# Applying Pattern Oriented Sampling in Current Fieldwork Practice to Enable More Effective Model Evaluation in Fluvial Landscape Evolution Research: Pattern Oriented Sampling Approach to Field Data for LEM Evaluation

Rebecca M. Briant, Kim M. Cohen, Stéphane Cordier, Alain J.A.G. Demoulin, Mark G. Macklin, Anne E. Mather, Gilles Rixhon, Tom Veldkamp, John Wainwright, Alex Whittaker, et al.

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## To cite this version:

Rebecca M. Briant, Kim M. Cohen, Stéphane Cordier, Alain J.A.G. Demoulin, Mark G. Macklin, et al. Applying Pattern Oriented Sampling in Current Fieldwork Practice to Enable More Effective Model Evaluation in Fluvial Landscape Evolution Research: Pattern Oriented Sampling Approach to Field Data for LEM Evaluation. Earth Surface Processes and Landforms, 2018, 43 (14), pp.2964–2980. 10.1002/esp.4458. hal-02477267

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## Applying Pattern Oriented Sampling in current fieldwork practice to enable more effective model evaluation in fluvial landscape evolution research

<table><tr><td>Journal:</td><td>Earth Surface Processes and Landforms</td></tr><tr><td>Manuscript ID</td><td>ESP-15-0246.R3</td></tr><tr><td>Wiley - Manuscript type:</td><td>Special Issue Paper</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Briant, Rebecca; Birkbeck, University of London, Geography, Environment and Development Studies<br>Cohen, Kim; Utrecht University, Department of Physical Geography<br>Cordier, Stephane; Universite Paris Est, Geographie<br>Demoulin, Alain; University of Liege, Dept of Physical geography and Quaternary<br>Macklin, Mark; University of Lincoln College of Science, School of Geography; Massey University, Institute Agriculture and Environment<br>Mather, Anne; University of Plymouth, Geography, Earth and Environmental Sciences<br>Rixhon, Gilles; CNRS Strasbourg, Laboratoire Image, Ville, Environnement (LIVE)<br>Wainwright, John; University of Durham, Geography<br>Wittmann, Hella; GFZ German Research Centre for Geosciences, Helmholtz Centre<br>Veldkamp, Tom; University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)</td></tr><tr><td>Keywords:</td><td>landscape evolution modelling, catchments, Pattern Oriented Sampling, fluvial systems, geological field data</td></tr></table>
## Applying Pattern Oriented Sampling in current fieldwork practice to enable more effective model evaluation in fluvial landscape evolution research

*Briant, R.M., Cohen, K.M., Cordier, S. Demoulin, A., Macklin, M.G., Mather, A.E., Rixhon, G., Veldkamp, A., Wainwright, J., Whittaker, A., Wittmann, H.

Studies using Landscape Evolution Models (LEMs) on real-world catchments are becoming increasingly common. Evaluating their reliability requires us to bring together field and model data. We argue that these are best synchronised by complementing the Pattern Oriented Modelling (POM) approach of most fluvial LEMs with Pattern Oriented Sampling (POS) fieldwork approaches (Figure 1).

<figure><img src="image_3.png" /><figcaption>Figure 1 – Flow chart for applying Pattern Oriented Modelling (POM) and Pattern Oriented Sampling (POS) within a join field-model investigation of a specific catchment.</figcaption></figure>
Briant et al. Applying Pattern Oriented Sampling in current fieldwork practice to enable more
effective model evaluation in fluvial landscape evolution research

Response to Reviewers May 2018

I have implemented all the in-text minor changes and clarifications requested by reviewer 2, where
these were still relevant after the major changes had been made to the text.

In relation to major changes to the text, these have been undertaken as follows. The version of the
text uploaded for rerreview shows these as additions (yellow highlight) and reworking (green
highlight):

1. In line with the suggestion of the Associate Editor, we have not removed the philosophical section of the text as suggested by the second reviewer.

2. Reviewer 1's first point appears to be questioning our claim that LEMs are fundamentally different from other environmental models. We stand by our position here, but have made a number of changes to the text to explain further why we believe this to be so, specifically:

a. To clarify what types of models we are referencing we have changed the terminology throughout to earth surface process models. In this way, the comparison undertaken is clearer.

b. In the third and penultimate paragraphs (lines 79 to 99 and 169 to 179) of the Introduction we expand on the differences that we see, and make reference to geodynamic models, which are indeed similar to LEMs except that they do not deal with the earth surface.

c. In the philosophical considerations section of the paper, the difference between LEMs and other models is again expanded upon in lines 218 to 233, where we argue that classical calibration is problematic for LEMs operating over geological timescales for a number of reasons, but specifically because of the presence of non-analogue landscapes during the past, giving the example of periglacial processes operating at mid-latitudes.

d. We further argue in relation to initial conditions (lines 291 to 296) that the scale of the difference between geological data and modern data for initial conditions is so much greater that this does constitute a different scale of challenge.

3. Reviewer 1's second point is querying whether LEMs are really not aiming for prediction. This point comes mainly from a misunderstanding of the text in lines 173 and 183 of the October 2017 revision where we were actually contrasting the very limited occasions when LEMs might be used for numerical prediction with the many more frequent occasions where it is not appropriate to do this. We have made this clearer in the text. In addition, we agree that the role of prediction and validation deserve more elaboration. We have therefore:

a. referred to 'numerical prediction' throughout to make clearer that we are meaning prediction in the sense that (for example) future climate modelling makes projections of global temperatures in absolute numerical terms, mostly for future planning and policy.

b. The fact that LEMs do not aim for numerical prediction is outlined in the Introduction in lines 100 to 109 and 110 to 113.

c. The specific point made about spatial interpolation of data is addressed in lines 208 to 212 in relation to calibration
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d. A new subsection has been added with a paragraph briefly outlining some issues around validation versus evaluation in the context of LEMs in lines 241 to 255. Reviewer 1 correctly notes that some hindcasting of future landscapes is necessary for model evaluation, and we discuss further how this fits with concepts of validation, proposing instead how evaluation might be more effectively done in an LEM context. This is also discussed in lines 514 to 527.

4. Reviewer 1's third comment and reviewer 2's most substantive comment related to the text associated with the explanation of the POS approach. Reviewer 1 was concerned that we were just advocating ever more field research, whilst reviewer 2 was requesting greater clarity. The Associate Editor asked for 'a bit more discussion of it, as well as its positioning with respect to other methods and approaches (e.g. a basic sensitivity analysis).' In response we have made the following changes:

a. We have increased the clarity with which we explain the POS approach and how it would be implemented in a field strategy. This is outlined in the text in lines 155 to 160 and 401 to 417 and a new figure inserted before all the other figures (Figure 1) which comprises a flow chart of the approach.

b. We have changed all the general text to make it clear that the aim is to broaden the range of possible field data types collected rather than simply to increase the total amount of field data collected in all cases. This has necessitated significant restructuring of lines 395 to 455 and 473 to 513.

c. In relation to positioning with respect to other methods and approaches (e.g. a basic sensitivity analysis), we have added text in lines 458 to 467.

d. In relation to a limited expansion of the case studies as requested by reviewer 2, we have added comments about the ability to quantify the goodness of fit in lines 557 to 562, 581 to 583 and 595 to 596, which links back to the new section on validation and evaluation in lines 241 to 255.

5. Reviewer 1 also commented about the section on knickpoints. We agree that there was some tangential material in this section and have removed and reworked this in lines 596 to 608. We have also included knickpoint mapping in Table 2, to bring it more in line with the other case studies addressed in terms of presentation.
1 Applying Pattern Oriented Sampling in current fieldwork practice to enable more effective model evaluation in fluvial landscape evolution research

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4 ¹Briant, R.M., ²Cohen, K.M., ³Cordier, S. ⁴Demoulin, A., ⁵,⁶Macklin, M.G., ⁷Mather, A.E., ⁸Rixhon, G.,

5 ⁹Veldkamp, A., ¹⁰Wainwright, J., ¹¹Whittaker, A., ¹²Wittmann, H.

6 Lead author email address: b.briant@bbk.ac.uk

7 ¹Department of Geography, Environment and Development Studies, Birkbeck, University of London,

8 Malet Street, London, WC1E 7HX, U.K.

9 ²Department of Physical Geography, Utrecht University, PO box 80.115, 3508 TC Utrecht, The Netherlands

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11 ³Département de Géographie et UMR 8591 CNRS- Université Paris 1-Université Paris Est Créteil,

12 Créteil Cedex, France

13 ⁴Department of Physical Geography and Quaternary, University of Liège, Sart Tilman, B11 - 4000 Liège, Belgium

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15 ⁵School of Geography, University of Lincoln, Brayford Pool, Lincoln, Lincolnshire, LN6 7TS, U.K.

16 ⁶Institute Agriculture and Environment, College of Sciences, Massey University, Private Bag 11 222,

17 Palmerston North 4442, New Zealand

18 ⁷School of Geography, Earth and Environmental Sciences, University of Plymouth, Drake Circus,

19 Plymouth, Devon, PL4 8AA, UK

20 ⁸Laboratoire Image, Ville, Environnement (LIVE), UMR 7362 - CNRS, University of Strasbourg- ENGEES, 3 rue de l'Argonne, 67083 Strasbourg, France

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22 ⁹ITC, Faculty of Geo-Information Science and Earth Observation of the University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands

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24 ¹⁰Durham University, Department of Geography, Science Laboratories, South Road, Durham, DH1 3LE, UK

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26 ¹¹Department of Earth Science and Engineering, Imperial College London, South Kensington Campus,

27 London SW7 2AZ, UK

28 ¹²Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany

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31 Note: Yellow highlighted text is completely new (other new text has been added but is less substantial), green highlighted text is substantially reworked

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33 Abstract

34 Field geologists and geomorphologists are increasingly looking to numerical modelling to understand

35 landscape change over time, particularly in river catchments. The application of Landscape Evolution

36 Models (LEMs) started with abstract research questions in synthetic landscapes. Now, however,

37 studies using LEMs on real-world catchments are becoming increasingly common. This development

38 has philosophical implications for model specification and evaluation using geological and

39 geomorphological data, besides practical implications for fieldwork targets and strategy. The type of

40 data produced to drive and constrain LEM simulations has very little in common with that used to

41 calibrate and validate models operating over shorter timescales, making a new approach necessary.

42 Here we argue that catchment fieldwork and LEM studies are best synchronised by complementing

43 the Pattern Oriented Modelling (POM) approach of most fluvial LEMs with Pattern Oriented

44 Sampling (POS) fieldwork approaches. POS can embrace a wide range of field data types, without

45 overly increasing the burden of data collection. In our approach, both POM output and POS field

46 data for a specific catchment are used to quantify key characteristics of a catchment. These are then

47 compared to provide an evaluation of the performance of the model. Early identification of these

48 key characteristics should be undertaken to drive focused POS data collection and POM model

49 specification. Once models are evaluated using this POM / POS approach, conclusions drawn from

50 LEM studies can be used with greater confidence to improve understanding of landscape change.

51 Keywords

52 Landscape evolution modelling, Pattern Oriented Sampling, catchments, fluvial systems, geological

53 field data

54 Introduction

55 Traditionally landscape evolution models have been heuristic models based on elaborate fieldwork

56 campaigns encompassing mapping and description of relevant landforms and deposits (e.g. Davis,

57 1922). The interpretation of the collected data on topography, bedrock and sediments of hillslopes

58 and valleys yielded chronological narratives centred around the available evidence (e.g. Maddy,

59 1997; Gibbard and Lewin, 2002). These narratives often used simple linear cause and effect

60 reasoning tailored to specific locations and prone to disciplinary biases. A danger with such models is

61 that they may then be applied as universal conceptual models in other locations where key

62 processes differ. The growing awareness that Earth is a coupled system with many global dynamics

63 caused researchers to incorporate known global oscillations such as in tectonics (e.g. Milliman and

64 Syvitski, 1992), climate (Vandenberghe, 2008; Bridgland and Westaway, 2008), base-level (Talling,

65 1998) and glaciation (e.g. Cordier et al., 2017) into their heuristic models. However, since it has

66 become more widely known that earth surface processes have non-linear complex dynamics it has

67 also become clear that simple linear cause and effect stories do not accurately capture all real world

68 behaviour. This non-linearity means that not all known global changes have left an imprint in all local
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69 records (e.g. Schumm, 1973; Vandenberghe, 1993; Blum and Törnqvist, 2000; Jerolmack and Paola, 2010).

