.41 7.22 Bisenzio ICPMS 4538.70 81.37 2121.53 568.95 1605.15 803.86 1775 133.31 5.72 Entella ICPMS 244.02 14.17 753.82 164.45 250.43 85.58 680 96.17 1.64 Entella ICPMS 711.44 28.87 1173.14 261.08 370.06 105.30 1655 117.05 2.58 Enza ICPMS 923.74 60.15 1474.69 566.89 300.01 441.17 1235 88.55 8.94 Idice IC 2241.80 176.30 1891.60 683.80 1149.30 1393.10 1827 4.70 Idice IC 2257.60 172.80 1354.50 665.50 1167.60 1006.00 1652 4.80 Lamone ICPMS 1112.29 67.91 1412.08 921.90 366.24 440.53 1970 117.03 7.85 Lamone ICPMS 798.52 56.58 1469.37 867.03 234.23 359.83 1835 129.70 8.95 Lima ICPMS 148.97 11.43 597.45 109.10 107.10 61.20 779.3 73.65 1.14 Lima ICPMS 434.37 23.68 1185.65 304.89 299.88 414.32 990 76.43 4.84 Magra ICPMS 463.26 23.53 1179.91 185.73 473.08 225.89 940 117.80 3.79 Magra ICPMS 3369.79 51.55 3059.56 531.67 2446.39 1333.95 1290 74.12 14.36 Montone ICPMS 1224.42 81.65 1430.75 960.74 370.55 670.16 1680 109.09 9.96 Montone ICPMS 292.41 48.43 976.41 424.26 159.90 185.29 1180 78.42 4.44 Nievole ICPMS 625.51 36.10 1300.77 345.25 374.60 145.90 1678.5 135.64 2.03 Nure ICPMS 548.02 45.64 1444.13 759.90 178.68 300.73 1610 199.86 6.77 Nure ICPMS 641.61 38.07 1807.03 763.33 218.24 751.57 1900 133.20 9.67 Nure ICPMS 335.35 37.06 1665.10 574.97 152.47 276.20 1840 132.66 6.25 Nure ICPMS 246.92 27.19 1524.15 492.84 192.91 228.92 1800 108.29 5.41 Nure ICPMS 254.83 26.00 1721.61 497.08 123.91 252.59 1680 118.80 5.48 Nure ICPMS 218.57 25.46 1441.83 511.06 125.01 186.93 1760 159.20 4.69 Nure ICPMS 305.11 37.37 1629.84 556.57 123.04 224.96 2120 103.53 5.71 Nure ICPMS 198.70 19.23 1414.35 431.45 134.32 186.59 1600 127.28 4.31 Nure ICPMS 253.97 27.71 1861.77 369.70 120.56 247.71 1840 96.96 6.87 Nure ICPMS 239.73 28.41 1188.07 647.69 123.13 173.83 1740 177.11 3.21 Nure ICPMS 118.25 14.41 925.20 546.18 71.84 97.47 1400 178.79 3.20 Panaro IC 345.70 45.80 921.00 305.30 217.80 225.60 1091 Panaro ICPMS 644.67 55.76 1363.41 467.86 276.19 289.29 1300 79.98 5.05 Parma ICPMS 749.74 42.24 1316.26 435.78 249.68 322.02 1435 81.30 8.83 Pescia ICPMS 397.53 25.20 646.09 165.28 64.60 53.50 839.2 162.43 0.82 Pescia ICPMS 438.52 25.92 814.42 180.12 949.80 354.10 1049.1 121.52 1.39 Recco ICPMS 460.95 36.19 1376.69 200.65 392.47 158.05 960 81.93 5.86

104 Recco ICPMS 355.58 27.38 1703.88 168.48 409.44 170.14 1420 92.11 6.20 Recco ICPMS 354.67 28.29 1806.25 173.63 449.97 169.11 1510 106.82 6.28 Recco ICPMS 325.31 23.90 1791.58 155.25 616.01 205.30 1680 94.44 6.48 Recco ICPMS 278.05 16.45 1781.87 118.19 370.27 120.56 1900 78.27 6.44 Recco ICPMS 337.05 27.05 1449.77 177.24 383.40 187.29 1370 107.38 5.16 Reno ICPMS 1000.03 213.99 5410.61 1958.16 842.00 1390.20 3996.4 397.00 21.03 Reno ICPMS 868.40 187.40 3377.20 3329.00 798.70 1361.20 4387 10.20 Reno ICPMS 817.22 164.97 5268.65 1631.81 927.44 1011.51 297.51 17.67 Reno IPCMS 1042.87 165.15 4521.48 2111.37 776.50 1482.70 3551.8 347.43 20.53 Reno IC 893.70 218.70 2866.90 1872.20 844.50 1370.70 2943 10.10 Reno ICPMS 577.94 128.23 4159.90 1771.45 518.83 739.52 294.73 18.33 Reno ICPMS 1633.32 91.46 3890.54 1682.98 413.60 1288.10 4585.9 532.65 21.68 Reno IC 1328.70 90.00 1706.20 3760.70 396.80 1184.80 4800 8.40 Reno ICPMS 1651.29 104.89 4882.92 1551.48 2100.39 1109.39 411.79 21.48 Reno ICPMS 2563.49 334.32 3361.63 694.24 2170.43 444.26 265.60 10.60 Reno ICPMS 2335.07 111.98 4838.50 1136.74 4225.90 937.90 2877.4 333.94 17.25 Reno ICPMS 709.13 82.48 3303.95 1258.52 640.65 448.98 323.10 6.89 Reno ICPMS 987.98 91.72 3308.49 1339.61 622.90 582.50 3496.9 394.41 7.04 Reno IC 841.60 100.20 1941.80 1031.40 643.50 545.40 2533 Reno ICPMS 940.36 80.91 2840.23 1067.98 296.80 772.80 2997.3 146.44 9.98 Reno IC 804.00 83.80 1661.70 785.60 336.40 770.20 1900 Reno ICPMS 834.40 78.33 3577.65 1103.97 263.55 336.60 116.02 12.05 Reno ICPMS 586.63 44.36 2431.86 1430.26 256.07 585.78 334.74 4.53 Reno ICPMS 835.32 51.16 3524.30 2005.07 192.30 500.50 3257.1 439.13 6.80 Reno IC 550.60 44.50 1016.40 3770.70 259.80 469.90 4488 Reno ICPMS 480.82 28.28 1183.84 332.35 231.20 251.90 1408.7 35.92 3.41 Reno IC 541.30 43.90 883.30 296.80 384.20 235.20 1049 Reno ICPMS 439.16 35.22 1243.81 370.74 466.25 1389.27 29.21 3.95 Reno ICPMS 3156.45 206.85 4057.24 2222.63 878.90 2902.60 3696.7 273.76 24.89 Reno ICPMS 5696.99 225.94 4379.59 2488.94 342.10 1027.50 2947.3 301.41 32.15 Reno ICPMS 1119.01 55.33 1633.29 489.50 540.80 532.20 1698.5 114.54 7.73 Reno ICPMS 2908.42 82.86 2124.82 1203.86 1140.30 1259.00 2357.9 49.89 14.59 Reno ICPMS 448.52 64.82 1148.75 314.31 219.40 211.30 1448.7 45.79 3.07 Reno ICPMS 212.26 93.10 872.77 226.96 144.70 133.40 1049.1 223.52 1.79 Reno IC 362.10 38.00 1328.90 312.00 256.60 265.30 1455 Reno IC 1334.10 122.20 1443.00 730.70 617.80 810.40 1790 5.70 Reno ICPMS 1517.03 226.97 2527.14 919.52 404.50 1008.00 3225 166.37 7.27 Reno ICPMS 1695.48 48.81 2220.06 954.88 398.30 1043.00 2884 70.84 9.90 Reno IC 1639.40 111.70 1642.70 677.60 564.20 984.10 1937 4.80 Reno ICPMS 185.17 23.97 866.66 209.50 646.10 692.10 1109 108.05 1.67 Reno IC 251.50 35.90 1063.10 280.40 208.90 165.20 1221 Reno ICPMS 168.09 98.92 1031.24 303.42 115.00 122.40 1198.9 249.52 1.90 Reno ICPMS 203.20 18.00 1056.05 247.03 119.60 126.20 1149 84.54 2.09 Reno IC 1172.10 78.90 1306.20 444.40 676.20 640.70 1444 43.30 Reno ICPMS 213.67 70.94 1464.86 345.96 136.40 195.90 1681 176.81 2.80 Reno ICPMS 889.20 16.57 1710.14 396.19 496.10 522.10 1938.3 31.32 6.68 Reno ICPMS 273.90 49.45 1200.78 295.33 350.70 705.70 1368.8 95.17 3.04 Reno ICPMS 1750.66 82.87 1527.96 977.34 560.20 1136.40 1918.3 37.15 9.78 Reno ICPMS 4779.04 636.41 3157.74 2163.35 291.50 361.90 1398.7 588.55 23.91 Savena IC 2344.60 108.00 1522.40 1033.50 387.40 428.80 3166 8.70

105 Savena ICPMS 977.60 34.71 1780.10 543.21 270.50 589.00 1848.3 46.67 9.98 Savena IC 1100.10 84.00 1356.20 537.40 415.30 738.70 1593 50.40 Scrivia ICPMS 373.43 25.30 1546.29 248.96 334.87 202.15 1930 89.81 6.63 Secchia ICPMS 10228.80 101.11 3945.57 881.55 6135.02 3318.18 1100 76.27 25.09 Senio ICPMS 1400.61 86.29 1425.95 1228.19 427.34 623.83 2050 117.46 7.92 Serchio ICPMS 319.27 26.45 1684.06 320.12 180.83 540.59 1300 105.42 5.71 Serchio ICPMS 1596.17 48.59 3714.54 1323.65 1117.69 3340.49 1175 133.86 31.44 Staffora ICPMS 892.74 71.73 1410.71 728.19 329.78 426.59 1535 254.37 10.83 Taro ICPMS 349.15 37.74 1498.11 470.82 169.93 236.31 1820 97.14 6.34 Taro ICPMS 585.04 57.44 1404.91 571.55 209.04 249.40 1480 136.52 6.56 Trebbia ICPMS 344.13 22.09 1401.15 346.97 304.74 214.47 1360 103.69 5.29 Trebbia ICPMS 968.29 38.57 1357.86 459.14 684.88 234.74 1380 150.22 6.68 Vara ICPMS 263.74 18.88 689.28 163.02 251.51 102.14 760 136.58 1.56 Vara ICPMS 412.78 26.17 1166.85 371.63 244.10 139.06 1295 119.29 2.78