71 Alongside this, the use of numerical landscape evolution models has accelerated. Since the early 1990s (see review by Veldkamp et al., 2017) these have developed into tools used to undertake theoretical experiments about the complexity of earth surface processes, although under controlled and strongly simplified conditions. Because they were invented to explore theoretical questions about past forcings within landscapes, these Landscape Evolution Models (LEMs) are significantly different from other types of models that simulate and forecast processes operating at present. Not least, their relation to field data is only now being assessed in detail, since initial studies frequently used synthetic landscapes (e.g. Whipple and Tucker, 1999; Wainwright, 2006).

79 There are five main groups of numerical models that deal with the earth surface processes: climatological, hydrological, ecological, hydraulic-morphodynamic and LEMs. Landscape evolution models are distinctive because they combine elements of the other four, frequently enabling all domains to change during a model run rather than modelling one and specifying others as input parameters. In doing this, they focus on long-term geomorphology – both the form of the landscape and the processes operating within it (e.g. Temme et al., 2017). Whilst some geomorphological features form quickly and can be monitored and modelled in parallel to hydraulic measurement and modelling (e.g. Camporeale et al. 2007), evolution of a full geomorphological landscape takes several orders of magnitude longer than human monitoring. The record that remains is therefore scattered and incomplete. As such, the cases being modelled are inherently more intractable. This is not only because process observations, even ‘long-term’ ones, rarely scale to the geological timescales under study (parameters of the LEM can account partially for this, see Veldkamp et al., 2017), but even more so because the initial conditions required for the LEM cannot be specified simply from modern datasets, even though LEMs are notoriously sensitive to the specification of initial conditions. LEMs share these characteristics of underdetermination with geodynamic models (e.g. Garcia-Castellanos et al., 2003), where key processes and features being modelled occur beneath the land surface and therefore very few initial conditions or processes can be directly measured. In addition, because more features of the landscape are allowed to change in a LEM than in the other types of earth surface models (Mulligan and Wainwright, 2004), they require a different approach, analogous to the difference between modern climate and palaeoclimate modelling (Masson-Delmotte et al., 2013).

100 Many non-LEM models seek numerical prediction (e.g. Oreskes et al., 1994), or at least robust projection of potential scenarios into the future, based on detailed comparison to a short time period of ‘the past’. This is because many of these other types of model (climate, hydrology and ecology) are used as a basis for future policy planning. Thus such models seek to replicate ‘reality’ more and more closely, as can be seen in the explosion of complexity in General Circulation Models from the 1970s to the present day (e.g. Taylor et al., 2012). This replication of reality is seen in increased inclusion of processes, but also in calibration, where parameters are tuned to known field observations to produce outputs that are as close to measured reality as possible. Once these non LEM models are validated using a different subset of past data, numerical prediction commences (Oreskes et al., 1994).
110 In contrast, landscape evolution modelling does not aim for **exact replication of present day** landscapes, although a measure of this is required to evaluate the usefulness of the model. Rather, the focus in most location-specific LEM studies is on narrowing down the range of processes likely to have been operating in a particular catchment in the geological past. For this reason calibration as defined above is rarely undertaken because numerical predictions are not required. This is not least because the difference between what is being modelled and what can be measured is greater than in (for example) hydrological models. For example in relation to temporal scale, the length of time being modelled means that the time steps necessarily used have little physical meaning (e.g. Codilean et al., 2006). Furthermore, some sets of parameter values that seem to fit the data well lack physical plausibility, questioning the value of applying calibration to LEMs, e.g. van der Beek and Bishop (2003). In addition, because of these longer timescales many properties are required to change in landscape evolution modelling that are frequently kept constant in hydrological models. These changing elements propagate impacts and uncertainties in space and time and the introduction of parameterisation arguably increases these uncertainties by introducing an additional level of uncertainty (Mulligan and Wainwright, 2004). Therefore, with landscape evolution models, the aim is not for more and greater complexity over time, but to constrain uncertainties as much as possible. Because the research questions being addressed usually involve explanation, the goal is to generate a plausible narrative based on the (frequently sparse) data available – just as in a forensic investigation - and not to achieve a numerical outcome that is ‘correct’ although some measure of the accuracy of approximation of the landscape to the present day is of course required for evaluation. Key research questions are likely to be framed as (e.g. Larsen et al., 2014): which are the most likely modes of formation for the landscape observed? What types or scales of tectonic activity are most likely to produce the landforms observed? What characteristics of a catchment enable a climate signal to be successfully transferred into a sedimentary record? As noted by Temme et al. (2017), the more complete the data available, the more catchment-specific the questions that can be addressed. Often, however, complete landscape and process reconstruction is not possible. Providing evidence to choose between competing hypotheses is more common (e.g. Viveen et al., 2014).

138 In order to generate a plausible narrative of landscape change, complexity is often actively reduced (e.g. Wainwright and Mulligan, 2005). Processes and parameters are only included in an LEM if there is evidence that they are likely to be relevant for explanation. This approach of ‘insightful simplification’ or ‘reduced complexity modelling’, does seek to explain what has happened in a specific place, as in the traditional heuristic model, but also to more broadly understand the known global driving factors within fluvial landscapes (Veldkamp and Tebbens, 2001), and to create generalizable statements about the development of large-scale geomorphological features. A further advantage of seeking simplification with complex feedbacks is that it allows emergent behaviour. In this case, a relatively simple set of factors is modelled, but can lead to apparently complex behaviour (e.g. Schoorl et al, 2014).

148 The above listed differences in approach between LEMs and other groups of earth surface models, encompass both philosophical issues in modelling and the relationship between models and field observations. This paper, whilst exploring the philosophical issues, seeks mainly to address the issue of field-model data comparison to evaluate LEM output created using this insightful simplification approach. It is aimed predominantly at field scientists, enabling them to apply the multiplicity of papers discussing modelling approaches and philosophy to their specific setting of landscape
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154 evolution model output and geological field data. In this paper, we argue that field data collection strategies and LEM studies are best brought together by deploying Pattern Oriented Sampling (POS) approaches when collecting field data. In this way, key characteristics of a real-world catchment are identified (e.g. sediment distribution, thalweg gradient, floodplain width) in both past timeslices and in the end situation and used to compare with the same characteristics generated from LEM output.
155 The Pattern Oriented Sampling approach that we advocate serves to collect field data that is more useful for comparison with model output. Improving our ability to evaluate model output will then allow us to use LEMs to narrow the range of plausible narratives that explain the field data observed.
156 In this way, we will be able to generate more robust generalisations than either those based on location-specific heuristic / conceptual models (e.g. Bridgland and Westaway, 2008) or those using synthetic landscapes (e.g. Whipple and Tucker, 1999). Whilst there are philosophical difficulties with strict validation of models of inherently open natural systems (Oreskes et al., 1994), evaluation of such modelling work against relevant field datasets is still crucial to determine at least the empirical adequacy of each model (e.g. Coulthard et al., 2005; Van De Wiel et al., 2011; Veldkamp et al., 2016).
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169 It is our contention that the nature and scarcity of much geological field data, which are typically not randomly generated, preserved or sampled, makes this a different and more intractable process for LEMs than for example hydrological modelling. Whilst it is true that all earth surface process models face problems of comparison with a limited set of field observations, this has mostly to do with bias and gaps in data collection. Because of the time scales involved, field data for comparison with LEM outputs have the additional problem that the geological and geomorphological records (deposits and erosional surfaces alike) are in large part removed and reworked by processes operating since they were first generated. Furthermore, most data are proxies for actual land surface characteristics that may or may not have analogues in the present day. Nonetheless, we argue that our Pattern Oriented sampling can significantly improve the suitability of geological field data selected for model evaluation.
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180 We focus on fluvial landscape evolution in this paper, but some of the general points raised are also relevant for modelling landscape evolution in other process domains. We will first discuss key philosophical considerations in applying field data to LEM evaluation. This is followed by advocating the use of a catchment wide Pattern Oriented Sampling (POS) approach to support fieldwork inventories, showing how such an approach might apply in different settings. This is a companion paper to Temme et al. (2017), which addresses a similar question from a numerical modelling perspective. Both papers arise from the newly created FACSIMILE (Field And Computer SIMulation In Landscape Evolution) network, which brings together European modellers and field-based geoscientists investigating landscape evolution at various scales with both tectonic and climatic drivers. This Pattern Oriented Sampling approach allows a more direct comparison with the Pattern Oriented Modelling approaches of numerical fluvial landscape evolution models at multiple spatial and temporal scales.
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192 *Philosophical considerations in applying field data to LEM evaluation*

193 *Calibration and parameterisation*
194 Parameterisation is the inclusion of the most relevant processes for the questions being asked in a particular modelling study. Calibration is setting these parameters to meaningful values for the specific location being modelled. When LEMs are used for studies that fall within the historic time period, then field data is sometimes used for model calibration – i.e. to inform and empirically adjust the parameterisation of the model (see for example Veldkamp et al., 2016). This process can also enable useful learning about model function (Temme et al., 2017). We would argue however that this full calibration is neither common nor useful for geological time-scale LEM studies. This is despite the fact that landscape evolution models contain multiple spatially-varying parameters that may have only a poor relation to field measurements (containing unmeasurable units such as erodibility) and would thus traditionally be targeted for significant calibration. This is because the aim of many landscape evolution models is to explore process outcomes, rather than to closely mimic field results or provide numerical prediction. As stated by Temme et al (2017, p. 28) 'calibration typically distinguishes studies where models support field reconstruction from studies where models are used in a more exploratory manner to ask 'what-if' questions about landscape development.' Whilst it could be argued that prediction could also be used as a term to refer to the interpolation of data spatially or temporally within the modelling process to estimate a value that has not been or cannot be measured this is not the definition of prediction that we are using here. We argue that such temporal interpolation is merely an extension of the process of exploring different pathways of landscape development. Because the models are not required for prediction, extensive calibration of parameters to a specific geomorphological setting is of less value, and indeed might 'tend to remove the physical basis of a model' (Mulligan and Wainwright, 2004, p. 55), for example when parameters are given values that do not make physical sense. It is this physical basis that enables investigation of process outcomes and we would therefore argue needs to be retained.