106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 10 19 0 8 8 1 1 1 10 10 9 9 1 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 1 1 0 18 10 1 9 11 9 1 18 10 91 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 8 91 8 8 9 1 11 19 1 11 1 9 1 1 1 89 8 11 1 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 99 8 9 8 1 1 1 9 8 9 19 0 1 1 18 90 1 1 18 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0000 0000 0000 0000 0 1 1 1 1 1 1 0 0 8 0 11 0 11 1 1 0 0 1 00 8 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 9 9 8 8 0 8 0 1 1 0 9 19 8 19 1 1 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 1 1 0 0 9 9 8 19 88 1 8 1 8 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 9 8 1 1 1 1 1 1 18 9 0 10 9 1 81 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1 1 9 8 0 99 11 0 11 99 1 10 9 1 1 1 19 19 1 1 19 1 9 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 8 0 1 1 9 1 9 1 9 0 0 1 0 1 1 1 1 3 0 00 00 00 00 01 00 01 00 01 009 008 018 008 0 08 9 00 009 09 00 01 008 00 00 01 01 01 00 0 01 01 08 01 0 01 0 01 08 0 09 01 01 008 009 0 01 01 00 0 01 19 01 0 09 11 19 1 01 0 00 0 0 0 1 019 0 0 90 90 11 10 0 09 8 0 4 000 000 000 000 000 000 000 000 000 000 000 000 000 000 81 000 000 000 000 000 000 000 000 000 000 000 000 3 000 000 000 000 000 000 000 000 000 000 000 000 000 000 8 000 000 00 00 01 01 0 0000 0000 0000 10 0 8 10 1 1 91 1 80 9 ) 00 000 000 000 9 0 8 00 19 0 000 000 1 000 000 000 000 0 10 000 000 000 8 000 000 000

( ti 0 1 1 1 1 1 1 1 1 1 ti Dissolved trace element concentrations in water samples. . 000 000 000 0 11 00 8 000 000 119 0 018 1 All concentrations in Table 7 Table 107 1.0E-02 1.0E-02 9.9E-03 6.1E-03 5.6E-03 5.8E-03 5.1E-03 5.9E-03 6.6E-03 5.8E-03 5.8E-03 5.7E-03 4.5E-03 1.4E-02 1.1E-02 1.0E-02 8.3E-03 6.2E-03 4.5E-03 3.5E-03 2.2E-03 5.9E-03 1.0E-02 1.1E-02 1.0E-02 8.2E-03 3.0E-02 1.0E-02 2.9E-03 1.8E-02 8.4E-03 2.6E-02 5.3E-02 5.7E-02 1.0E-02 1.0E-01 7.1E-03 1.8E-02 2.6E-02 1.2E-02 4.0E-04 4.2E-02 4.3E-02 1.4E-02 1.8E-02 6.4E-02 2.2E-02 1.0E-02 1.6E-02 7.0E-02 9.3E-02 2.9E-01 0.0E+00 0.0E+00 3.0E-02 1.0E-02 2.0E-02 1.9E-02 1.7E-02 1.9E-02 1.9E-02 1.4E-02 1.7E-02 1.6E-02 1.4E-02 1.4E-02 8.6E-03 1.9E-02 8.5E-03 1.0E-02 2.7E-02 1.4E-02 1.3E-02 1.2E-02 1.1E-02 1.3E-02 1.0E-02 8.9E-03 1.0E-02 1.3E-02 1.2E-01 2.2E-02 3.3E-02 2.8E-02 3.5E-02 3.0E-02 3.6E-02 2.5E-02 3.7E-02 2.0E-02 3.1E-02 4.8E-02 1.5E-02 5.0E-03 4.9E-03 2.7E-03 2.0E-03 1.8E-03 2.3E-03 3.6E-03 1.0E-02 1.0E-02 1.0E-02 6.4E-03 1.2E-01 0.0E+00 0.0E+00 0.0E+00 6.0E-02 3.0E-02 9.4E-02 4.0E-02 2.1E-01 1.6E-01 7.6E-02 4.3E-01 3.0E-01 1.8E-01 1.9E-01 1.3E-02 4.0E-02 5.0E-02 9.3E-02 7.3E-02 3.4E-02 1.3E-02 2.9E-01 1.0E-02 1.7E-01 5.0E-02 2.2E-01 7.3E-01 1.2E+00 1.9E+00 2.6E+00 2.3E-01 4.0E-02 1.4E-02 1.2E-01 3.3E-02 3.7E-01 7.2E-02 1.8E-02 4.6E-02 5.0E-01 3.8E-02 4.2E-02 2.5E-02 9.9E-01 7.6E-03 1.0E-02 3.1E-02 2.0E-02 1.3E-02 9.6E-03 4.1E-03 4.8E-02 1.6E-02 3.7E-02 7.0E-02 0.0E+00 0.0E+00 1.0E-02 3.0E-02 1.7E-02 2.6E-02 3.1E-02 3.1E-02 4.5E-02 2.3E-02 4.2E-02 6.2E-03 5.1E-02 8.6E-02 4.2E-03 1.9E-03 1.0E-02 2.0E-03 3.1E-03 3.9E-03 2.9E-03 2.7E-03 3.8E-03 1.0E-02 5.7E-03 6.0E-03 1.0E-02 0.0E+00 0.0E+00 1.0E-02 1.0E-02 8.0E-03 3.0E-03 4.4E-03 5.4E-03 6.7E-03 6.3E-03 5.5E-03 8.2E-03 4.1E-03 9.6E-03 8.2E-03 6.7E-03 2.3E-03 1.0E-02 2.0E-03 2.6E-03 2.5E-03 2.5E-03 2.0E-03 2.3E-03 3.7E-03 5.8E-03 1.0E-02 0.0E+00 0.0E+00 1.9E-03 7.0E-04 1.4E-03 2.4E-03 1.5E-03 1.4E-03 1.9E-03 2.8E-03 5.8E-04 1.7E-03 9.9E-04 5.2E-03 1.5E-03 2.2E-03 1.4E-03 2.7E-04 4.1E-04 2.1E-04 1.0E-03 9.0E-04 1.0E-02 3.1E-03 1.0E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 4.2E-05 2.0E-05 1.3E-04 4.6E-05 5.8E-06 6.7E-05 3.3E-04 8.2E-05 5.1E-05 2.8E-04 2.5E-05 3.7E-05 4.6E-05 7.9E-06 7.4E-05 3.4E-05 5.8E-05 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 -1.1E-04 -9.4E-05 -7.3E-05 -3.7E-05 0.12 0.36 0.03 0.02 0.08 0.23 0.08 0.11 0.11 0.29 0.11 0.14 0.04 0.09 0.04 0.22 0.11 0.21 0.18 0.19 0.12 0.20 0.40 0.28 0.40 0.21 0.45 0.04 0.09 0.17 0.08 0.22 0.95 0.22 0.11 0.45 1.78 0.34 0.19 0.19 2.65 0.11 0.11 0.20 0.23 0.14 0.12 0.10 0.38 0.03 0.16 0.05 0.20 0.75 0.09 0.10 1.25 1.68 0.90 0.62 0.60 0.53 0.96 0.43 0.75 0.72 0.22 8.50 1.57 1.77 0.06 0.23 0.27 0.31 0.28 0.14 0.33 8.33 9.19 12.24 10.80 11.92 10.90 15.66 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.10 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 87.40 34.12 16.53 17.53 11.30 76.50 33.70 4239.80 2196.00 2893.69 3416.20 1142.10 2115.06 0.00 5.89 2.22 2.89 2.21 2.04 2.27 2.37 0.00 0.00 0.00 2.30 0.00 0.00 0.00 2.13 0.00 0.00 0.00 0.00 7.50 7.20 13.48 11.96 0 1 2 3 4 5 7 1 2 1 1 3 4 5 6B 8B na 2E 6A 8A 2W Collodi Qt5-1B Qt5-1C Qt6-1B Qt6-1C Qt5-1A Qt6-1A Qt6-2A Pisotiese Pietrabuo Serravalle Nure Nure Nure Nure Nure Nure Nure Nure Nure Nure Nure Reno Reno Reno Reno Reno Reno Reno Recco Recco Recco Recco Recco Recco Parma Pescia Pescia Panaro Panaro Nievole

108 3.4E-02 1.2E-01 3.0E-02 5.2E-03 1.0E-02 1.0E-02 9.5E-03 1.2E-02 2.0E-02 1.0E-02 8.1E-03 2.0E-02 5.0E-02 1.0E-02 2.0E-02 1.0E-02 1.0E-02 1.0E-02 0.0E+00 0.0E+00 4.1E-01 3.5E-02 1.0E-02 2.1E-02 1.0E-02 1.5E-02 4.8E-02 2.0E-02 1.0E-02 2.6E-02 1.0E-02 1.0E-02 1.0E-02 1.0E-02 1.0E-02 1.0E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.9E-01 1.4E-02 2.0E-02 4.7E-03 8.8E-03 1.6E-03 1.0E-02 1.3E-02 2.0E-02 2.0E-02 1.0E-02 3.0E-02 1.0E-02 3.0E-02 3.0E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.3E-01 5.0E-02 9.0E-02 5.2E-03 1.0E-02 1.0E-02 1.1E-02 8.4E-03 1.0E-02 1.0E-02 6.8E-03 1.0E-01 1.5E-01 1.0E-02 3.0E-02 1.0E-02 2.0E-02 0.0E+00 0.0E+00 0.0E+00 8.2E-01 4.0E-02 1.2E-01 2.0E-02 1.0E-02 9.5E-02 4.0E-02 9.1E-02 2.1E-01 2.0E-02 3.0E-02 5.0E-02 1.0E-02 4.0E-02 1.2E-01 1.0E-02 1.4E+00 5.4E+00 4.2E+00 1.1E+00 4.1E-02 2.8E-01 1.6E-02 7.0E-02 8.5E-03 7.0E-02 5.4E-02 1.3E-01 3.6E-01 2.0E-02 1.0E-02 1.0E-01 1.0E-02 1.1E-01 1.0E-02 1.2E+00 0.0E+00 1.6E+00 1.3E+00 0.0E+00 1.1E-02 8.7E-03 1.3E-03 7.3E-04 9.1E-04 5.2E-03 1.0E-02 1.0E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.5E-02 2.0E-02 2.0E-02 2.6E-03 8.0E-04 3.0E-03 2.1E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.4E-03 8.5E-03 1.3E-03 6.7E-04 1.8E-03 1.1E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 6.1E-05 1.7E-04 1.1E-05 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 -4.4E-05 -6.1E-05 -6.6E-06 0.08 0.21 0.23 0.12 0.05 0.19 0.10 0.22 0.32 0.20 0.15 0.16 0.29 0.06 0.04 0.18 0.04 0.51 0.11 91.57 0.24 1.47 0.04 0.18 0.02 0.03 0.16 0.24 0.04 0.04 0.10 0.02 0.13 0.05 0.02 0.02 0.05 0.06 0.08 0.05 2.66 4.04 2.90 3.75 9.10 3.65 8.80 2.95 4.63 7.39 9.50 1.56 8.40 1.30 2.21 4.40 1.07 0.16 9.10 3.31 4.54 9.50 0.16 11.40 10.38 20.06 26.09 11.90 19.60 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.00 0.00 0.70 0.00 2.80 0.00 0.00 0.00 1.00 1.90 0.00 0.00 0.60 0.00 0.00 1.10 0.40 0.00 0.40 0.00 41.65 0.00 0.00 0.00 3.00 0.00 0.00 0.00 8.30 7.60 8.30 0.00 0.00 0.00 0.00 5.30 -4.00 -6.60 13.84 93.90 12.90 10.50 12.00 432.22 191.30 185.10 110.90 347.35 1433.13 1092.50 2.70 2.90 2.42 3.70 0.20 4.19 3.70 4.80 3.80 1.60 17.60 29.66 11.64 12.76 26.17 6 7 5B 5C 12 19 20 24 25 26 28 22 5A 29A 29B Qt6-2B Qt6-2C Qt6-3B Qt6-3C Qt9-4B Qt9-4C Qt9-5B Qt9-5C Qt6-3A Qt6-3D Qt6-4A Qt9-4D Qt9-5A Limentra Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno Reno