218 This retention of basic physics is particularly important because rules drawn from short-term process observations do not scale up easily to longer timescales. One reason for this is that magnitude-frequency distributions of the parameterised events driving the process may have been different in the past, particularly when there is no suitable present day analogue. For example, whilst it is clear that periglacial processes have played an important role in fluvial activity and geomorphological change over Pleistocene timescales across Eurasia and North America (e.g. Vandenberghe, 2008), and we understand the links between annual temperature cycle variations and periglacial processes in the modern circum-arctic very well, yet we have no understanding of how such annual freeze-thaw processes differ when occurring in mid-latitude rather than Arctic regions (e.g. Murton and Kolstrup, 2003).

228 In the situation where one is forced to parameterise processes for settings lacking an analogue situation, which is very common when using LEMs, we argue that the researcher should avoid a full calibration of said parameters because it introduces greater certainty into the modelling than there is in the real world. Instead, a wider range of process pathways need to be explored in the LEM than possible using the subset of partial analogue settings for which calibration data would be available. Indeed, not calibrating parameters allows the investigation of process outcomes to also include experiments in which different values of these parameters are investigated, rather than a narrower range of experiments in which they have been 'optimised' in advance of the reported modelling study. For example, Attal et al. (2008) calibrated the model CHILD to known tectonic settings, but
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other parameters in that LEM were varied in series of experimental scenarios. Similarly, a restricted

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range of values can be set for a parameter on the basis of field data without specifying a single value

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through a traditional parameterisation process (e.g. erosion rates estimated between two dated lava

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flow events – van Gorp et al., 2015).

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## Validation versus evaluation

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A second issue to be considered is that of validation. As Oreskes et al. (1994) state, this is intimately

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linked with the process of calibration, which we discuss above. Strict validation uses a separate

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dataset to that used for initial model specification and parameter calibration. However, over

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geological time scales, information relating to each parameter is often too sparse to afford the

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luxury of splitting a dataset into calibration and validation subsets. Indeed, it is usually the case that

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almost all the information available is used to specify initial conditions and narrow down the range

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of parameters used in model runs. Because of this, the only way in which a separate dataset can be

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generated for validation is by systematically leaving out part of the collected data and using only this

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data to compare with the key patterns emerging from model outputs in a form of quasi-validation

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(e.g. Veldkamp et al., 2016). Whilst not strictly independent, this type of quasi-validation is often

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sufficient to indicate if the LEM simulation is in the correct range of process rates and timing. As

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discussed in more detail below, and in Table 2, some quantification of the success of this evaluation

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/ quasi-validation is useful if possible, even though the use of R² values to score performance is

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usually inappropriate.

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## Equifinality

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Thirdly, equifinality is worth discussing because most LEM modelling of river catchments runs

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forward from some initial situation and ends in a simulation of 'the present'. The model output for

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the present is the simplest to both evaluate (comparing modelled and field data) and analyse

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(tracing development through time) for explanatory understanding of landscape evolution and the

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geological / geomorphological record preserved from it. This approach is of course sensitive for

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equifinality, considering that the generated end state in simulations can be reached in many ways

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starting from different initial conditions and physical assumptions, whereas in the real world it was

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just one path. Equifinality is well known to play an important role in fluvial records and their

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modelling by dedicated LEMs (Beven, 1996; Nicholas and Quine, 2010; Veldkamp et al., 2017). Such

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modelling is therefore often coupled with the use of multiple model runs to capture the range of

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statistical variability between different runs with either fixed or varying parameters. The narrative

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favoured for explanation is then adopted from the modelled scenario with the best fit to the present

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day (e.g. Bovy et al., 2016). Where only one scenario fits the geological data available for evaluation,

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equifinality is avoided. However, we argue here that whilst a single modelled scenario can

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sometimes be chosen, this is not always helpful in advancing understanding. Indeed, where more

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than one scenario fits well to the present day, we argue that this should be embraced as defining an

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envelope of possible explanations, narrowing down our understanding of the processes that could

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produce such a suite of features without suggesting an unrealistic level of certainty about which

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landscape history has taken place. If a single solution is still desired, a valuable way of dealing with

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equifinality in such settings is to gradually work through multiple competing hypotheses. This has

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traditionally been a common approach in geomorphology for assessing the plausibility of different
278 conceptual models and has recently been adopted by some ecologists, e.g. Johnson and Omland

279 (2004). It has been shown to be particularly useful in evolutionary biology, a field that bears

280 remarkable similarity to landscape evolution modelling, given the long time-scales involved, lack of

281 data from many time periods other than the present, and the possibility of equifinality e.g. Lytle

282 (2002). A more recent example of this in landscape evolution is the use of field data alone to

283 determine the relative importance of seepage compared to runoff in canyon formation (Lamb et al.,

284 2006). The two stage LEM strategy of Braun and van der Beek (2004) also demonstrates the gradual

285 investigation of different hypotheses, with a second stage adding in modelling of the lithosphere to

286 enable differentiation between two similar outputs based on different synthetic initial topographies.

287 *Initial conditions*

288 Fourthly, the influence of initial conditions should be considered. When the modelling exercise is

289 carried out in a real-world (rather than synthetic) landscape, specifications of the initial digital

290 elevation model (DEM - resolution, x, y and z accuracy) and surface characteristics (sediment

291 thickness, grain size distribution and erodibility) are particularly important. Whilst all models that

292 forward-simulate open systems require specification of initial conditions (e.g. snow cover or soil

293 moisture in hydrological modelling), specifying initial conditions for geological timescales is

294 particularly problematic because of the scale of difference from modern conditions. This is discussed

295 above in relation to calibration and does not apply to other earth surface model types. This scale of

296 difference is important because uncertainty propagation through the modelling process to output

297 DEMs may be significant, and as discussed above equifinality can also play a role in such outcomes.

298 For example, if starting topography 'contains the common processing artefact of steps near contour

299 lines, these steps will tend to become areas of strong localised erosion and deposition that can

300 obscure the larger patterns' (Tucker, 2009, p. 1454). There are two approaches to specifying the

301 initial DEM. The first is to use the modern land surface. This is only possible if change over time is

302 minimal and topographic data are not used to evaluate model outputs. It has the advantage that the

303 uncertainty relating to spatial resolution and associated interpolation is low (e.g. as investigated by

304 Parsons et al., 1997, for hydrological modelling). However, the longer the time period to be

305 modelled, the greater the error associated with using such a surface, especially in models where

306 sensitivity to initial conditions is a significant feature. For example, use of a modern DEM is not

307 appropriate where sediments known to be deposited during the time period modelled are present

308 below the modern land surface or when studying a tectonically triggered episode of deep valley

309 incision (e.g. van de Wiel et al, 2011).

310 Defining an alternative initial DEM or 'palaeoDEM' requires expert judgment based on field

311 experience that is not easily harvested from literature. For example, when incision over time is the

312 main focus, it may be possible to determine surfaces within the landscape from which incision is

313 likely to have started using modern land-surface DEMs as a starting point, such as relict long profiles

314 (e.g. Beckers et al., 2015) or reliably reconstructed and dated palaeosurfaces (e.g. Fuchs et al., 2012).

315 A number of numerical approaches can be adopted here, as outlined by Demoulin et al. (2017).

316 Expert judgment can also suggest palaeosurfaces based on sedimentological investigations. For

317 example, erosional contacts may suggest initial surfaces lay higher prior to a period of erosion, but

318 gradational contacts that initial surfaces were close to the base of the sequence. Such delineation is

319 only worth doing however, if terraced depositional units have a thickness greater than the depth of
320 a typical main channel and thus truly deviate from modern surface conditions (e.g. Boenigk & Frechen, 2006). The disadvantage of using a reconstructed palaeosurface as an initial DEM is that they are 'typically of very coarse spatial resolution, smoothed and subject to considerable uncertainty' (van de Wiel et al., 2011, p. 179). A useful recent development is the application of geospatial interpolation to refine field derived terrace data sets for palaeosurface reconstructions (Geach et al., 2014; van Gorp et al., 2015). This approach can improve the resolution of the initial DEM and thus the quality of the end results but cannot resolve the fundamental problem of reconstructing the unknown.
362 region is buffered, or even 'shredded' with relation to the original signal (Métivier 1999; Castelltort and van den Driessche, 2003; Jerolmack and Paola, 2010; Wittmann et al., 2009; Armitage et al., 2013). We can also determine by how much and where it is delayed by intermittent sediment storage related to hill slope - channel (dis)connectivity (Michaelides and Wainwright, 2002; Veldkamp et al., 2015). Evidence for tectonic response can be ascertained by geomorphologic markers distributed within the drainage network, such as slope break knickpoints resulting from the same regional uplift pulse (e.g. Table 1, Beckers et al., 2015). Nonetheless, as noted by Blum et al. (2013), criteria for distinguishing between allogenic and autogenic control in catchments still remain to be tightly defined and it is recognized by Veldkamp et al. (2017) that there is an urgent need for research strategies that allow the separation of intrinsic and extrinsic record signals using combined fieldwork and modelling.

373 It is also worth discussing where the boundaries of the catchment should be drawn. In full source to sink modelling, all four of the following elements would be included: a record from the source, a record from the sink, a model for the source and a model for the sink. When catchments are small, downstream data can comprise field data from alluvial fans, floodplains and lakes containing deltaic and prodeltaic deposits. When a larger catchment is considered, the downstream regions are sedimentary basins with broad valleys and plains (e.g. megafans, distributive fluvial systems - e.g. Davidson et al., 2013; Nichols and Fisher, 2007, Weissman et al., 2015), lakes (e.g. Schillereff et al., 2015) and/or delta plains and coastal zones (e.g. basins that form part of continental shelves). Often, as discussed below, downstream data from the sink is not readily available and LEM studies simulate only the source area of the catchment, but this is likely to change as the application of LEMs becomes more widespread.

384 We therefore focus here on the small-medium catchment-scale (c. 10-1000 km long channels) over the later parts of the Quaternary where age control is more robust (c. 500,000 years to present) - there is only so much 'badass' behaviour that our LEMs can currently manage. We recognise that for now, this excludes ancient systems where preservation is fragmentary or dating absent or very limited. In such catchments, many originally deposited sediment sequences will have been modified by other depositional or erosional processes that may not be captured within the model specification. If numerical modelling is to be applied to such systems, we suggest that lower order research questions, i.e. a more speculative 'what if?' approach could be used to try to capture the main driving processes over longer time-scales, and that detailed evaluation of model output in relation to field data is not yet possible.

394 Pattern Oriented Sampling of field data for effective evaluation of model outputs

395 We propose evaluation of model output using pattern-matching, because it is a practical solution to some of the difficulties encountered in comparing it against geological data. This is an approach that has been used in ecological research for several decades (e.g. Grimm et al., 1996, 2005), and to some extent in fluvial geomorphology, e.g. Nicholas (2013). In this practical approach, adequate models should be able to (re-)create similar emergent properties to the field data, not only time-series.
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401 Taking this approach requires that we are very specific in defining what these emergent properties

402 or key characteristics are. For any one catchment these may be geomorphological features or

403 sedimentary sequences. Different types of field data will therefore be available from each

404 catchment, some of the most common of which are outlined in Tables 1 and 2. Once identified, both

405 field and model development can be focussed on these catchment-specific properties (Figure 1). This

406 will enable development of model outputs that can be most readily be compared with field data in a

407 combined pattern-oriented modelling (POM) (Grimm and Railsback, 2012) and pattern-oriented

408 sampling (POS) approach. These should be chosen to allow evaluation or quasi-validation, preferably

409 using semi-quantitative measures, as discussed above. It is likely that some fieldwork will already

410 have been undertaken at this stage, but we advocate that these discussions should not be left until

411 after all field data has been collected. Identification of key characteristics to be used in a POM / POS

412 approach should precede a further round of fieldwork and data gathering, this time focussed purely

413 on the key characteristics identified, rather than driven by opportunistic availability of sedimentary

414 sequences (Figure 1). It is our contention that this approach will open up whole catchments and a

415 wider range of field data to study. We do not therefore advocate more fieldwork, but more targeted

416 collection of field data by considering comparison with model output at an earlier stage in the

417 research process.