109 1.0E-02 1.0E-02 1.0E-02 2.0E-02 3.0E-02 1.0E-02 4.6E-03 3.0E-02 1.8E-02 9.4E-03 6.7E-02 1.3E-02 5.5E-03 1.3E-02 5.2E-03 1.3E-02 4.3E-03 7.4E-03 0.0E+00 0.0E+00 1.0E-02 1.0E-02 1.0E-02 1.0E-02 2.0E-02 1.0E-02 5.5E-03 5.2E-03 8.2E-03 2.1E-03 8.6E-03 6.5E-03 8.9E-03 2.5E-03 1.4E-02 2.7E-04 7.7E-03 1.2E-04 0.0E+00 0.0E+00 1.0E-02 1.0E-02 1.0E-02 4.0E-02 2.0E-02 1.0E-02 1.1E-02 2.1E-02 1.4E-02 8.7E-03 4.5E-03 1.7E-02 1.7E-02 2.2E-02 8.5E-03 5.7E-03 7.3E-03 6.8E-03 0.0E+00 0.0E+00 1.0E-02 2.0E-02 1.2E-01 1.0E-02 1.1E-02 2.7E-02 1.7E-02 2.5E-03 2.4E-03 1.8E-02 2.7E-02 1.9E-02 2.5E-02 1.3E-02 7.0E-03 8.0E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 1.0E-02 2.0E-02 1.0E-02 2.0E-02 6.0E-02 9.0E-02 9.4E-01 7.0E-02 2.3E-01 2.7E-02 1.1E-01 3.1E-02 8.2E-02 3.3E-02 2.4E-01 1.3E-01 4.6E-01 2.1E-01 2.1E-01 5.2E-02 1.0E-02 1.0E-02 4.0E-02 8.0E-02 2.6E-01 5.0E-02 3.0E-02 6.3E-02 1.4E-01 3.7E-02 3.2E-02 1.2E-02 3.8E-02 3.5E-02 8.5E-02 1.1E-02 7.9E-02 7.8E-02 0.0E+00 0.0E+00 1.0E-02 1.0E-02 1.0E-02 6.7E-03 1.7E-03 9.5E-04 7.4E-03 6.3E-03 3.8E-03 1.1E-02 8.8E-03 1.9E-02 1.9E-02 1.0E-02 1.8E-02 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 3.4E-03 7.8E-03 1.1E-02 1.6E-02 8.9E-03 1.1E-02 3.2E-03 6.6E-03 3.9E-03 6.8E-03 2.9E-03 5.9E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 6.9E-04 1.9E-03 1.9E-03 1.7E-03 2.3E-03 1.6E-03 1.2E-03 2.3E-03 1.5E-03 1.7E-03 1.9E-03 1.2E-03 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 5.2E-05 7.5E-05 4.0E-05 4.1E-05 2.2E-04 3.9E-05 1.0E-04 4.6E-05 1.6E-05 3.7E-05 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 -2.4E-05 -2.3E-05 0.21 0.39 0.70 0.10 0.42 0.36 0.19 0.07 0.06 0.07 0.49 0.12 0.04 0.04 0.15 0.16 0.11 0.03 0.12 0.02 0.05 0.05 0.08 0.09 0.18 0.06 0.11 0.03 0.32 0.11 0.20 0.12 0.19 0.06 0.26 0.24 0.34 0.28 0.44 0.09 0.20 0.20 9.00 0.39 1.65 0.57 3.95 9.50 3.21 9.10 0.82 3.06 2.14 0.28 6.27 3.16 1.18 1.62 0.94 2.10 0.21 0.22 16.68 1.40 0.00 0.00 0.00 0.00 0.30 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9.70 0.00 6.50 3.00 0.00 4.60 0.70 0.00 0.00 0.00 0.00 9.07 0.00 0.00 0.00 26.10 23.90 28.95 18.20 43.51 40.34 38.33 10.60 2.30 3.10 2.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14.70

1 2 1 2 1 2 Silla a di Setta Setta Setta Senio Terme Scrivia Filicaia Treppio Savena Secchia Vergato Vergato Staffora Poretta Savena-1 Savena-2 Piaggone Limentrell Taro Taro Reno Reno Reno Reno Reno Reno Vara Vara Reno Reno Senio Scrivia Savena Savena Savena Secchia Serchio Serchio Trebbia Trebbia Staffora

110 4.4.2.1 14C and δ13C Percent modern carbon (pMC) values vary between ~55–90% (Table 8), with a distribution centered around 86% modern (Figure 11), and a single outlier at 55.26% (Serchio River). Values of δ13C values vary between 2- -12.4‰ and -0.40‰ (Table 8) and are shown in Figure 12 plotted against the proportion SO4 , measured in percent of total anion charge (Galy and France-Lanord, 1999; Emberson et al., 2018).

Sample Charge n Elev Water T Fractio d13C Catchment Number Latitude Longitude Date pH Balance (m) Type ℃ modern (‰) /Location Error (14C) Baganza 0 44.605 10.120 380 15.07.18 River 8.39 25.6 3.87 0.82 -9.53 Baganza 1 44.498 9.988 807 20.03.18 River 8.34 4.4 8.83 0.82 Baganza 2 44.486 9.978 926 20.03.18 River 8.34 4.1 5.40 Baganza 4 44.568 10.041 651 20.03.18 River 8.65 4.2 3.13 Baganza 5 44.606 10.123 385 21.03.18 River 8.46 4 5.94 Baganza 6 44.635 10.171 302 21.03.18 River 8.4 4.8 0.07 -9.43 Baganza 7 44.684 10.213 200 21.03.18 River 8.42 5.4 4.18 Baganza 3A 44.525 10.007 705 20.03.18 River 8.39 4.5 0.55 Baganza 3B 44.525 10.007 705 20.03.18 River 8.41 4.6 6.47 Bisagno 1 44.408 8.950 8 18.03.18 River 8.33 9.6 1.38 Bisagno 2 44.438 8.963 33 19.03.18 River 8.37 9.1 6.30 0.89 -9.78 Bisagno 4 44.452 9.033 107 19.03.18 River 8.44 7.6 5.54 Bisagno 5 44.441 9.045 134 19.03.18 River 8.54 9.1 3.32 0.88 -8.96 Bisagno 6 44.444 9.085 274 19.03.18 River 8.4 8.9 11.13 Bisagno 3A 44.449 9.020 80 19.03.18 River 8.42 8.4 8.38 Bisagno 3B 44.449 9.020 80 19.03.18 River 8.6 9.6 7.58 Bisenzio 1 43.926 11.127 118 15.07.18 River 8.84 26 19.32 0.75 Entella 1 44.351 9.362 12 20.03.18 River 8.03 9.4 8.08 0.86 -7.68 Entella 2 44.351 9.362 6 15.07.18 River 8.11 20.6 -3.76 0.78 Enza 1 44.627 10.413 146 15.07.18 River 8.39 22.9 16.22 0.87 Idice 1 44.429 11.430 73 04.07.17 River Idice 2 44.425 11.427 74 04.07.17 River Lamone 1 44.169 11.688 187 15.07.18 River 8.45 20.5 5.99 0.88 Lamone 2 44.065 11.601 326 15.07.18 River 8.57 18.7 8.91 0.81 Lima Cutigliano 44.099 10.752 594 04.05.17 River 7.8 7.9 -6.70 Lima Lima 43.999 10.554 102 15.07.18 River 8.64 24 5.05 0.80 Magra 1 44.187 9.926 55 20.03.18 River 8.19 9.9 6.81 0.86 -9.42 Magra 2 44.187 9.924 28 15.07.18 River 8.42 22.5 15.90 0.82 Montone 1 44.121 11.885 123 15.07.18 River 8.43 21.4 9.12 0.86 Montone 2 43.982 11.689 530 15.07.18 River 8.59 16 4.17 0.88 Serravalle Nievole Pisotiese 43.906 10.816 62 04.05.17 River 8.86 16 -3.03 Nure 0 44.828 9.617 258 15.07.18 River 8.35 24.8 11.13 0.90 Nure 1 44.882 9.653 178 21.03.18 River 8.23 7.8 2.58 Nure 2 44.812 9.615 272 21.03.18 River 8.52 6.3 5.04 -8.93 Nure 3 44.751 9.597 352 21.03.18 River 8.5 5.9 0.64 Nure 4 44.706 9.566 429 21.03.18 River 8.45 6.3 8.35 Nure 5 44.676 9.568 471 21.03.18 River 8.43 5.5 1.58 -9.22 Nure 7 44.673 9.590 504 21.03.18 River 8.63 5.1 -1.05 Nure 6A 44.647 9.509 589 21.03.18 River 8.45 4.3 2.62 Nure 6B 44.647 9.509 589 21.03.18 River 8.54 4.4 4.96 Nure 8A 44.657 9.581 526 21.03.18 River 8.58 4.6 -0.14 Nure 8B 44.657 9.581 526 21.03.18 River 8.48 4.7 0.14 Panaro 1 44.420 10.924 169 04.07.17 River Panaro 2 44.420 10.925 161 15.07.18 River 8.68 24.2 11.15 0.86 Parma 1 44.569 10.237 321 15.07.18 River 8.47 23.8 6.14 0.82 Pescia Collodi 43.898 10.654 101 04.05.17 River 8.49 15.2 4.32 111 Pescia Pietrabuona 43.922 10.693 95 04.05.17 River 8.79 14.5 -22.88 Recco 1 44.411 9.150 305 19.03.18 River 8.52 9.1 16.29 Recco 3 44.382 9.159 39 19.03.18 River 8.46 10.5 6.94 Recco 4 44.379 9.153 20 20.03.18 River 8.24 8.6 6.56 Recco 5 44.374 9.153 28 20.03.18 River 8.4 8.8 -1.67 0.90 -10.18 Recco 2E 44.395 9.168 84 19.03.18 River 8.43 10.2 -3.72 Recco 2W 44.395 9.168 84 19.03.18 River 8.48 10.3 1.69 0.86 -9.79 Reno Qt5-1A 44.348 11.218 162 01.05.17 Terrace 7.13 12.9 -11.50 Reno Qt5-1B 44.348 11.218 162 02.07.17 Terrace Reno Qt5-1C 44.348 11.218 166 01.05.18 Terrace Reno Qt6-1A 44.345 11.210 143 01.05.17 Terrace 7.63 13.1 -9.97 Reno Qt6-1B 44.345 11.210 143 02.07.17 Terrace Reno Qt6-1C 44.345 11.210 139 01.05.18 Terrace Reno Qt6-2A 44.355 11.215 135 01.05.17 Terrace 7.61 13.7 2.56 Reno Qt6-2B 44.355 11.215 135 02.07.17 Terrace Reno Qt6-2C 44.355 11.215 140 01.05.18 Terrace Reno Qt6-3A 44.361 11.213 135 01.05.18 Terrace Reno Qt6-3B 44.361 11.213 127 02.05.17 Terrace 7.84 17.3 9.68 Reno Qt6-3C 44.414 11.272 89 01.05.18 Terrace Reno Qt6-3D 44.414 11.272 83 03.05.17 Terrace 7.1 13.7 6.20 Reno Qt6-4A 44.414 11.272 83 02.07.17 Terrace Reno Qt9-4B 44.338 11.213 126 01.05.17 Terrace 7.33 13.4 3.69 Reno Qt9-4C 44.338 11.213 126 02.07.17 Terrace Reno Qt9-4D 44.338 11.213 140 01.05.18 Terrace Reno Qt9-5A 44.417 11.281 99 01.05.18 Terrace Reno Qt9-5B 44.417 11.281 99 03.05.17 Terrace 7.62 12.8 21.51 Reno Qt9-5C 44.417 11.281 99 02.07.17 Terrace Reno 5A 44.338 11.213 126 01.05.17 River 8.79 15.1 -0.63 Reno 5B 44.338 11.213 126 02.07.17 River Reno 5C 44.338 11.213 124 01.05.18 River Reno 6 44.351 11.217 113 01.05.17 Stream 8.08 12.4 -1.05 Reno 7 44.339 11.214 142 01.05.17 Stream 8.41 11.1 40.66 Reno 12 44.362 11.257 107 03.05.17 River 8.46 17.4 3.79 Reno 19 44.330 11.191 130 05.05.17 Stream 8.74 15 6.98 Reno 20 44.317 11.185 145 05.05.17 River 8.46 13.8 -1.67 Reno 24 44.104 10.998 533 05.05.17 River 8.25 11.4 -0.55 Reno 25 44.183 10.971 329 02.07.17 River Reno 26 44.281 11.114 191 02.07.17 River Reno 28 44.204 11.013 311 06.05.17 Stream 7.59 11.7 -1.35 Reno 29A 44.225 11.031 254 06.05.17 River 8.53 12.5 -0.97 Reno 29B 44.225 11.031 254 02.07.17 River Reno 22 44.079 11.051 584 05.05.17 River 8.31 11.2 -28.67 Reno Limentra 44.104 10.998 533 02.07.17 River

Limentrella Reno di Treppio 44.085 11.044 518 05.05.17 River 8.34 10.5 2.71 Poretta Reno Terme 44.100 10.962 483 04.05.17 River 8.37 10.1 1.89 Reno Setta 44.362 11.257 107 02.07.17 River

112 Reno Setta 44.115 10.967 538 04.05.17 Stream 8.35 9.4 -0.40 Reno Setta 44.274 11.196 215 05.05.17 River 8.23 15.1 -2.95 Reno Silla 44.183 10.971 329 05.05.17 River 8.84 15.9 -15.23 Reno Vergato 44.281 11.114 191 05.05.17 River 9.13 20.2 1.28 Reno Vergato 44.283 11.116 182 05.05.17 River 9.12 18.8 61.55 Savena Savena 44.322 11.300 258 04.07.17 Terrace Savena Savena-1 44.360 11.329 186 03.05.17 River 8.6 16.7 4.76 Savena Savena-2 44.360 11.329 186 02.07.17 River Scrivia Scrivia 44.719 8.860 208 18.03.18 River 8.35 7.8 -7.13 0.86 -8.64 Secchia Secchia 44.543 10.767 105 15.07.18 River 8.24 25.7 14.34 Senio Senio 44.227 11.632 155 15.07.18 River 8.57 23.2 7.62 0.87 Serchio Filicaia 44.137 10.374 318 15.07.18 River 8.74 21.3 5.52 0.84 Serchio Piaggone 43.935 10.506 51 15.07.18 River 8.136 21.2 6.77 0.55 Staffora Staffora 44.893 9.057 191 15.07.18 River 8.26 27.4 10.21 0.90 Taro 1 44.698 10.093 127 21.03.18 River 8.46 5.5 0.49 -9.39 Taro 2 44.697 10.094 130 15.07.18 River 8.43 26 10.15 0.87 Trebbia 1 44.908 9.590 131 22.03.18 River 8.37 4.5 5.59 0.86 Trebbia 2 44.909 9.589 131 15.07.18 River 7.77 23.7 7.53

Vara 1 44.190 9.858 44 20.03.18 River 7.7 9.6 0.29 0.86 -8.80 Vara 2 44.192 9.858 37 15.07.18 River 8.51 24.8 5.75 0.85

10

8

6

4 Number of Rivers

2

0 52 56 60 64 68 72 76 80 84 88 92 96 100 Percent Modern Carbon (pMC) Figure 11. Distribution of 14C values in Northern Apennines rivers, in units of percent modern carbon (pMC).

113 more carbon derived from more carbon atmosphere derived from 0.4 and soil rock

Baganza Magra (Eq%) Nure Recco 2- Taro 4 Recco 0.2 Bisagno Vara Entella Baganza Scrivia XSO Nure Bisagno

0 -10.5 -10 -9.5 -9 -8.5 -8 -7.5 δ13C (‰) 13 Figure 12. Carbon isotope ratio of dissolved inorganic carbon (δ CDIC) plotted against the proportion of sulfate to 2- bicarbonate (XSO4 ).

4.4.2.2 Bulk Composition Riverine samples from the Northern Apennines are slightly alkaline, with pH values ranging from 7.7–9.13. The Reno terrace samples and one proximal river have the lowest pH values (7.1–7.84) relative to the other rivers. When pH data were available, we calculated the calcite saturation index (SI) (Figure 13, Table 10). For replicate samples collected in spring and summer, we use the averaged TDS concentration. Most samples (86%) have SI values greater than 0, and three samples have values of approximately zero, all three of which were collected during March of 2018. For replicate samples, the saturation index is always lower for the spring sample. Although we generally see no correlation between the saturation index and percent carbonate sand, the undersaturated samples have some of the lowest percent carbonate values (Figure 14).

Supplementary Table 9: Corrected major dissolved ions for evaporite-draining basins

River Sample Collection Na K Ca (µmol/L) Mg (µmol/L) Cl SO4 (µmol/L) HCO3 Magra 2.00 15.07.18 953.39 51.55 1725.05 531.67 0.00 0.00 1290.00 Secchia 1.00 15.07.18 4123.78 101.11 814.01 881.55 0.00 184.08 1100.00 Serchio at 1.00 15.07.18 508.48 48.59 487.98 1323.65 0.00 111.39 1175.00

Total dissolved solids for river samples increase from spring to summer (Figure 15). Throughout the sampled 2+ - seasons, the water chemistry is dominated by Ca and HCO3 concentrations (Figure 16), which show - 2+ - + 2- similar mean values, although HCO3 concentrations are more variable. The mean Mg , Cl , Na , and SO4 concentrations are similar for March samples (Figure 16A), although Mg2+ and Cl- occupy a greater range of + 2- 2+ values than Na and SO4 concentrations. The May samples also illustrate similar mean concentrations of Mg , - + 2- + 2- Cl , Na , and SO4 , although Na and SO4 concentrations show higher maximum values by about a factor of 1.5 (Figure 16B). The mean concentrations for these ions in the July samples (Figure 16C) illustrate greater variability relative to March and May samples, with mean Na+ concentrations that are a factor of 2 higher relative to May samples, and a factor of 4 higher than March samples. 114 8

6

4 Number of Rivers

2

0 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Saturation Index (SI) Figure 13. Histogram showing distribution of Saturation Index (SI) values for water samples. 1.6

1.2 Oversaturated

0.8 Saturation Index (SI)

0.4

0 Undersaturated

0 20 40 60 80 Carbonate Sand (%) Figure 14. Percent carbonate sand plotted against saturation index for all river samples. 115 30000

25000

20000

15000

10000 Total Disolved Solids ( μ mol/L)

5000

0 March May July Sample Collection Month Figure 15. Total dissolved solids (TDS) for river samples in the Northern Apennines, excluding Reno terrace seeps and tributary streams. Whiskers illustrate minimum and maximum values, edges of the box mark the 25th and 75th percentile, and horizontal line inside box indicates the median value.

We illustrate water composition in a series of ternary diagrams, which show the relative abundance of cations or anions as a percentage of the cation or anion sum total. For all rivers, Ca+Mg/Na ratios range from 0.4– 12.4. Excluding the Reno terraces, tributary streams, and labeled outliers, cation concentrations (Figure 17A) illustrate high variability in total Ca2+ (30–85%), Mg2+ (5–38%), and Na++K+ (10–55%) concentrations. Anion - - - concentrations (Figure 17B) reveal a composition dominated by high HCO3 (55–95%), and lower NO3 +Cl (2– 2- 45%), and SO4 concentrations (2–35%). For the longitudinal samples, we find a weakly positive correlation between distance from the outlet and dissolved concentrations of HCO3 for all rivers, illustrating that HCO3 generally increases downstream (Figure 18t).

Outliers in the both the cation and anion ternary plots are the Magra, Secchia, and Bisenzio, Rivers (Num. 7, 10 and 17 on Figure 1), as well as some of the Reno stream and terrace samples (Num. 19 on Figure 1). These 2+ - - 2+ - outliers are characterized by a higher proportion of Mg and NO3 +Cl , lower proportion of Ca and HCO3 , and variable Mg2+ and SO42-. Relative to concentrations of Ca2++Mg2+, these rivers are enriched in SO42-, NaCl, - and Sr and are depleted in HCO3 , indicative of waters that drain evaporite deposits (Gaillardet et al., 1999).