418 Figure 2 illustrates the type of records that could be sampled if occurring in the investigated

419 research area. These proposed multi-scale records are both erosional landscape features and

420 sedimentary records such as soil depth patterns, hillslope/colluvial records, local alluvial fan records,

421 fluvial terrace records and delta records. The latter are particularly often overlooked in field studies

422 and yet fundamental in providing an independent 'depositional' mirror record of the 'erosional'

423 record in the catchment (e.g. Whittaker et al., 2010; Forzoni et al., 2014). Comparing the catchment

424 and downstream data and partitioning the sediment budget to ensure that the budget 'closes' as

425 effectively as possible (although see caveats in Parsons, 2011) will improve the quality of model

426 input data. Sediment budgeting also better quantifies the field data, enabling more precise

427 evaluation of the match between modelled outputs and field observations. However, it is not always

428 easy to include downstream data. Sometimes sediment budgets cannot be closed if small-scale sinks

429 within the system store sediment over significant time periods (e.g. Blöthe and Korup, 2013), or the

430 downstream record is incomplete (e.g. Parsons, 2011) or 'leaky' (i.e. sediment passes through to

431 even more downstream areas such as the coast, sea or shelf). This 'leakiness' is hard to quantify

432 from the geological record alone (e.g. Jerolmack and Paola, 2010; Godard et al., 2014, Armitage et

433 al., 2013). Non-linearities due to hillslope - channel (dis) connectivity and events such as river

434 capture or glacial interventions would also cause a lack of a clear source to sink connectivity. In

435 relation to other record types, an example is sub-catchment outlet ¹⁰Be erosion rates which can be

436 measured to get time aggregated erosion rates (e.g. Von Blanckenburg, 2005) and combined with

437 sediment budget estimates from source sink comparisons (item 8, Table 2).

438 POS can also be applied not simply for evaluation but also for specifying initial conditions such as

439 sediment thickness and composition for each grid cell, to avoid assuming a uniform cover across the

440 catchment due to limited information. Whilst this may involve more fieldwork, it may rather involve

441 creatively using existing datasets for this new purpose. Good pedological maps can be invaluable in

442 achieving this aim (e.g. Bovy et al., 2016), as can use of geotechnical borehole data. These datasets

443 can also be usefully used for making volumetric comparisons of various types, as noted in Table 2. In
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444 parallel with developments in the automatic recognition of landforms (e.g. Jones et al., 2007) from DEMs, new technologies and data sources such as ground penetrating radar (GPR), other geophysical surveys, LIDAR data (both airborne and scanning vertical faces) and the game changing use of Structure-from-Motion (SfM) to generate high resolution DSMs from aerial and UAV imagery (e.g. Dabskia et al., 2017) make the collection of geomorphological and spatially distributed sedimentary data much more feasible than was previously the case (Demoulin et al., 2007; Del Val et al., 2015). These data can be used iteratively with remotely sensed data both before and after field investigations. This spatially distributed dataset can provide information on erosional and depositional landforms as well as sedimentary units (Tables 1 and 2).

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446 geophysical surveys, LIDAR data (both airborne and scanning vertical faces) and the game changing use of Structure-from-Motion (SfM) to generate high resolution DSMs from aerial and UAV imagery (e.g. Dabskia et al., 2017) make the collection of geomorphological and spatially distributed sedimentary data much more feasible than was previously the case (Demoulin et al., 2007; Del Val et al., 2015). These data can be used iteratively with remotely sensed data both before and after field investigations. This spatially distributed dataset can provide information on erosional and depositional landforms as well as sedimentary units (Tables 1 and 2).

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453 Systematic collection of data from multiple landscape elements using a POS approach generates a better description and understanding of the catchment and thus allows for a more effective evaluation of model output than illustrated by Temme et al. (2017) in their Fig. 4.

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456 The strength of Pattern Oriented Modelling is that it recognises both the inherent (x,y,z,t) uncertainties in specification of initial conditions and the non-linearity of ecological and geomorphological processes and systems. Systematic Pattern Oriented Sampling will allow a more systematic characterisation of the relevant landscape properties that can then be used for systematic sensitivity analysis of the developed LEM. It is for example equally relevant to know where sediments occur and where they do not. For landscape-evolution models, the inherent (x,y,z,t) uncertainties are primarily due to DEMs, sediment thickness / characteristics and dating technique uncertainties. Too often we have much data from particular locations while at the same time we have almost no data outside these unique locations (often boreholes and quarries). Non-linearity evaluation requires approaches such as Monte Carlo sensitivity ensembles to quantify the role of autogenic feedbacks in the model outcomes (Nicholas and Quine, 2010). In order to do this in a meaningful way we have to quantify their spatial and temporal distributions as well as possible. For example, Hajek et al. (2010) statistically define the degree of channel-belt clustering. By comparing the degree of spatial clustering between channel units observed in late Cretaceous-age rocks and a flume experiment, they conclude that the patterns observed could have formed as a result of self-organisation within the system rather than due to external forcing (Humphrey and Heller, 1995). A similar approach is taken with Quaternary age sequences by Bovy et al. (2016).

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460 systematic sensitivity analysis of the developed LEM. It is for example equally relevant to know where sediments occur and where they do not. For landscape-evolution models, the inherent (x,y,z,t) uncertainties are primarily due to DEMs, sediment thickness / characteristics and dating technique uncertainties. Too often we have much data from particular locations while at the same time we have almost no data outside these unique locations (often boreholes and quarries). Non-linearity evaluation requires approaches such as Monte Carlo sensitivity ensembles to quantify the role of autogenic feedbacks in the model outcomes (Nicholas and Quine, 2010). In order to do this in a meaningful way we have to quantify their spatial and temporal distributions as well as possible. For example, Hajek et al. (2010) statistically define the degree of channel-belt clustering. By comparing the degree of spatial clustering between channel units observed in late Cretaceous-age rocks and a flume experiment, they conclude that the patterns observed could have formed as a result of self-organisation within the system rather than due to external forcing (Humphrey and Heller, 1995). A similar approach is taken with Quaternary age sequences by Bovy et al. (2016).

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473 Similarly the strength of Pattern Oriented Sampling (POS) as illustrated in Figure 2 is that it recognises the inherently stochastic nature of sediment preservation at the land surface compared with at-a-point comparisons. POS therefore widens the range of possible field data that can be used whilst simultaneously targeting only those data types that actually add information about the key characteristics identified. It is likely that this will include areas with no sedimentary records, running counter to much current geological fieldwork practice. It may also require the collection of field data for evaluation of model output across the whole catchment. As such it will require an intentional strategy and possibly some additional resources to observe and describe sedimentary successions and landforms even in hard to access locations. We propose here various new data types and patterns as useful for pattern-matching comparisons (Table 2), many of which can be quantified and applied concurrently. As shown in Figure 1, identification of which of these can be used in model evaluation is crucial in guiding fieldwork strategy.

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POS also aids in decision making when attempting to build a robust chronology because sample selection can be targeted to the key characteristics identified for the catchment as shown in Figure 1. For example, where depositional units are the focus, samples should be taken to enable robust comparison between sedimentary units. This means that whilst it is necessary only to undertake chronological analyses from suitable depositional settings (Table 3), chronological data should be sampled both up and downstream (e.g. Chiverrell et al., 2011; Macklin et al., 2012a; Rixhon et al., 2011), combining vertical (successive terrace levels at a given location, e.g. Bahain et al., 2007) and longitudinal (same level at multiple places along the river profile, e.g. Cordier et al., 2014) sampling.

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528 **Pattern Oriented Sampling applied to specific field settings**

529 Three main case study types can be distinguished where different types of field data are relevant to be used in comparisons with model output. These are 1) sedimentary records where the study focus is usually on climate and anthropogenic forcing of fluvial landscape dynamics (e.g. Viveen et al., 2014), 2) the more erosional and morphological records that are often more focussed on tectonic forcing (e.g. Demoulin et al., 2015; Beckers et al., 2015) and 3) study of long-term denudation rates (e.g. Willenbring et al., 2013; Veldkamp et al., 2016). The two first categories are compared in Table 1 and discussed in more detail below in relation to Pattern Oriented Sampling. All case study types have still unresolved challenges related to the previously discussed issues of initial topography, equifinality and the separation of internal complex response from external forcing. Table 1 demonstrates the different data scale emphasis of the two first case study types. Table 2 gives seven potential field data types that can be used to improve field-model pattern comparison.

540 A detailed discussion of the data that will be most useful in evaluating model output is important because the data that is generated separately by the two endeavours (modelling and fieldwork) are by nature very different. For example, field data often comprises detailed study of only a very small part of the catchment (the best or 'type' example). Depending on the methods used to develop a chronology the reconstructed depositional history of a catchment may also lack significant temporal resolution, perhaps due to lack of dateable material or to large error bars. Indeed even the smallest error bars possible are frequently larger than the time intervals used in model runs. In contrast, model outputs have complete spatial coverage (e.g. mapped change in height / volume of sediment deposited) with high temporal resolution, but often lack local detail. Variables outputted by models are also different from those generated from field-based geological records – e.g. sediment and discharge variations which can only be inferred from sedimentary sequences, not directly measured. Whilst a combined POM-POS approach can aim to minimise these differences, it can never completely eliminate them.

553 1) Sedimentary records with a focus on climate and anthropogenic forcing

554 Comparison of sedimentary field data and modelled deposition will involve integration of borehole and 3-D surface data within a single system (Table 2). For example Viveen et al. (2014, Figure 3a) used spatially constrained data on sediment thickness to compare with model output at multiple locations within a catchment, as do Geach et al. (2015). This is not as useful as volumetric data because it potentially masks the volumetric implications of variations in sediment thickness due to confluences, uneven floodplain bases and scour hollows. However, borehole data is not widely available from the regions in which these studies were based, so average sediment thickness had to be used instead. This limits the quality of the match between field and model data in these studies and means they are compared only qualitatively. It is also exemplified by the qualitative comparison of modelled and observed histograms of Holocene 500-yr step sediment delivery for the Rhine and the Meuse delta sediments (Erkens et al. 2006; Erkens, 2009) and catchment-data based quantifications. These studies could potentially be taken further by direct comparison of the modelled and observed volumes of key sediment bodies within a catchment, tightly spatially constrained to ensure comparability (see item 1 in Table 2). An alternative approach to understanding fluvial activity over time using estimates of palaeohydrology (item 2, Table 2) over
569 longer time periods shows that results are highly dependent on the approach used, highlighting a need to develop more standardised approaches for describing Quaternary river archives (both alluvial fans and terraces - e.g. Stokes et al, 2012; Mather & Stokes, 2016; Mather et al., 2017).