116 A) 3000

2000 mol/L) μ

1000 Concentration (

0 2+ - + 2- 2+ - - + Ca HCO3 Na SO4 Mg Cl K NO3 Major Dissolved Ions (March Samples) B) 3000

2000 mol/L) μ

1000 Concentration (

0 2+ - + 2- 2+ - - + Ca HCO3 Na SO4 Mg Cl K NO3 Major Dissolved Ion Concentrations (May Samples) C) 3000

2000 mol/L) μ

1000 Concentration (

0 2+ - + 2- 2+ - - + Ca HCO3 Na SO4 Mg Cl K NO3 Major Dissolved Ion Concentrations (July Samples) Figure 16. Concentrations of riverine major dissolved ions for A) March sample, B) May samples, and C) July samples. Whiskers illustrate minimum and maximum values, edges of the box mark the 25th and 75th percentiles, and the horizontal line inside the box indicate the median value. Reno terrace seeps and tributary stream samples are excluded. 117 4.4.2.3 Reno Valley The Reno River Valley samples were divided into three types: main river channel, small tributary streams to the main channel, and terrace seeps. Most rivers in the study area have a relatively similar composition of cations and anions marked by a cluster in the ternary space (Figure 17). The Reno terraces and stream samples deviate from that cluster to some degree, so we treat these samples separately to elucidate possible patterns in the distribution of cations and anions. The terrace seep and stream samples illustrate unusually high concentrations - 2- + 2+ + - of NO3 , SO4 , Na , Mg , K , Li, Si, and U. In terraces Qt5 and Qt6 (Table 6), NO3 concentrations above 1000 mmol/L were found in samples from four seeps, and from stream water samples taken proximal to the terrace seeps.

4.4.2.4 Seasonal Variations in River Chemistry A comparison of river samples collected in spring and summer of 2017 and 2018 illustrates seasonal variability in cation and anion concentrations (Figures 19, 20, and 21), which we discuss in terms of relative proportions. The lowest variability in cation and anion proportions is from samples collected during March of 2018 (Figure - - - 19). For samples collected in May and July, anion proportions show high variability in NO3 + Cl and HCO3 (~10–90%), and relative cation proportions illustrate high variability in Ca2+ (30–90%) and Na+ + K+ (10–70%). - - From March to July of 2018, relative anion proportions show an increase in NO3 + Cl and decrease in HCO3, - + associated with a spike in absolute NO3 concentrations. Cation proportions illustrate a relative increase in Na + K+ and decrease in Ca2+ proportions, also consistent with changes in the absolute concentrations of these ions between spring and summer (Figure 14).

Ion concentrations from individual basins (Figure 20 and 21) highlight the magnitude of variability between - spring and summer. With the exception of the Magra River (Figure 19), the relative proportions of NO3 + - - 2- Cl and HCO3 change by ~20% from spring to summer, whereas the SO4 proportions remain fairly constant. Cation proportions of Na+ + K+ and Ca2+ similarly change by ~15–20% percent from spring to summer, and Mg2+ proportions increase by up to 10%. In contrast, the replicate samples from the Reno terrace seeps show fairly stable Ca2+ and Na+ + K+ proportions between May and July of 2017, with relative changes of less than 20% (Figure 19A). However, these terraces record a much greater relative increase in Mg2+ proportions and decrease in Ca2+, up to 40%, between May and July of 2017. The anion proportions show variability between - - terraces seeps, up to 30% for SO4 and up to 50% for NO3 + Cl and HCO3 (Figure 21B). However, seasonal variability in anions proportions is small, less than 10% for all anions.

118 0 100 Reno Tributary Streams Reno Terrace Seeps Rivers 20 80

Secchia

40 60 Bisenzio Na

2+ + Magra +K

Ca +

60 40

80 20

100 0

0 20 40 60 80 100 Mg2+

0 100 Rivers Reno Tributary Streams Reno Terrace Seeps 20 80

60 - 40 HCO

+Cl 3 3 - NO 60 40 Bisenzio

80 Magra 20 Serchio Secchia

100 0

0 20 40 60 80 100 2- SO 4 Figure 17. A) Ternary Diagram illustrating relative proportions of major dissolved cations from all water samples. B) Ternary Diagram illustrating relative proportions of major dissolved anions from all water samples. Yellow diamonds indicate samples from the Reno River, tributaries, or terrace seeps; all other samples are shown as black circles. Outlier samples are labeled accordingly. 119 00

.

000

. ) μ

( 100

.34 . 100

800 0 0 0 10 0 ()

Figure 18. Distance from outlet at Ligurian Sea (Bisagno and Recco Rivers) or mountain front (Nure and Baganza Rivers) plotted against HCO3- concentration for longitudinal samples.

120 A) 0 100 05.17 07.17 03.18 20 80 07.18 05.18

40 60 Na

2+ + +K

Ca +

60 40

80 20

100 0

0 20 40 60 80 100 Mg2+

B) 0 100 05.17 07.17 03.18 20 80 07.18 05.18

- 40 60 HCO

- + Cl 3 3 - NO 60 40

80 20

100 0

0 20 40 60 80 100 2- SO 4 Figure 19. Ternary diagram of seasonal variability in A) cation and B) anion concentrations for all rivers except the Reno stream and terrace samples. 121 A) River Color Name Code 0 100 Sample Collection Magra Date Entella 05.17 03.18 Nure Vara 07.17 07.18 0 80 Trebbia Taro Baganza Panaro 0 60 Reno

0 40

80 20

100 0

0 20 40 60 80 100

B) River Color Name Code 0 100 Sample Collection Magra Date Entella Nure 05.17 03.18 Vara 20 80 07.17 07.18 Trebbia Taro Baganza Panaro Reno 40 60

60 40

80 20

100 0

0 20 40 60 80 100 Figure 20. Ternary diagram illustrating A) cation concentrations and B) anion concentrations among replicate samples collected in May and July of 2017, July of 2018, and March of 2018. 122 A) 0 100 Sample Collection Date

05.17 20 80 07.17

40 60 Na + 2+ + K

Ca +

60 40

80 20

100 0

0 20 40 60 80 100 Mg2+ B) 0 100 Sample Collection Date

05.17 20 80 07.17

- 40 60 HCO

+ Cl - 3 - 3

NO 60 40

80 20

100 0

0 20 40 60 80 100 2- SO4 Figure 21. Ternary diagram illustrating A) cation concentrations and B) anion concentrations among replicate samples collected from Reno terrace seeps in May and July of 2017. 123 4.5 Discussion Catchment-wide percent carbonate content scales weakly with 10Be denudation rates. Ion concentrations are 2+ - dominated by Ca and HCO3 (Figure 16), and samples are generally oversaturated with respect to calcite. 2+ + + + - - Seasonal variability illustrates shifts in the relative proportions of Ca with Na + K , and NO3 with HCO3 . - From spring to summer, NO3 concentrations increase, particularly in the Reno river and terrace samples. In the following discussion, we interpret the main trends in the water chemistry data and present the denudation, erosion, and weathering fluxes.

4.5.1 14C and δ13C Theoretical stoichiometry predicts that dissolution of silicate minerals through only carbonic acid will produce a pMc value of 100, whereas dissolution of carbonate through sulfuric acid will produce a pMc value of 0 (Blattmann et al., 2019). Values in between these endmembers therefore indicate a mixed contribution of modern carbon and radiocarbon dead sources. We find that most samples have values close to modern (pMc = 75–90), indicating that chemical weathering of carbonate and silicate is primarily dominated by carbonic acid (Blattmann et al., 2019). One exception is the Serchio sample (pMc = 55), where depleted δ34S isotopic signatures (-1.1 and -8.9) measured in some tributaries (Cortecci et al., 2008a) reflect the oxidation of pyrite, and consequently sulfuric acid weathering of carbonate. These values were associated with the local, cold springs found in the Serchio Valley (Schwarcz and Cortecci, 1974), although other catchments with springs (e.g. Secchia River) have a modern pMc signature (0.86), perhaps reflecting differences in the compositions of the springs. Unfortunately, no replicate measurements of 14C were collected for spring and summer, so it is unclear whether dilution might also affect the pMc signature for each river.

4.5.2 Seasonal Variability Increased relative carbonate concentrations in summer samples, with respect to spring samples, were observed in the majority of replicate samples and can generally be attributed to natural variability. Soil CO2 is often higher in summer, due to increased auto- and heterotrophic respiration driven by both higher productivity and higher temperatures. This, in turn, creates more soil CO2 for dissolution, and particularly carbonate, which can lead to higher concentrations. Nevertheless, we also observed seasonal trends in ion concentrations that are likely related to river discharge and dilution. Previous studies have found the greatest seasonal variations in river water chemistry to coincide with rainfall or flood events (Calmels et al., 2007). One of the evaporite- bearing rivers (Magra catchments) experienced a factor of three increase in Ca2+ and an order of magnitude 2- + - increase in SO4 , Na , and Cl concentrations in the July 2018 samples, relative to the March 2018 samples. On the day of sampling in March 2018, daily discharge of the Magra was measured at 102 m3/s. In contrast, on the July sampling date, the recorded average river discharge was two orders of magnitude lower (7 m3/s). For this catchment, the March sample was at saturation, indicating that dilution could be a reason for the difference in ion concentrations measured between spring and summer. Depending on what is driving the oversaturation, dilution may not be a controlling factor on ion concentrations in oversaturated samples, which have reached the thermodynamic limit. As such, we can only attribute differences in ion concentrations between spring and summer as natural variability or anthropogenic inputs, with the exception of the few rivers than are slightly below or at saturation.

Along the Bisenzio River, no sources of evaporite deposits are recorded in the literature, although this water + 2- sample has high Na (4538 μmol/L) and SO4 (803.86 μmol/L) concentrations relative to other Northern 124 Apennines Rivers. Cortecci et al. (2002) reported δ34S isotopes from sulfate and ion concentrations along the Bisenzio River, 4 km upstream of our sample location, and observed values of -9‰. These isotopic values are consistent with natural sources of sulfate from oxidation of pyrite (Calmels et al., 2007; Cortecci et al., 2002; Spence and Telmer, 2005; Cortecci et al. 2008), rather than values associated with Triassic evaporites (+16 ± 0.5‰) (Boschetti et al., 2005). Relative to the ion concentrations measured at this site by 2- Cortecci et al. (2002), our Bisenzio water sample from July of 2018 shows a factor of 4 times higher SO4 concentrations and an order of magnitude higher Na+, Cl-, and Ca2+ concentrations. These values are consistent with higher ion concentrations from Bisenzio river samples farther downstream, which were associated with anthropogenic Na2SO4 from textile industries (Cortecci et al., 2002). These high ion concentrations can result in an overestimation of weathering fluxes. Given the available data, we have no method for isolating natural versus anthropogenic weathering fluxes, so we ignore the Bisenzio River in our denudation and weathering flux calculations.

Rivers with replicate samples (Trebbia, Taro, and Vara catchments; Num. 6, 11, and 13 on Figure 1) record a - spike in NO3 concentrations from 0 μmol/L (March of 2018) to ~10–40 μmol/L (July of 2018). This increase - in NO3 is likely due to the application of fertilizer during the spring season (Marchina et al., 2015). One study noted that nitrogen-based fertilizers used in agriculturally intensive areas can act to decrease riverine alkalinity and enhance carbonate dissolution (Perrin et al., 2008). This observation is consistent with the lowest pH values (7.1–7.84) measured from some of the Reno River terraces, coupled with the highest dissolved Ca2+ - concentrations (~2,500–5,400 μmol/L) and NO3 concentrations (~0–4240 μmol/L) for rivers that do not drain evaporites. Interestingly, the highest recorded pH values of all riverine samples were also from the Reno River, and resulted in the highest calcite saturation index values (1.57) (Table 10). The few samples at saturation or slightly undersaturated samples in Northern Apennines rivers were collected in March, when average water temperatures were 7˚C, compared to the average summer water temperature (23˚C). This undersaturation with respect to calcite is consistent with lower temperatures of 5—10˚C favored for calcite dissolution (Gaillardet et al., 2018) and the especially high discharges that were recorded in rivers during March of 2018. The finding that almost all rivers are oversaturated with respect to calcite suggests that secondary precipitation of calcite may be a locally important process for removing excess Ca2+ ions from water. We noted travertine along the Reno River, and previous studies have observed limestone precipitating springs (LPS) along rivers on the Adriatic side of the Northern Apennines (Cantonati et al., 2016).

Overall, we find that seasonal variability in riverine ion concentrations is likely a mix of natural and anthropogenic influences, from soil respiration, a dilution effect due to spring and summer discharge variability, and the application of nitrogen fertilizers during the spring season.

125 Table 10. Calcite saturation index calculations.

Seasonal Results Annual Results Measured Measured River Sample Date SI pH SI pH

Baganza 0 15.07.18 0.67 8.39 Baganza 1 20.03.18 0.28 8.34 Baganza 2 20.03.18 0.21 8.34 Baganza 4 20.03.18 0.87 8.65 0.59 8.42 Baganza 5 21.03.18 0.69 8.46 Baganza 6 21.03.18 0.63 8.40 Baganza 7 21.03.18 0.69 8.42 Baganza 3A 20.03.18 0.45 8.39 Baganza 3B 20.03.18 0.69 8.41 Bisagno 1 18.03.18 0.52 8.33 Bisagno 2 19.03.18 0.47 8.37 Bisagno 4 19.03.18 0.42 8.44 Bisagno 5 19.03.18 0.54 8.54 0.56 8.44 Bisagno 6 19.03.18 0.45 8.40 Bisagno 3A 19.03.18 0.51 8.42 Bisagno 3B 19.03.18 0.93 8.60 Bisenzio 1 15.07.18 1.38 8.84 1.38 8.84 Entella 1 20.03.18 -0.45 8.03 0.00 8.07 Entella 2 15.07.18 0.35 8.11 Enza 1 15.07.18 0.61 8.39 0.61 8.39 Lamone 1 15.07.18 0.81 8.45 0.85 8.51 Lamone 2 15.07.18 0.90 8.57 Lima 1 04.05.17 0.71 7.80 0.02 8.22 Lima 2 15.07.18 -0.73 8.64 Magra 1 20.03.18 0.03 8.19 0.52 8.31 Magra 2 15.07.18 0.92 8.42 Montone 1 15.07.18 0.74 8.43 0.65 8.51 Montone 2 15.07.18 0.54 8.59 Nievole 18 04.05.17 1.08 8.86 1.08 8.86 Nure 0 15.07.18 0.70 8.35 Nure 1 21.03.18 0.49 8.23 Nure 2 21.03.18 0.72 8.52 Nure 3 21.03.18 0.65 8.50 Nure 4 21.03.18 0.62 8.45 Nure 5 21.03.18 0.54 8.43 0.62 8.47 Nure 7 21.03.18 0.86 8.63 Nure 6A 21.03.18 0.50 8.45 Nure 6B 21.03.18 0.75 8.54 Nure 8A 21.03.18 0.59 8.58 Nure 8B 21.03.18 0.30 8.48 Panaro 2 15.07.18 0.92 8.68 0.92 8.68 Parma 1 15.07.18 0.73 8.47 0.73 8.47 Pescia 16 04.05.17 0.13 8.49 0.37 8.64 Pescia 17 04.05.17 0.61 8.79 Recco 1 19.03.18 0.42 8.52

126 Recco 3 19.03.18 0.63 8.46 Recco 4 20.03.18 0.43 8.24 0.58 8.42 Recco 5 20.03.18 0.64 8.40 Recco 2E 19.03.18 0.74 8.43 Recco 2W 19.03.18 0.57 8.48 Reno 5 01.05.17 0.88 8.79 Reno 12 03.05.17 0.78 8.46 Reno 14 04.05.17 0.26 8.37 Reno 20 05.05.17 0.53 8.46 Reno 21 05.05.17 0.60 8.23 Reno 22 05.05.17 0.12 8.31 0.84 8.63 Reno 23 05.05.17 0.24 8.34 Reno 24 05.05.17 0.04 8.25 Reno 25 05.05.17 0.94 8.84 Reno 26 05.05.17 1.50 9.13 Reno 27 05.05.17 1.57 9.12 Reno 29 06.05.17 1.11 8.53 Savena 11 03.05.17 0.98 8.60 0.98 8.60 Scrivia 1 18.03.18 0.57 8.35 0.57 8.35 Secchia 1 15.07.18 0.77 8.24 0.77 8.24 Senio 1 15.07.18 0.98 8.57 0.98 8.57 Serchio F 1 15.07.18 1.03 8.74 1.03 8.14 Serchio P 2 15.07.18 0.64 8.14 0.64 8.74 Staffora 1 15.07.18 0.62 8.26 0.62 8.26 Taro 1 21.03.18 0.60 8.46 0.69 8.45 Taro 2 15.07.18 0.76 8.43 Trebbia 2 15.07.18 0.02 7.77 0.19 8.07 Trebbia 2 22.03.18 0.35 8.37 Vara 1 20.03.18 -0.76 7.70 0.00 8.11 Vara 2 15.07.18 0.70 8.51

4.5.3 Partitioning the Denudation Flux

4.5.3.1 Weathering Fluxes For comparing general trends in denudations fluxes, we calculated weathering fluxes from annual discharge measurements, and averaged the weathering fluxes from replicate samples. We do not report fluxes from the Serchio at Filicaia and Vergato catchments, as no discharge measurements are available.

Total denudation fluxes derived from 10Be concentrations vary over an order of magnitude, from 278–1597 t/km2/yr (Figure 22, Table 11). Total weathering fluxes are generally 1–2 orders of magnitude lower than denudation fluxes, with values ranging from 37.6 to 216.5 t/km2/yr. Partitioned into silicate and carbonate weathering, we find that silicate weathering fluxes (10.1–169.7 t/km2/yr) are lower than carbonate weathering fluxes (-28.1–90.6 t/km2/yr) for 93% of our sampled sites (Figure 23). We note that the negative carbonate weathering flux (-28.1 t/km2/yr) and highest silicate weathering flux (169.7 t/km2/yr) are from the Secchia River, for reasons we will describe in the limitations section. Finally, silicate weathering and carbonate weathering fluxes (Figure 23) illustrate a weakly positive correlation (R2 = 0.27).

127 1800 200 150 100 1600 50 0 1400 -50

1200

1000 /yr) 2

800

600 Fluxes (t/km

400

200

0

-200

Denudation Total Carbonate Silicate Total Carbonate Silicate Physical Erosion Erosion Chemical Weathering Weathering Erosion Weathering

Figure 22. Box and whisker plot showing denudation, chemical weathering, and physical erosion fluxes. Inset image magnifies chemical weathering fluxes. Whiskers illustrate minimum and maximum values, edges of the box mark the 25th and 75th percentile, and horizontal line inside box indicates the median value.

We also calculated weathering fluxes using seasonal discharge measurements, to compare differences between replicate samples. Among these samples, March weathering fluxes are up to 50 times larger than July weathering fluxes, illustrating the important role of runoff in determining weathering fluxes in the Northern Apennines. This observation is consistent with previous studies that have found a strong influence of seasonal climate (i.e. temperature and runoff) on the relative magnitude of short-term silicate and carbonate fluxes in Himalayan catchments (Tipper et al., 2006). However, because denudation fluxes are 1–2 orders of magnitude higher than weathering fluxes, this does not have a large impact on our calculated partitioning between erosion and weathering.

128 100

Higher carbonate weathering

80

2 /yr)

2 R = 0.27

60

40 Carbonate Weathering Flux (t/km 20

Higher silicate weathering 1:1 0 0 10 20 30 40 50 Silicate Weathering Flux (t/km 2/yr)

Figure 23. Silicate weathering fluxes plotted against carbonate weathering fluxes. Linear regression is given by the solid, black line and R2 value. Dashed, gray line illustrates 1:1 trendline separating the zone of higher carbonate weathering (gray background) from the zone of higher silicate weathering (white background).

4.5.3.2 Erosional Fluxes Erosional fluxes (159.3–1542.1 t/km2/yr) are higher than weathering fluxes in the Northern Apennines (Table 11), and the ratio of erosional fluxes to weathering fluxes varies over one order of magnitude (1.3–31.5). We find the silicate and carbonate erosional fluxes are not correlated 2(R = 0.05) (Figure 24), and 58% of sampled catchments have higher silicate erosional fluxes (35.3–817.2 t/km2/yr) relative to carbonate erosional fluxes (27.2–966.3 t/km2/yr) (Table 11, Figure 24).

129 Error Error 862.03 (t/km2/yr) Flux Flux 827.4 3 Silicate Erosion (t/km2/yr) 84.99 359.96 85.00 Error Error 609.55 104.75 523.98 104.73 (t/km2/yr) Flux Flux 120.9 1 734. 78 167.5 7 Erosion Carbonate (t/km2/yr) 84.95 Error Error 609.54 . (t/km2/yr) 488. 47 692.28 104.72 1562. 22 Physical (t/km2/yr) Erosion Flux 4.91 2.49 2.39 1028.38 265.18 304.31 265.03 768.19 265.19 3.0 4 Error Error (t/km2/yr) Flux Flux 32.35 53.0 4 40. 23 -28.0 2 Carbonate (t/km2/yr) Weathering 1.49 6.12 0.80 Error Error (t/km2/yr) Flux Flux 30.8 9 23.0 8 169.7 5 Silicate (t/km2/yr) Weathering 5.13 6.61 Error Error (t/km2/yr) Flux Flux 83. 93 141. 73 Chemical (t/km2/yr) Weathering Error Error (t/km2/yr) Flux Flux (t/km2/yr) Denudation 0.710.68 726.102.17 360.400.97 288.85 286.200.67 1317.05 54.331.23 66.15 34.45 821.500.94 328.60 145.05 503.50 5.14 126.84 94.08 572.40 97.90 5.36 58.30 7.78 84.80 70.31 18.40 5.12 85.08 138.81 3.84 47.59 2.13 41.88 5.67 5.04 2.23 25.12 47.75 1.58 24.67 6.24 1.33 79.24 4.24 56.02 1.42 1.82 659.95 7.45 45.20 4.87 215.35 60.41 288.90 159.36 3.60 1213.45 54.59 505.39 5.49 35.32 328.64 665.42 60.72 289.00 447.13 730.11 27.24 94.46 154.56 54.66 328.69 58.42 35.51 158.11 408.63 154.62 486.75 83.90 214.99 94.18 77.25 464.81 58.61 49.97 525.36 357.48 133.17 82.86 0.320.78 1544.951.01 789.700.67 295.48 1139.500.58 120.58 948.70 37.640.91 201.40 710.20 56.29 153.70 1703.95 86.95 1.65 125.88 609.50 51.99 3.39 4.81 47.90 18.53 4.33 18.66 3.57 35.09 0.70 20.40 0.75 1.33 10.19 19.11 2.78 37.62 0.36 51.86 1.49 31.59 3.30 1186.55 4.62 37.71 295.89 733.41 2.86 1054.40 3.56 639.01 120.62 201.45 896.71 569.65 295.51 279.12 751.44 153.76 126.31 683.52 120.68 201.58 321.08 374.61 295.93 432.39 301.65 153.77 126.05 170.63 201.60 534.75 247.44 217.44 126.35 0.53 1269.35 265.00 66.59 2.68 34.24 1.21 0.840.82 1597.951.47 1171.30 325.95 278.25 593.60 73.87 42.40 70.36 4.79 80.64 4.23 18.93 5.21 24.84 0.66 23.15 0.88 54.94 0.81 45.52 4.74 57.49 4.10 1524.08 5.14 1100.94 325.99 593.62 197.61 966.28 660.96 326.05 42.72 593.65 546.80 36.44 426.75 326.17 42.76 593.67 196.32 60.44 1.02 752.60 104.68 63.13 3.21 Runoff (m/yr) Annual ated only for rivers with available discharge data Denudation, weathering, and erosional fluxes. Fluxes were calcul ated only for rivers with available discharge . Enza Lima Taro Reno Vara Senio Entella Parma Scrivia Name Magra Panaro Secchia Trebbia Bisenzio Lamone Baganza Montone Piaggione Serchio at Table 11 Table 130 1200 R 2 = 0.05