572 Meta-analysis, a systematic approach to aggregating dated sedimentary units and landforms in catchments (e.g. Macklin et al., 2013; Thorndycraft and Benito, 2006), can also be used in model evaluation at a catchment-scale. For example, it has been used for comparing periods of aggradation and quiescence found in the modelled and observed records in four adjacent upland catchments (e.g. Coulthard et al., 2005; item 4, Table 2; Figure 3b). The use of consistent protocols for the aggregation of data is important in order to quantify reach-scale variability in the fluvial record (cf. Macklin et al., 2012b), enabling catchment-wide and regional patterns to be detected. What we advocate with the Pattern Oriented Sampling however is not only aggregation but also disaggregation of data to specific locations in the catchment to get a more comprehensive picture of the fluvial system pattern for model comparison. More work also needs to be undertaken on how to quantify the comparison of this data type because it is very dependent on the quality of the chronology (item 4, Table 2).

584 2) *Erosional and morphological records with a focus on tectonic forcing*

585 Where the landscape is mostly erosional and the main landscape driver of interest is crustal uplift (Table 1) high quality morphological data is relevant. Specific DEM-derived metrics (e.g. chi plots, hypsometric integrals, geophysical relief, R/SR – e.g. Cohen et al., 2008; Perron and Roydon, 2013; Demoulin et al., 2015) can be used to quantify field characteristics and integrated into a common GIS software package, which will facilitate pattern-matching with model output in addition to greater understanding of the systems by comparison with other basins. Data such as non-lithologically controlled knickpoints or vertical spacing between fluvial terrace levels may additionally be useful for model output evaluation. As Stange et al. (2014) show, the spacing, timing and tilting (i.e. convergent, divergent or parallel) of exposure dated terrace forms can provide a powerful modelling test of competing hypotheses about the tectonic history of a region (Item 6, Table 2; Figure 3). Significantly more work is needed to quantify the match between field and modelled data in relation to long profiles however. At present this is possible only subjectively. Similarly, many studies support the usefulness of knickpoint mapping (item 7, Table 2). They can be used to test the validity of river-incision models based on the stream power law (e.g. Berlin & Anderson, 2007; Beckers et al., 2015) and evaluate the role of additional controls on incision (e.g. Whittaker et al., 2007, 2008; Whittaker & Boulton, 2012). TCN dating of the progression of erosion waves across drainage systems also enables the two types of data to be compared (e.g. Anthony & Granger, 2007b; Rixhon et al., 2011). However not all knickpoints are valid targets for model-data comparison. For example, a knickpoint in a highly erodible lithology or highly resistant lithology subject to structural discontinuities (e.g. Anton et al 2015) is unlikely to be useful for evaluating landscape evolution modelling of longer timescales because climatic or tectonic controls on migration will be masked. In addition, other tectonic factors will influence fluvial systems, for example dislocation of river courses across laterally or vertically faulted landscapes, differential uplift or subsidence across substrate lithological boundaries or solution driven collapse.

609 3) *Promising new techniques for quantifying denudation rates*
610 *In situ* cosmogenic-based denudation rates, which are inherently spatially and temporally averaged (item 8, Table 2) provide an additional opportunity for a very powerful check on denudation rates produced from landscape evolution models. They can only be used where the relevant assumptions hold (i.e. relatively steady rates of sediment production over time, well-mixed sediment). To date, most comparisons of numerical model output with cosmogenic denudation rates have been undertaken with the aim of better understanding the robustness of the TCN signal, for example in relation to different rates and styles of climate change (Schaller and Ehlers, 2006), or in basins where sediment inputs to the system are dominated by landslides (Yanites et al., 2009). More recently, the ability of spatial analysis of such denudation rates to improve understanding of transient response to a tectonic perturbation has been effectively shown by Willenbring et al. (2013), with an acceptable match between independently modelled and cosmogenic-based basin-wide denudation rates (Figure 4). More recently Veldkamp et al., (2016) used fluvial terrace properties (thickness and timing of deposition/erosion for specific locations) to calibrate a longitudinal profile model. After an elaborate stepwise calibration and sensitivity analysis the derived temporal landscape erosion (sedimentary delivery) rates were compared with measured $^{10}$Be catchment denudation rates (Schaller et al., 2002), proving to be comparable both in rate magnitudes and timing. We therefore propose that this approach has reached sufficient maturity that it should be used more widely in future studies, by using cosmogenic-based denudation rates as a means to evaluate landscape evolution modelling over timescales of $10^2$-$10^5$ years.

629 With regard to intra-catchment pattern identification, TCN-based denudation rates can address this by sampling streams of different orders. Differences between catchments can highlight a specific intrinsic control such as lithology, steepness, a climatic gradient or different tectonic histories (Von Blanckenburg, 2005), which are also key questions often addressed in landscape evolution modelling studies. TCN-based denudation rates help to constrain such controls across a wide range of spatial scales. However, one must bear in mind that a steady state assumption is intrinsic when deriving TCN-denudation rates, such that applications of this method to non-steady state settings should be exercised with care. Non-steady state settings are most common in catchments prone to mass-wasting processes, such as landsliding, where most of the sediments leaving the catchment may originate from a small area and there is therefore incomplete sediment mixing between hillslope and channels, as recorded in differing cosmogenic nuclide concentrations (Small et al., 1997; Norton et al., 2010; Binnie et al., 2006; Savi et al., 2013). Large contrasts in lithology within a catchment may also cause these assumptions to be violated (von Blanckenburg, 2005). In practice, although such situations should be avoided when they are obviously present, they are rare and in many cases TCN have proven to record robust denudation rates over wide ranges of climatic and tectonic settings (Table 2).

645 **Recommendations**

646 Landscape evolution models have moved away from purely theoretical research questions addressed in synthetic landscapes towards answering specific research questions in particular catchments. This brings into sharp relief the nature of the field data that enables effective evaluation of model outputs. We have argued above that the current practise of field data collection does not always allow for this. We believe that there are two key elements to be addressed.
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651 Firstly, researchers need to be aware that landscape evolution models are qualitatively different

652 from other earth surface process models commonly used in the environmental sciences. They

653 operate over longer geological time periods, with sparser datasets and a different purpose. Research

654 questions usually seek explanation rather than numerical prediction, using an insightful

655 simplification approach where minimum numbers of parameters are used. Instead of seeking an

656 optimum set of parameters, different model runs often explore their relative importance and the

657 effects of changing their amplitude. Whilst such forward modelling can result in equifinality, we

658 argue that this should be embraced as narrowing down the plausible set of events that have

659 occurred in the catchment, even if not converging on a single outcome. Indeed such convergence

660 might suggest a greater level of certainty than is actually present and thus be misleading.

661 Secondly, we advocate the use of a quantitative pattern-matching approach for field-model

662 evaluation such as that often used in ecological studies. Recognizing that fluvial landscape evolution

663 modelling is also a pattern-oriented modelling (POM) approach (Grimm and Railsback, 2012),

664 generating geological field data that is comparable with model output will require adaptation of

665 fieldwork strategies using pattern-oriented sampling (POS). This sampling should focus only on data

666 that provides information about identified key characteristics of the catchment (Figure 1). This will

667 embrace a wider range of data types overall (Figure 2), but not increase the burden of data

668 collection for study of a specific catchment. A number of suitable data targets for such an approach

669 are outlined in Table 2 and exemplified in Figures 3-5 and related text.

670 We have shown that Pattern Oriented Sampling is starting to be applied in some cases. However, we

671 believe that the community should more generally apply these principles in a structured way. Our

672 aim as FACSIMILE is to facilitate a research approach that compares this wider range of field data

673 with model output from a range of model types. Given that it is neither possible nor desirable to

674 model all systems, we are in the process of working on a specific field catchment where initial

675 pattern-matching model-data comparisons can be undertaken to determine further which

676 approaches are most useful.

677 Acknowledgements

678 The authors are grateful to the Faculty of Geo-Information Science and Earth Observation of the

679 University of Twente who provided support for two workshops during which these ideas were

680 developed. We also thank three anonymous referees and the Associate Editor Professor Stuart Lane

681 whose comments greatly clarified our thinking on these matters.

682 References

683 Adamson KR, Woodward JC, Hughes, PD. 2014. Glaciers and rivers: Pleistocene uncoupling in a

684 Mediterranean mountain karst. *Quaternary Science Reviews* **94**: 28-43.

685 Anderson RS, Repka JL, Dick GS. 1996. Explicit treatment of inheritance in dating depositional surface

686 using in situ ¹⁰Be and ²⁶Al. *Geology* **24**: 47-51.
687 Anthony DM, Granger DE. 2007a. A new chronology for the age of Appalachian erosional surfaces

688 determined by cosmogenic nuclides in cave sediments. *Earth, Surface, Processes and Landforms* **32**:

689 874-887.

690 Anthony D, Granger D. 2007b. An empirical stream power formulation for knickpoint retreat in

691 Appalachian Plateau fluviokarst. *Journal of Hydrology* **343**: 117-126.

692 Antón, L., Mather, A.E., Stokes, M., Munoz-Martin, A., De Vicente, G., 2015. Exceptional river gorge

693 formation from unexceptional floods. *Nature Communications* **6**, 7963, doi:10.1038/ncomms8963

694 Armitage JJ, Jones TD, Duller R, Whittaker AC, Allen PA. 2013. Temporal buffering of climate-driven

695 sediment flux cycles by transient catchment response. *Earth and Planetary Science Letters* **369**: 200-

696 210.

697 Attal M, Tucker GE, Whittaker AC, Cowie PA, Roberts GP, 2008. Modeling fluvial incision and

698 transient landscape evolution: Influence of dynamic channel adjustment. *Journal of Geophysical

699 Research: Earth Surface, 113(F3).

700 Balco G, Rovey CW. 2008. An isochron method for cosmogenic-nuclide dating of buried soils and

701 sediments. *American Journal of Science* **308**(10): 1083-1114.

702 Balco G, Soreghan GS, Sweet DE, Marra KR, Bierman PR. 2013. Cosmogenic-nuclide burial ages for

703 Pleistocene sedimentary fill in Unaweep Canyon, Colorado, USA. *Quaternary Geochronology* **18**: 149-

704 157.

705 Bahain J-J, Falguères C, Laurent M, Voinchet P, Dolo J-M, Antoine P, Tuffreau A. 2007. ESR

706 chronology of the Somme River Terrace system and first human settlements in Northern France.

707 *Quaternary Geochronology* **2**: 356-362.

708 Bayliss A, Brock F, Farid S, Hodder I, Southon J, Taylor RE. 2015. Getting to the Bottom of It All: A

709 Bayesian Approach to Dating the Start of Çatalhöyük. *Journal of World Prehistory* **28**: 1-26.

710 Beckers A, Bovy B, Hallot E, Demoulin A. 2015. Controls on knickpoint migration in a drainage

711 network of the moderately uplifted Ardennes Plateau, Western Europe. *Earth Surface Processes and

712 Landforms* **40**(3): 357-374. DOI: 10.1002/esp.3638.

713 Benito G, Macklin MG, Zielhofer C, Jones AF, Machado MJ. 2015. Holocene flooding and climate

714 change in the Mediterranean. *Catena* **130**: 13-33.

715 Berlin M, Anderson R. 2007. Modeling of knickpoint retreat on the Roan Plateau, western Colorado.

716 *Journal of Geophysical Research* **112**: F03S06, doi:10.1029/2006JF000553.

717 Beven KJ, 1996. Equifinality and Uncertainty in Geomorphological Modelling, in B L Rhoads and C E

718 Thorn (Eds.), *The Scientific Nature of Geomorphology*, Wiley, Chichester, 289-313
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

719 Bird MI, Ayliffe LK, Fifield LK, Turney CSM, Cresswell RG, Barrows TT, David B. 1999. Radiocarbon

720 dating of 'old' charcoal using a wet oxidation-stepped combustion procedure. *Radiocarbon* **41**: 127–

721 140.

722 Binnie SA, Phillips WM, Summerfield MA, Fifield LK. 2006. Sediment mixing and basin-wide

723 cosmogenic nuclide analysis in rapidly eroding mountainous environments. *Quaternary*

724 *Geochronology* **1**(1): 4-14.

725 Blöthe JH, Korup O. 2013. Millennial lag times in the Himalayan sediment routing system. *Earth and*

726 *Planetary Science Letters* **382**, 38-46.

727 Blum MD, Törnqvist TE, 2000. Fluvial responses to climate and sea - level change: a review and look

728 forward. *Sedimentology* **47**: pp.2-48.

729 Blum M, Martin J, Milliken K, Garvin M. 2013. Paleovalley systems: insights from Quaternary analogs

730 and experiments. *Earth-Science Reviews* **116**: 128-169.

731 Boenigk W, Frechen M. 2006. The Pliocene and Quaternary fluvial archives of the Rhine system.

732 *Quaternary Science Reviews* **25**(5): 550-574.

733 Bovy B, Braun J, Demoulin A. 2016. Soil production and hillslope transport in mid-latitudes during the

734 last glacial-interglacial cycle: a combined data and modelling approach in northern Ardennes. *Earth*

735 *Surface Processes and Landforms* **41**, 1758–1775.

736 Braun J, van der Beek P 2004. Evolution of passive margin escarpments: what can we learn from low

737 temperature thermochronometry? *Journal of Geophysical Research: Earth Surface* **109**(F4).

738 Brocard GY, Van Der Beek PA, Bourlès DL, Siame LL, Mugnier JL. 2003. Long-term fluvial incision rates

739 and postglacial river relaxation time in the French Western Alps from 10 Be dating of alluvial terraces

740 with assessment of inheritance, soil development and wind ablation effects. *Earth and Planetary*

741 *Science Letters* **209**(1): 197-214.

742 Briant RM, Bates MR, Schwenninger J-L Wenban-Smith FF. 2006. A long optically-stimulated

743 luminescence dated Middle to Late Pleistocene fluvial sequence from the western Solent Basin,

744 southern England. *Journal of Quaternary Science* **21**: 507-523.

745 Briant RM, Brock F, Langford HE, DeMarchi B, Penkman KEH, Schreve DC, Schwenninger J-L. 2018.

746 Improving chronological control for environmental sequences from the last glacial period.

747 *Quaternary Geochronology* **43**: 40-49.

748 Briant RM, Bateman MD. 2009. Luminescence dating indicates radiocarbon age under-estimation in

749 Late Pleistocene fluvial deposits from eastern England. *Journal of Quaternary Science* **24**: 916-927.

750 DOI: 10.1002/jqs.1258.

751 Bridgland, D. and Westaway, R., 2008. Climatically controlled river terrace staircases: a worldwide

752 Quaternary phenomenon. *Geomorphology*, **98**, 285-315.
753 Burbank DW, Leland J, Fielding E, Anderson RS, Brozovic N, Reid MR, Duncan C. 1996. Bedrock incision, rock uplift and threshold hillslopes in the northwestern Himalayas. *Nature* **379**: 505-510.

754 incision, rock uplift and threshold hillslopes in the northwestern Himalayas. *Nature* **379**: 505-510.

755 Busschers FS, Cohen KM, Vandenberghe J, Van Balen RT, Kasse C. Wallinga J, Weerts HJ. 2011.

756 Comment on 'Causes, consequences and chronology of large-magnitude palaeoflows in Middle and Late Pleistocene river systems of northwest Europe' by Westaway and Bridgland (2010). *Earth Surface Processes and Landforms* **36**(13): 1836-1840.

757 Late Pleistocene river systems of northwest Europe' by Westaway and Bridgland (2010). *Earth Surface Processes and Landforms* **36**(13): 1836-1840.

759 Busschers FS, Wesselingh FP, Kars RH. 2014. Radiocarbon dating of Late Pleistocene marine shells from the southern North Sea. *Radiocarbon* **56**: 1151-1166.

760 from the southern North Sea. *Radiocarbon* **56**: 1151-1166.

761 Camporeale C, Perona P, Porporato A, Ridolfi L, 2007. Hierarchy of models for meandering rivers and related morphodynamic processes. *Reviews of Geophysics* **45**.

762 related morphodynamic processes. *Reviews of Geophysics* **45**.

763 Castelltort S, Van Den Driessche J. 2003. How plausible are high-frequency sediment supply-driven cycles in the stratigraphic record? *Sedimentary Geology* **157**(1): 3-13.

764 cycles in the stratigraphic record? *Sedimentary Geology* **157**(1): 3-13.

765 Chaussé C, Voinchet P, Bahain JJ, Connet N, Lhomme V, Limondin-Lozouët N. 2004. Middle and Upper Pleistocene evolution of the River Yonne valley (France). First results. *Quaternaire* **15**: 53-64.

766 Upper Pleistocene evolution of the River Yonne valley (France). First results. *Quaternaire* **15**: 53-64.

767 Chiverrell RC, Thorndycraft VR, Hoffmann TO. 2011. Cumulative probability functions and their role in evaluating the chronology of geomorphological events during the Holocene. *Journal of Quaternary Science* **26**(1): 76-85.

768 in evaluating the chronology of geomorphological events during the Holocene. *Journal of Quaternary Science* **26**(1): 76-85.

769 *Science* **26**(1): 76-85.

770 Choi S-W, Preusser F, Radtke U. 2007. Dating of lower terrace sediments from the Middle Rhine area, Germany. *Quaternary Geochronology* **2**: 137-142.

771 area, Germany. *Quaternary Geochronology* **2**: 137-142.

772 Codilean, A.T., Bishop, P. and Hoey, T.B., 2006. Surface process models and the links between tectonics and topography. *Progress in Physical Geography* **30** 307-333.

773 tectonics and topography. *Progress in Physical Geography* **30** 307-333.

774 Cohen S, Willgoose GR, Hancock GR. 2008. A methodology for calculating the spatial distribution of the area-slope equation and the hypsometric integral within a catchment. *Journal of Geophysical Research-Earth Surface* 113 F03027.

775 the area-slope equation and the hypsometric integral within a catchment. *Journal of Geophysical Research-Earth Surface* 113 F03027.

776 *Research-Earth Surface* 113 F03027.

777 Cordier S, Harmand D, Lauer T, Voinchet P, Bahain J-J, Frechen M. 2012. Geochronological reconstruction of the Pleistocene evolution of the Sarre valley (France and Germany) using OSL and ESR dating techniques. *Geomorphology* **165-166**: 91-106.

778 reconstruction of the Pleistocene evolution of the Sarre valley (France and Germany) using OSL and ESR dating techniques. *Geomorphology* **165-166**: 91-106.

779 ESR dating techniques. *Geomorphology* **165-166**: 91-106.

780 Cordier S, Frechen M, Harmand D. 2014. Dating fluvial erosion: fluvial response to climate change in the Moselle catchment (France, Germany) since the Late Saalian. *Boreas* **43**: 450-468.

781 the Moselle catchment (France, Germany) since the Late Saalian. *Boreas* **43**: 450-468.

782 Cordier, S., Adamson, K., Delmas, M., Calvet, M. and Harmand, D., 2017. Of ice and water: Quaternary fluvial response to glacial forcing. *Quaternary Science Reviews*, **166**, 57-73.

783 Quaternary fluvial response to glacial forcing. *Quaternary Science Reviews*, **166**, 57-73.

784 Coulthard TJ, Van de Wiel MJ, 2013. Climate, tectonics or morphology: what signals can we see in drainage basin sediment yields? *Earth Surface Dynamics* 1, 67-91.

785 drainage basin sediment yields? *Earth Surface Dynamics* 1, 67-91.
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

786 Coulthard TJ, Lewin J, Macklin MG. 2005. Modelling differential catchment response to environmental change. *Geomorphology* **69**: 222–241.

787

788 Crosby BT, Whipple KX. 2006. Knickpoint initiation and distribution within fluvial networks: 236 waterfalls in the Waipaoa River, North Island, New Zealand. *Geomorphology* **82(1)**: 16-38.

789

790 Dąbskia M, Zmarzb A, Pabjanekb P, Korczak-Abshirec M, Karszniab I, Chwedorzewskac, KJ, 2017.

791 UAV-based detection and spatial analyses of periglacial landforms on Demay. Point (King George Island, South Shetland Islands, Antarctica). *Geomorphology* **290**, 29-38.

792

793 Davidson SK, Hartley AJ, Weissman GS, Nichols GJ, Scuderi LA. 2013. Geomorphic elements on modern distributive systems. *Geomorphology* **180-181**, 82-95.

794

795 Davis, W.M., 1922. Peneplains and the geographical cycle. *Geological Society of America Bulletin*, **33**,

796 587-598.

797 Demoulin A, Bovy B, Rixhon G, Cornet Y. 2007. An automated method to extract fluvial terraces from digital elevation models: the Vesdre valley, a case study in eastern Belgium. *Geomorphology* **91**: 51-64.

798

799 64.

800 Demoulin A, Beckers A, Hubert-Ferrari A, 2015. Patterns of Quaternary uplift of the Corinth rift

801 southern border (N Peloponnese, Greece) revealed by fluvial landscape morphometry.

802 *Geomorphology*, **246**, 188-204.

803 Demoulin, A., Mather, A. and Whittaker, A., 2017. Fluvial archives, a valuable record of vertical crustal deformation. *Quaternary Science Reviews*, **166**, 10-37.

804

805 Del Val M, Iriarte E, Arriolabengoa M, Aranburu A. 2015. An automated method to extract fluvial terraces from LIDAR based high resolution Digital Elevation Models: The Oiartzun valley, a case study in the Cantabrian Margin. *Quaternary International* **364**: 35-43.

806

807

808 Erkens G. 2009. Sediment dynamics in the Rhine catchment - Quantification of fluvial response to climate change and human impact. *Netherlands Geographical Studies* **388**: pp. 278.

809

810 Erkens G, Cohen KM, Gouw MJP, Middelkoop H, Hoek WZ. 2006. Holocene sediment budgets of the Rhine Delta (The Netherlands): a record of changing sediment delivery. In Rowan JS, Duck RW, Werritty A. (eds.), *Sediment Dynamics and the Hydromorphology of Fluvial Systems*: 406-415. IAHS.

811

812 Werritty A. (eds.), *Sediment Dynamics and the Hydromorphology of Fluvial Systems*: 406-415. IAHS.

813 Erlanger ED, Granger DE, Gibbon RJ. 2012. Rock uplift rates in South Africa from isochron burial dating of fluvial and marine terraces. *Geology* **40**: 1019–1022.

814

815 Forzoni A, Storms JEA, Moreau J, Reimann T, de Jager G. 2014. Upstream versus downstream forcing: the response of the Golo River system, Corsica, to late quaternary climatic and eustatic oscillations. In *Proceedings of the Nederlands aardwetenschappelijk congres 12 (NAC 12)*, 8 en 9 april 2014, Veldhoven, Nederland. Faculty Geosciences, University of Utrecht.

816

817

818
819 Fryirs KA, Brierley GJ, Preston NJ, Kasai M. 2007. Buffers, barriers and blankets: The (dis)connectivity of catchment-scale sediment cascades. *Catena* **70**: 49-67.