1:1 Line

800 /yr) 2

400

Higher

Silicate Erosional Flux (t/km Silicate 0 Erosion

Higher Carbonate Erosion

-400 -400 0 400 800 1200 1600 Carbonate Erosional Flux (t/km2/yr)

Figure 24. Carbonate erosional fluxes plotted against silicate erosional fluxes. Linear regression is given by the solid, black line and R2 value. Dashed, gray line illustrates 1:1 trendline that separates the zone of higher silicate erosion (light brown background) from the zone of higher carbonate erosion (gray background).

Here, we address the guiding hypotheses for this study that were presented in the “Approach” section. Due to the methods used to calculate carbonate and silicate erosional fluxes (equations 11-13, Methods Section), denudation and erosional fluxes are dependent variables, and are therefore inherently correlated with one another. We therefore focus on comparing the independent variables in our dataset, namely: (1) percent carbonate sand, (2) denudation fluxes, (3) weathering fluxes, and (4) ion concentrations.

We hypothesized that carbonate dissolution is decoupled from carbonate physical erosion. To compare independent datasets, we illustrate carbonate physical erosion fluxes plotted against total Ca2+ concentrations (Figure 25A) and Ca2+ concentrations attributed to carbonates (Figure 25B). We find a moderate correlation between carbonate erosion and Ca2+ content in both cases, suggesting that increased carbonate erosion fluxes are associated with higher Ca2+ contents. This is an interesting result, considering that most rivers in the Northern Apennines are oversaturated with respect to Ca2+, meaning that the threshold for continued Ca2+ dissolution should have been exceeded. We might then expect that the Ca2+ concentrations would remain steady, while carbonate erosion would continue to increase. However, due to the uncertainties in carbonate erosion, it is unclear whether a positive correlation with carbonate erosion remains at higher Ca2+ concentrations (>1400 131 μmol/L), or whether there is a decoupling between Ca2+ concentrations and carbonate erosion.

A) 1800 Magra R 2 = 0.41

1600

1400

1200 ]

2+ 1000

total Secchia 800 [Ca

600 Serchio P.

400

200

0 0 400 800 1200 Carbonate Physical Erosion (t/km 2/yr) B) 1800

1600 R 2 = 0.30 Magra 1400

1200 ]

2+ 1000 carb 800 [Ca

600

400 Serchio P.

200 Secchia 0 0 400 800 1200 Carbonate Physical Erosion (t/km 2/yr)

Figure 25. Carbonate erosional flux plotted against A) total Ca2+ concentrations and B) Ca2+ concentrations attributed to carbonate weathering. Black, labeled data points are evaporite rivers that were excluded from the regression. Linear regressions are given by the solid, black line and R2 values. 132 We also hypothesized that chemical weathering should scale with denudation. Despite a number of studies that have shown a strong positive correlation between denudation and chemical weathering fluxes (Stallard and Edmond, 1983; Gaillardet et al., 1999; Millot et al., 2002; Lyons et al., 2005; von Blanckenburg, 2006), we find no correlation between denudation and chemical weathering fluxes (Figure 26), due to a large amount of scatter in the data and the relatively small range of denudation fluxes. If we remove the 4 outliers in weathering flux values, we find a weakly positive correlation between denudation and weathering (Figure 27). The overall lack of clear scaling between weathering and denudation in our samples may be explained by the negative trend of runoff with denudation (Figure 28), which may partly counteract any positive trend of denudation with weathering kinetics or supply.

1000 /yr) 2

100 Total Weathering Flux (t/km

10 100 1000 10000 Denudation Flux (t/km2/yr) Figure 26. Denudation flux plotted against total weathering flux.

When compared with global data, we find that our data occupy a relatively narrow range of denudation and weathering values (Figure 29). Relative to the global dataset, we find that our silicate erosion and weathering estimates are similar to other small mountainous catchments (surface area < 103 km2) around the world, despite differences in catchment lithologies (granite/diorite, and high-grade metapelites) (West et al., 2005). Total erosional fluxes from other temperate orogens, such as the eastern side of the New Zealand Southern Alps (140–1700 t/km2/yr) (Jacobson and Blum, 2003), are consistent with our total erosional fluxes (159.3–1542.1 t/km2/yr). However, as illustrated in Figure 29, our total weathering fluxes (37.6–216.5 t/km2/yr) are generally higher relative to the eastern side of the Southern Alps (9–36 t/km2/yr); rather, our weathering fluxes are consistent with values from the wetter, western side Southern Alps (81–150 t/km2/yr). On the eastern side of the Southern Alps, carbonate to silicate weathering flux ratios range from 0.92– 2.73, with an average of 1.68 (Jacobson and Blum, 2003). With the exception of one location, all ratios are >1, indicating the dominance of 133 160

R 2 = 0.31 /yr)

2 120

80 Total Weathering Flux (t/km

40 400 800 1200 1600 2000 2400 Denudation Flux (t/km 2/yr) Figure 27. Denudation fluxes plotted against total weathering fluxes. Regression line and R2 value show weak, positive correlation between axes when outliers are removed (Reno, Entella, Vara, and Montone Rivers). 2.4

2 R2 = 0.31

1.6

1.2

0.8 Average Annual Runo (m/yr)

0.4

0 0 400 800 1200 1600 2000 10Be Denudation Flux (t/km2/yr) Figure 28. 10Be denudation fluxes plotted against average annual runoff. Linear regression is given by the solid, black line and R2 value. 134 carbonate over silicate weathering (Table 12). This is an interesting observation, as the studied region in the Southern Alps is dominated by silicate-rich greywacke and schist lithologies, with minor hydrothermal calcite veins (Jacobson et al., 2003). Our weathering fluxes are generally higher, and the ratio of silicate to carbonate weathering fluxes varies from 0.94–3.70, with an average value of 2.04. While we already showedthat carbonate weathering fluxes are higher than silicate weathering fluxes (Figure 23), the average ratio illustrates that carbonate weathering has a greater contribution to the overall weathering flux relative to the Southern Alps example, consistent with the mixed carbonate-silicate lithologies of the Northern Apennines. 1000

100 /yr) 2

10 Total Weathering Flux (t/km 1

Northern Apennines (this study) Andes (Gaillardet et al., 1997) Global Data (West et al., 2005) New Zealand (Jacobson and Blum, 2003)

0.1 1 10 100 1000 10000 100000 Denudation Flux (t/km2/yr) Figure 29. Denudation fluxes plotted against total weathering fluxes for data from this study and previous studies in other orogenic settings.

One important issue to consider between denudation fluxes in the Southern Alps and Northern Apennines is the thermodynamic limit. Given that saturation index values are above zero for most Northern Apennines Rivers (Table 10) suggests that we are at the thermodynamic limit, so we would expect dilution to have a negligible effect on weathering. In contrast, rivers in New Zealand are generally undersaturated with respect to carbonate (Jacobson et al., 2003). As we have no constraints on the seasonal discharge in New Zealand, the dilution effects on weathering are unclear. However, the saturation state is an interesting and potentially important difference between these landscapes. 135 Table 12. Partitioned denudation fluxes for the Southern Alps of New Zealand (from Jacobson and Blum, 2003).

Physical Chemical Silicate Carbonate Erosion Weathering weathering Weathering Carb/Sil Area Discharge Runoff TDS Denudation Fluxes Fluxes fluxes Fluxes Weathering 2 3 2 2 2 2 2 Location (km ) (m /s) (m/yr) (g/L) (t/km /yr) (t/km /yr) (t/km /yr) (t/km /yr) (t/km /yr) Ratio Whataroa 445 9526 9.53 0.40 15020 14900 120 35 88 2.51 Waitaha 223 9152 9.15 0.45 15050 14900 150 45 100 2.22 Hokitika 352 8920 8.92 0.56 17210 17100 110 40 72 1.80 Waiho 185 5900§ 5.90 0.53 5400 5300 100 34 64 1.88 Haast 1020 5787 5.79 0.37 12781 12700 81 22 60 2.73 Paringa 230 4982§ 4.98 0.58 2959 2900 59 22 38 1.73 Rakai 2560 2723 2.72 0.57 1636 1600 36 13 23 1.77 Rakai 2560 2723 2.72 0.54 1636 1600 36 13 24 1.85 Rangitata 1461 2042 2.04 0.79 974 950 24 11 14 1.27 Rangitata 1461 2042 2.04 0.77 973 950 23 10 13 1.30 Jollie 9760 1245 1.85 0.94 215 200 15 7.5 8 1.07 Waimakariri 3210 1193 1.19 0.63 1717 1700 17 6.9 11 1.59 Waimakariri 3210 1193 1.19 0.64 1715 1700 15 5.8 9.1 1.57 Ashburton, N. 276 1145 1.15 0.75 374 360 14 6 8 1.33 Ashburton, S. 539 701 0.70 1.09 229 220 9.4 4.9 4.5 0.92

4.5.4 Limitations Here, we discuss some possible limitations to our methodology and results. For the Secchia River, we calculated negative carbonate weathering fluxes. Casil concentrations are calculated using the ratio of Ca/Na + multiplied by the Na concentration, so the expected Casil concentration from this calculation should have been 1443 μmol/L. However, the actual, remaining Ca2+ concentration was only 631 μmol/L, as we corrected for Ca2+ concentrations due to gypsum contributions. Despite also correcting Na+ for halite contributions, the remaining Na+ concentration (>4000 μmol/) was still an order of magnitude higher than 89% of all samples, so calculating Cacarb using equation (10) resulted in a negative contribution to carbonate weathering. In this case, we recognize that the evaporite correction could not sufficiently correct for all excess ions. However, we note that using the uncorrected Ca2+ and Na+ concentrations would also have produced this result, given the methods used in this study.