821 Fuchs M, Will M, Kunert E, Kreutzer S, Fischer M, Reverman R. 2012. The temporal and spatial quantification of Holocene sediment dynamics in a meso-scale catchment in northern Bavaria, Germany. *The Holocene* **21**: 1093-1104.

824 Geach MR, Stokes M, Telfer MW, Mather AE, Fyfe RM, Lewin S. 2014. The application of geospatial interpolation methods in the reconstruction of Quaternary landform records. *Geomorphology* **216**, 234-246.

827 Geach MR, Viveen W, Mather AE, Telfer MW, Fletcher WJ, Stokes M, Peyron O. 2015. An integrated field and numerical modelling study of controls on Late Quaternary fluvial landscape development (Tabernas, SE Spain). *Earth Surface Processes and Landforms*. DOI 10.1002/esp.3768

830 Gibbard, P.L. and Lewin, J., 2002. Climate and related controls on interglacial fluvial sedimentation in lowland Britain. *Sedimentary Geology*, **151**, 187-210.

832 Godard V, Bourlès DL, Spinabella F, Burbank DW, Bookhagen B, Fisher GB. Léanni L. 2014. Dominance of tectonics over climate in Himalayan denudation. *Geology* **42**(3): 243-246.

834 Granger DE, Fabel D, Palmer A. 2001. Pliocene-Pleistocene incision of the Green River, Kentucky, determined from radioactive decay of cosmogenic²⁶Al and ¹⁰Be in Mammoth Cave sediments. *GSA Bulletin* **113**: 825-836.

837 Grimm V, Railsback SF, 2012. Pattern-oriented modelling: a multi-scope for predictive systems ecology. Phil. Trans. R. Soc. B **367**: 298-310.

839 Grimm V, Frank K, Jeltsch F, Brandl R, Uchmanski J, Wissel C. 1996. Pattern-oriented modelling in population ecology. *Science of the Total Environment* **183**: 151-166. doi:10.1016/0048-9697(95)04966-5.

842 Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke H-H, Weiner J, Wiegand T, DeAngelis DL. 2005. Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. *Science* **310**: 987-991. DOI: 10.1126/science.1116681.

845 Hajek EA, Heller PL, Sheets BA. 2010. Significance of channel-belt clustering in alluvial basins. *Geology* **38**: 535-538.

847 Higham TFG, Jacobi RM, Bronk Ramsey C. 2006. AMS radiocarbon dating of ancient bone using ultrafiltration. *Radiocarbon* **48**: 179-195

849 Humphrey NF, Heller PL. 1995. Natural oscillations in coupled geomorphic systems: an alternative origin for cyclic sedimentation. *Geology* **23**: 499-502.
851 Jerolmack DJ, Paola C. 2010. Shredding of environmental signals by sediment transport. *Geophysical

852 *Research Letters* **37**(19).

853 Johnson, J.B. and Omland, K.S., 2004. Model selection in ecology and evolution. *Trends in ecology &

854 *evolution* **19**: 101-108.

855 Jones AF, Brewer PA, Johnstone E, Macklin MG. 2007. High-resolution interpretative

856 geomorphological mapping of river valley environments using airborne LiDAR data. *Earth Surface*

857 *Processes and Landforms* **32**: 1574-1592.

858 Jones AF, Macklin MG, Benito G. 2015. Meta-analysis of Holocene fluvial sedimentary archives: A

859 methodological primer. *Catena* **130**: 3-12.

860 Kars RH, Busschers FS, Wallinga J. 2012. Validating post IR-IRSL dating on K-feldspars through

861 comparison with quartz OSL ages. *Quaternary Geochronology* **12**: 74-86.

862 King, GE, Herman, F, Guralnik, B, 2016. Northward migration of the eastern Himalayan syntaxis

863 revealed by OSL thermochronometry. *Science* **353**: 800-804.

864 Kock S, Kramers JD, Preusser F, Wetzel A. 2009. Dating of Late Pleistocene terrace deposits of the

865 River Rhine using Uranium series and luminescence methods: Potential and limitations. *Quaternary*

866 *Geochronology* **4**: 363-373.

867 Lamb MP, Howard AD, Johnson J, Whipple KX, Dietrich WE, Perron JT, 2006. Can springs cut canyons

868 into rock? *Journal of Geophysical Research: Planets* **111**(E7).

869 Larsen L, Thomas C, Eppinga M, Coulthard T, 2014. Exploratory modeling: Extracting causality from

870 complexity. *Eos, Transactions American Geophysical Union* **95**: 285-286.

871 Lewis SG, Maddy D, Scaife RG. 2001. The fluvial system response to abrupt climate change during

872 the last cold stage: the Upper Pleistocene River Thames fluvial succession at Ashton Keynes, UK.

873 *Global and Planetary Change* **28**: 341-359.

874 Lytle, D.A. 2002. Flash floods and aquatic insect life-history evolution: evaluation of multiple models.

875 *Ecology* **83**: 370-385.

876 Macklin, M.G., Jones, A.F. and Lewin, J., 2010. River response to rapid Holocene environmental

877 change: evidence and explanation in British catchments. *Quaternary Science Reviews*, **29**, 1555-

878 1576.

879 Macklin MG, Fuller IC, Jones AF, Bebbington M. 2012a. New Zealand and UK Holocene flooding

880 demonstrates interhemispheric climate asynchrony. *Geology* **40**: 775-778.

881 Macklin MG, Lewin J, Woodward JC. 2012b. The fluvial record of climate change. *Philosophical*

882 *Transactions of the Royal Society A* **370**: 2143-2172.
883 Macklin MG, Lewin J, Jones AF. 2013. River entrenchment and terrace formation in the UK Holocene.

884 *Quaternary Science Reviews* **76**: 194-206.

885 Maddy, D., 1997. Uplift-driven valley incision and river terrace formation in southern England.

886 *Journal of Quaternary Science*, **12**, 539-545.

887 Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J. Beer, A. Ganopolski, J.F. González Rouco, E. Jansen,

888 K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B. Otto-Bliesner, T. Quinn, R. Ramesh, M. Rojas, X.

889 Shao and A. Timmermann, 2013: Information from Paleoclimate Archives. In: *Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change* [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.

890
891 *The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change* [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.

892 Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press,

893 Cambridge, United Kingdom and New York, NY, USA.

894 Mather, A.E., Stokes, M., Griffiths, J.S., 2002. Quaternary landscape evolution: A framework for

895 understanding contemporary erosion, southeast Spain. *Land Degradation and Development* **13**, 89-109.

896

897 Mather AE, Stokes M. 2016. Extracting palaeoflood data from coarse-grained Pleistocene river

898 terrace archives: an example from SE Spain. *Earth Surface Processes and Landforms* **41**: 1991–2004,

899 DOI: 10.1002/esp.4001.

900 Mather, A.E., Stokes, M. and Whitfield, E., 2017. River terraces and alluvial fans: The case for an

901 integrated Quaternary fluvial archive. *Quaternary Science Reviews*, **166**, 74-90.

902 Métivier F. 1999. Diffusivelike buffering and saturation of large rivers. *Physical Review E* **60**: 5827.

903 Michaelides K, Wainwright J, 2002. Modelling the effects of hillslope-channel coupling on catchment

904 hydrological response. Earth Surface Processes and Landforms **27**: 1441-1457.

905 Milliman JD, Syvitski JP, 1992. Geomorphic/tectonic control of sediment discharge to the ocean: the

906 importance of small mountainous rivers. *The Journal of Geology* **100**: 525-544.

907 Mulligan M, Wainwright J. 2004. Modelling and Model Building. In Mulligan M, Wainwright J (eds)

908 *Environmental Modelling: Finding Simplicity in Complexity*. Wiley, Chichester, p. 7-73.

909 Murton J.B. and Kolstrup, E., 2003. Ice-wedge casts as indicators of palaeotemperatures: precise

910 proxy or wishful thinking? *Progress in Physical Geography* **27**: 155-170.

911 Nichols GJ, Fisher JA. 2007. Processes, facies and architecture of fluvial distributary system deposits.

912 *Sedimentary Geology* **195**: 75-90.

913 Nicholas AP, Quine TA, 2010. Quantitative assessment of landform equifinality and

914 palaeoenvironmental reconstruction using geomorphic models. *Geomorphology* **121**: 167–183.
915 Nicholas AP. 2013. Modelling the continuum of river channel patterns. *Earth Surface Processes and Landforms* **38**: 1187–1196

916

917 Norton KP, Abbühl LM, Schlunegger F. 2010. Glacial conditioning as an erosional driving force in the

918 Central Alps. *Geology* **38**: 655-658.

919 Oreskes N, Shrader-Frechette K, Belitz K. 1994. Verification, validation, and confirmation of

920 numerical models in the earth sciences. *Science* **263**: 641-646.

921 Parsons AJ, 2011. How useful are catchment sediment budgets? *Progress in Physical Geography*,

922 0309133311424591.

923 Parsons AJ, Wainwright J, Abrahams AD, Simanton JR. 1997. Distributed dynamic modelling of

924 interrill overland flow. *Hydrological Processes* **11**: 1833-1859.

925 Perron JT, Royden L. 2013. An integral approach to bedrock river profile analysis. *Earth Surface*

926 *Processes and Landforms* **38**(6), 570-576.

927 Phillips JT. 2015. Badass geomorphology. *Earth Surface Processes and Landforms* 40, 22–33.

928 Pierce JL, Meyer GA, Rittenour T. 2011. The relation of Holocene fluvial terraces to changes in

929 climate and sediment supply, South Fork Payette River, Idaho. *Quaternary Science Reviews* **30**: 628-

930 645.

931 Reusser LJ, Bierman PR, Pavich MJ, Zen E, Larsen J, Finkel R. 2004. Rapid Late Pleistocene incision of

932 Atlantic passive-margin river gorges. *Science* **305**: 499-502.

933 Rixhon G, Braucher R, Bourlès D, Siame L, Bovy B, Demoulin A. 2011. Quaternary river incision in NE

934 Ardennes (Belgium)—Insights from ¹⁰Be/²¹Al dating of river terraces. *Quaternary Geochronology* **6**:

935 273–284.

936 Rixhon, G., Briant, R.M., Cordier, S., Duval, M., Jones, A. and Scholz, D., 2017. Revealing the pace of

937 river landscape evolution during the Quaternary: recent developments in numerical dating methods.

938 *Quaternary Science Reviews*, **166**, 91-113.

939 Savi S, Norton K, Picotti V, Brardinoni F, Akçar N, Kubik PW, Schlunegger F. 2014. Effects of sediment

940 mixing on 10 Be concentrations in the Zielbach catchment, central-eastern Italian Alps. *Quaternary*

941 *Geochronology* **19**: 148-162.

942 Schaller M, von Blanckenburg F, Hovius N, Kubik PW. 2001. Large-scale erosion rates from *in situ*-

943 produced cosmogenic nuclides in European river sediments. *Earth and Planetary Science Letters* **188**:

944 441-458.

945 Schaller M, Ehlers TA. 2006. Limits to quantifying climate driven changes in denudation rates with

946 cosmogenic radionuclides. *Earth and Planetary Science Letters* **248**: 153-167.
947 Schaller M, van Blanckenburg F, Veldkamp A, Tebbens LA, Hovius N, Kubik PW, 2002. A 30000 yr record of erosion rates from cosmogenic 10Be in Middle European river terraces. Earth and Planetary Science Letters 204: 307-320.