The overall lack of correlation between denudation and weathering fluxes could be due to different integration timescales for each method, as we quantify chemical weathering with river solutes and denudation fluxes with cosmogenic nuclides. Denudation rates derived from 10Be reflect the time over which it takes to erode through one penetration length, approximately 60 cm of rock. Given the denudation rates in the Northern Apennines (Cyr and Granger, 2008; Cyr et al., 2014; Wittmann et al., 2016;), this equates to a timescale of approximately 2000-3000 years. In contrast, solute fluxes represent extremely short timescales, and concentrations can vary from season to season or year to year, as shown in this study. The Vergato River is an example where the different integration time periods for solute and 10Be data have likely produced unrealistic weathering fluxes relative to denudation fluxes. The Vergato River recorded exceptionally high average discharge during the last five available years of data, resulting in a high total weathering flux (2387 t/km2/yr), relative to a denudation flux of only 784 t/km2/yr. However, this sample is an outlier, and all other samples produced denudation fluxes that are at least an order of magnitude higher than weathering fluxes, consistent with previous studies (Riebe et al., 2001, 2004).

Alternatively, the weak correlation between chemical weathering and denudation fluxes could be due to 136 the weathering state of the catchments. We found that nearly all samples (uncorrected concentrations) are oversaturated with respect to calcite. Furthermore, the presence of carbonate sand in all catchments suggests that the system is quickly buffered, so no more Ca2+ can dissolve in water, resulting in enhanced physical erosion of carbonate lithologies and the precipitation of secondary carbonate.

Our results reflect erosional processes for sand-sized grains in the catchment. A number of studies have argued that grain size bias, due to unevenly distributed sand production, increases with relief and catchment area in mountainous settings. This would negate the assumption that the sampled sediments are representative of denudation in the entire upstream catchment (Riebe et al., 2015; Lukens et al., 2016; Van Dongen et al., 2019). Therefore, cosmogenic nuclide denudation rates could underestimate or overestimate the actual denudation rates for catchments. In the Northern Apennines, average sand size is similar between the Ligurian and Adriatic rivers, despite steeper slopes on the Ligurian side relative to the gentler Adriatic slopes. Although we cannot address weathering and erosional processes for other grain sizes that represent important volumes of the riverbed, our results nevertheless contribute an important dataset for understanding large-scale weathering and erosion processes in an orogenic setting.

4.6 Conclusion For the first time, we have partitioned denudation fluxes into physical erosion and chemical weathering for both carbonates and silicates in an orogenic setting with mixed siliciclastic-carbonate lithologies. In the Northern Apennines, we found that chemical weathering is decoupled from carbonate physical erosion, and chemical weathering has a weakly positive correlation with total denudation, when ignoring outliers. We also find no obvious pattern in denudation and weathering fluxes for carbonate versus silicate-dominated catchments in the Northern Apennines.

Consistent with studies performed in other orogenic settings, physical erosion fluxes are higher than chemical weathering fluxes in the Northern Apennines. With the exception of a few outliers, weathering fluxes are on the higher end of weathering flux estimates relative to other orogens with similar denudation rates. Relative to the Southern Alps of New Zealand, where carbonate exists primarily as hydrothermal fluids, the exposure of carbonate lithologies in the Northern Apennines can account for the higher carbonate weathering fluxes and overall higher total weathering fluxes calculated in this study.

Young orogens, such as the Himalaya, Taiwan, and Southern Alps of New Zealand are comprised of both silicate and carbonate lithologies and have the potential to significantly impact weathering fluxes. Considering that previous studies have focused exclusively on silicate-rich regions within mountainous landscapes (e.g. Sierra Nevada Mountains of California, Andes Mountains, Himalaya Mountains, Southern Alps of New Zealand; Gaillardet et al., 1997; Riebe et al., 2001, 2004; Jacobson and Blum, 2003; West et al., 2005), studying other regions within these mountainous landscapes is important for understanding the overall variability in denudation, weathering, and erosional fluxes within orogenic systems.

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141 CHAPTER 5 Conclusions and future work

142 5.1 Conclusions The goal of this thesis was to understand the spatial variability in modern and long-term denudation rates, in weathering rates, and in physical erosion rates in the Northern Apennines Mountains. New modern denudation estimates from catchments on the Ligurian side of the range illustrate consistently slower denudation rates relative to their Adriatic counterparts, and this pattern of denudation contradicts the observed pattern of normalized channel steepness, predictions of divide migration from χ, and evidence for river captures. The paradox between denudation rates and the pattern of topographic asymmetry was addressed by proposing an alternative interpretation of a 10Be concentration as a velocity with horizontal and vertical components of motion. The interpreted velocities from each catchment, and geodetic rates of horizontal and vertical rock motion, were incorporated into a kinematic model of an orogenic wedge above a subducting, rollback slab. Given our assumption of a common horizontal velocity across the orogen, but independent vertical velocities, we defined a kinematic model consistent with the 10Be data, and calculated a horizontal velocity between 2–5 mm/yr towards the southwest, accounting for measured geodetic uplift rates of -0.16–0.12 mm/yr on the Ligurian side and 0.5–1.0 mm/yr on the Adriatic side. This model illustrated that the retrowedge is dominated by horizontal rock motion of the wedge relative to the surface, and the prowedge is characterized by both vertical and horizontal motion of rock. A new estimate for the rate of slab retreat (6–9 mm/yr) was also constrained by the kinematic model, which is consistent with independent estimates of slab retreat (6–10 mm/yr) from the position of the subduction trench through time (Faccenna et al., 2014) and the distribution of extensional basins and sedimentation ages on the Ligurian side (Boccaletti and Sani, 1998; Rosenbaum and Piana Agostinetti, 2015). The kinematic model is consistent with the observed morphology of river channel profiles on the Ligurian and Adriatic sides and the pattern of topographic steepness across the Northern Apennines, as well as results from other studies that have addressed horizontal advection in asymmetric orogens (Willett et al., 2001, 2018; Miller et al., 2007).

Long-term denudation rates inverted from AFT and AHe thermochron cooling ages illustrate spatial and temporal variability in denudation rates during the late Miocene to Plio-Pleistocene. New and existing detrital and bedrock AFT estimates show that denudation rates were similar across the orogen up to approximately 4–5 Ma. Using a simple inversion method, we interpreted Age Elevation Relationships (AERs) between AFT and AHe ages to reflect an increase in denudation rates on the Adriatic side, and a decrease in denudation rates on the Ligurian side. Others interpreted the AER on the Ligurian side to reflect local tilting of a normal fault footwall block (Thomson et al., 2010), although our interpretation is consistent with the overall decrease in denudation rates through time on the Ligurian side, demonstrated from the pattern of detrital long-term and modern denudation rates.

The AHe and AFT reset fronts vary with distance from the northern (Adriatic) mountain front, producing offset reset fronts between 10˚30’ and 11˚30’ longitude, flanked by regions where the resent fronts are coincident (Thomson et al., 2010). The relative position of the reset fronts illustrates the importance of different methods of crustal accretion that are either characterized by horizontal rock motion (crustal accretion) or vertical rock motion (underplating). Orogen kinematics, material cooling paths and cooling ages were modeled, using the geometry of the wedge, estimates of slab retreat, and surface erosion rates. This model reproduced the pattern of measured cooling ages and offset AFT and AHe reset fronts, and predicted uplift rates across the range that match measured geodetic rates. In addition, predicted cooling paths illustrated the importance of horizontal motion on the retrowedge side, in order to produce older cooling ages on this side of the range.

143 For the first time, denudation was partitioned into carbonate and silicate physical erosion and chemical weathering fluxes in an orogen comprised of mixed siliciclastic and carbonate lithologies. Physical erosion is 1–2 orders of magnitude higher than weathering fluxes in the Northern Apennines, and is variably dominated by either silicate or carbonate erosion in each catchment. Carbonate physical erosion illustrates a moderately positive correlation with Ca2+ concentrations, and total chemical weathering in the Northern Apennines is dominated by the dissolution of carbonate rocks. Previous studies from silicate-rich orogenic settings have shown strong correlations between chemical weathering and denudation fluxes (Gaillardet et al., 1997; Jacobson et al., 2003; West et al., 2005). In the Northern Apennines, denudation fluxes are weakly correlated with total weathering fluxes, and are within the range of global data from silicate-rich, mountainous settings. Relative to other temperature orogens, such as the eastern side of the Southern Alps (Jacobson and Blum, 2003), denudation fluxes are similar, but weathering fluxes from the Northern Apennines are higher. The higher ratio of carbonate to silicate weathering and exposure of carbonate lithologies in the Northern Apennines suggests that mixed-lithology orogens could be expected to have higher total weathering fluxes relative to silicate-rich orogens.

The results of this thesis have contributed new, regional estimates of denudation through space and time for the Northern Apennines, the first estimates of chemical weathering and physical erosion for an orogenof mixed carbonate and siliciclastic lithologies, and challenged existing interpretations regarding the relationship between denudation and topographic metrics of steepness. Here, future work related to this thesis, and further topics of interest within the Apennines Mountains, are discussed.

5.2 Future Work

5.2.1 Orogenic Wedge Modeling The kinematics of the orogenic wedge dictate how material is introduced into the wedge, the path of material through the orogen, and where will be eroded from the surface. Thermal constraints are also needed to properly calculate the time at which a rock passes through the closure temperature and the time at which the rock will reach the surface. Both the kinematics and thermal history of a rock are crucial for understanding the distribution of cooling ages among exposed rocks. To this end, work is currently underway to couple the kinematic model of Thomson et al. (2010) and thermal model of Reiners et al. (2015) for the Northern Apennines. Combined with constraints on slab retreat and denudation rates from Chapter 2, this new model will provide better constraints on the exhumation paths and cooling ages for orogens such as the Northern Apennines, where horizontal and vertical material accretion styles were shown to be variable through space and time (Thomson et al., 2010).

5.2.2 Rock Erodibility

The correlation between erodibility (K) and ksn is a hotly debated topic, as some have found differences in K of several orders of magnitude among rock types (Stock and Montgomery, 1999). However, in a similar region, a 10 more recent study (Hilley and Young, 2018) found a poor correlation between ksn and K, using Be denudation rates. These results raise the question of what factors may control erodibility in the Northern Apennines, particularly for similar lithologies that nevertheless erode at different rates? To address this question, two approaches will be explored. The first approach will converge upon erodibility (K) values for each lithology in the Northern Apennines, using a Bayesian inversion model. The second approach will quantify rock strength 144 through field measurements, and correlate this parameter with the maximum depth of burial that each Tertiary foredeep unit has experienced.

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