950 Schillereff DN, Chiverrell RC, Macdonald N, Hooke JM. 2015. Flood stratigraphies in lake sediments: A review, Earth-Science Reviews 135: 17-37

952 Schmitz B, Pujalte V. 2007. Abrupt increase in seasonal extreme precipitation at the Paleocene-Eocene boundary. Geology 35: 215-218.

954 Schoorl, J.M., Temme, A.J.A.M., Veldkamp, A., 2014. Modelling centennial sediment waves in an eroding landscape - catchment complexity. Earth Surface Processes and Landforms 39: 1526-1537.

956 Schulte L, Julià R, Burjachs F, Hilgers A. 2008. Middle Pleistocene to Holocene geochronology of the River Aguas terrace sequence (Iberian Peninsula): Fluvial response to Mediterranean environmental change. Geomorphology 98: 13-33.

959 Schumm SA, 1973. Geomorphic thresholds and complex response of drainage systems. Fluvial geomorphology 6: 69-85.

961 Seong YB, Owen LA, Bishop MP, Bush A, Clendon P, Copland L, Finkel RC, Kamp U, Schroder Jr. JF. 2008. Rates of fluvial bedrock incision within an actively uplifting orogen: Central Karakoram Mountains, northern Pakistan. Geomorphology 97: 274-286.

964 Sharp WD, Ludwig KR, Chadwick OA, Amundson R, Glaser LL. 2003. Dating fluvial terraces by $^{230}$Th/U on pedogenic carbonate, Wind River Basin, Wyoming. Quaternary Research 59: 139-150.

966 Small EE, Anderson RS, Repka JL, Finkel R. 1997. Erosion rates of alpine bedrock summit surfaces deduced from in situ 10Be and 26Al. Earth and Planetary Science Letters 150: 413-425.

968 Snyder NP, Whipple KX, Tucker GE, Merritts DJ. 2003. Channel response to tectonic forcing: field analysis of stream morphology and hydrology in the Mendocino triple junction region, northern California. Geomorphology 53: 97-127.

971 Stange KM, Van Balen RT, Garcia-Castellanos D, Cloetingh S. 2014. Numerical modelling of Quaternary terrace staircase formation in the Ebro foreland basin, southern Pyrenees, NE Iberia. Basin Research.

974 Stokes M, Mather AE, Harvey AM. 2002. Quantification of river capture induced base-level changes and landscape development, Sorbas Basin, SE Spain. In: Jones SJ, Frostick LE. (eds) Sediment Flux to Basins: Causes, Controls and Consequences. Geological Society, London Special Publication, 191, 23-35.

978 Stokes M, Griffiths JS, Mather AE. 2012. Palaeoflood estimates of Pleistocene coarse grained river terrace landforms (Rio Almanzora, SE Spain). Geomorphology 149, 11-26.
980 Talling PJ, 1998. How and where do incised valleys form if sea level remains above the shelf edge?

981 *Geology* **26**: 87-90.

982 Taylor KE, Stouffer RJ, Meehl GA, 2012. An overview of CMIP5 and the experiment design. *Bulletin of*

983 *the American Meteorological Society*, **93**, 485-498.

984 Temme AJAM, Veldkamp A, 2009. Multi-process Late Quaternary landscape evolution modelling

985 reveals lags in climate response over small spatial scales. *Earth Surface Processes and Landforms*, **34**:

986 573-589.

987 Temme AJAM, Armitage J, Attal, M, van Gorp W, Coulthard T, Schoorl JM. 2017. Choosing and using

988 landscape evolution models to inform field-stratigraphy and landscape reconstruction studies. *Earth*

989 *Surface Processes and Landforms* **42**, 2167–2183.

990 Thorndycraft VR, Benito G. 2006. The Holocene fluvial chronology of Spain: evidence from a newly

991 compiled radiocarbon database. *Quaternary Science Reviews* **25**: 223-234.

992 Toms PS. 2013. Optical dating of Broom. In *Quaternary History & Palaeolithic Archaeology in the Axe*

993 *Valley at Broom* (Hosfield R, Green C, Chambers J. (eds). Oxbow.

994 Tucker GE. 2009. Natural experiments in landscape evolution. *Earth Surface Processes and*

995 *Landforms* **34**: 1450-1459.

996 Van De Wiel MJ, Coulthard TJ, Macklin MG, Lewin J. 2011. Modelling the response of river systems

997 to environmental change: Progress, problems and prospects for palaeo-environmental

998 reconstructions. *Earth-Science Reviews* **104**: 167–185.

999 Van Der Beek, P. and Bishop, P., 2003. Cenozoic river profile development in the Upper Lachlan

1000 catchment (SE Australia) as a test of quantitative fluvial incision models. *Journal of Geophysical*

1001 *Research: Solid Earth* **108**(B6).

1002 Valla P, Shuster D, Van der Beek P. 2011. Significant increase in relief of the European Alps during

1003 mid-Pleistocene glaciations. *Nature Geoscience* **4**: 688-692

1004 van Balen RT, Busschers FS, Tucker GE. 2010. Modeling the response of the Rhine–Meuse fluvial

1005 system to Late Pleistocene climate change. *Geomorphology* **114**: 440-452.

1006 Van Gorp W, Temme AJAM, Veldkamp A, Schoorl JM, 2015. Modelling long-term (300 ka) upland

1007 catchment response to multiple lava damming events. *Earth Surface Processes and Landforms* **40**:

1008 888-900.

1009 Vandenberghe J, 1995. Timescales, climate and river development. *Quaternary Science Reviews* **14**:

1010 631-638.

1011 Vandenberghe, J., 2008. The fluvial cycle at cold–warm–cold transitions in lowland regions: a

1012 refinement of theory. *Geomorphology*, **98**, 275-284.
1013 Veldkamp A, Tebbens LA, 2001. Registration of abrupt climate changes within fluvial systems:

1014 insights from numerical modelling experiments. Global and Planetary Change **28**: 129 - 144.

1015 Veldkamp A, Kroonenberg SB, Heijnis H, van den Berg van Saparoea, R. 2004. The suitability of dated

1016 travertines as a record of fluvial incision: Allier (France) floodplain dynamics during the late

1017 Quaternary. *Quaternaire* **15**: 159-165

1018 Veldkamp A, Candy I, Jongmans AG, Maddy D, Demir T, Schoorl JM, Schreve D, Stemerdink C, van

1019 der Schriek T. 2015, Reconstructing Early Pleistocene (1.3 Ma) terrestrial environmental change in

1020 western Anatolia: did it drive fluvial terrace formation? *Palaeogeography Palaeoclimatology*

1021 *Palaeoecology*, **417**: 91-104.

1022 Veldkamp A, Schoorl JM, Viveen W, 2016. Predicting location-specific fluvial archives with the

1023 FLUVER2 model for the late Quaternary Allier River (France). Earth Surface Processes and Landforms

1024 **41**: 2256-2268

1025 Veldkamp A, Baartman JEM, Coulthard TJ, Maddy D, Schoorl JM, Storms JEA, Temme, AJAM, van

1026 Balen RT, van De Wiel MJ, van Gorp W, Viveen W, Westaway R, Whittaker AC, 2017. Two decades of

1027 numerical modelling to understand long term fluvial archives: advances and future perspectives.

1028 Quaternary Science Reviews, **166**, 177-187.

1029 Viveen W, Schoorl JM, Veldkamp A, van Balen RT. 2014. Modelling the impact of regional uplift and

1030 local tectonics on fluvial terrace preservation. *Geomorphology* **210**: 119-135.

1031 Von Blanckenburg F. 2005. The control mechanisms of erosion and weathering at basin scale from

1032 cosmogenic nuclides in river sediment. *Earth and Planetary Science Letters* **237**: 462-479.

1033 Wagner T, Fabel D, Fiebig M, Häuselmann P, Sahy D, Xu S, Stüwe K. 2010. Young uplift in the non-

1034 glaciated parts of the Eastern Alps. *Earth and Planetary Science Letters* **295**: 159-169.

1035 Wainwright, J., 2006. Degrees of separation: hillslope-channel coupling and the limits of

1036 palaeohydrological reconstruction. *Catena*, **66**, 93-106.

1037 Wainwright J. 2007. Can modelling enable us to understand the rôle of humans in landscape

1038 evolution? *Geoforum* **39**: 659-674. Doi: 10.1016/j.geoforum.2006.09.011.

1039 Wainwright, J. and Mulligan, M. eds., 2005. *Environmental modelling: finding simplicity in*

1040 *complexity*. John Wiley & Sons.

1041 Weissmann, G.S., Hartley, A.J., Scuderi, L.A., Nichols, G.J., Owen, A., Wright, S., Felicia, A.L., Holland,

1042 F., Anaya, F.M.L., 2015. Fluvial geomorphic elements in modern sedimentary basins and their

1043 potential preservation in the rock record: A review. *Geomorphology* **250**, 187-219.

1044 Westaway R, Bridgland DR. 2010. Causes, consequences and chronology of large-magnitude

1045 palaeoflows in Middle and Late Pleistocene river systems of northwest Europe. *Earth Surface*

1046 *Processes and Landforms* **35**: 1071-1094.
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3

4

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6

7

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9

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45

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48

49

50

51

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54

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56

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58

59

60

1047 Westaway R, Bridgland DR. 2011. Reply to comments by FS Busschers, KM Cohen, J. Vandenberghe,

1048 RT Van Balen, C. Kasse, J. Wallinga, and HJT Weerts on 'Causes, consequences and chronology of

1049 large-magnitude palaeoflows in Middle and Late Pleistocene river systems of northwest Europe', by

1050 Rob Westaway and David R. Bridgland (2010). *Earth Surface Processes and Landforms* **36**: 1841-

1051 1846.

1052 Whipple, K.X. and Tucker, G.E., 1999. Dynamics of the stream-power river incision model:

1053 Implications for height limits of mountain ranges, landscape response timescales, and research

1054 needs. *Journal of Geophysical Research: Solid Earth*, **104**(B8), 17661-17674.

1055 Whittaker A, Boulton S. 2012. Tectonic and climatic controls on knickpoint retreat rates and

1056 landscape response times. *Journal of Geophysical Research* **117**: F02024, doi:10.1029/2011JF002157.

1057 Whittaker A, Cowie P, Attal M, Tucker G, Roberts G. 2007. Contrasting transient and steady-state

1058 rivers crossing active normal faults: New field observations from the central Apennines, Italy. *Basin*

1059 *Research* **19**: 529-556.

1060 Whittaker A, Attal M, Cowie P, Tucker G, Roberts G. 2008. Decoding temporal and spatial patterns of

1061 fault uplift using transient river long profiles. *Geomorphology* **100**: 506-526.

1062 Whittaker AC, Attal M, Allen PA. 2010. Characterising the origin, nature and fate of sediment

1063 exported from catchments perturbed by active tectonics. *Basin Research* **22**: 809-828.

1064 Willet SD, Fisher D, Fuller C, En-Cho Y, Chia-Yu L. 2003. Erosion rates and orogenic wedge kinematics

1065 in Taiwan inferred from fission-track thermochronology. *Geology* **31**: 945-948.

1066 Willenbring JK, Gasparini NM, Crosby BT, Brocard G. 2013. What does a mean mean? The temporal

1067 evolution of detrital cosmogenic denudation rates in a transient landscape. *Geology* **41**: 1215-1218.

1068 Wittmann H, Von Blanckenburg F, Guyot JL, Maurice L, Kubik PW. 2009. From source to sink:

1069 Preserving the cosmogenic¹⁰Be-derived denudation rate signal of the Bolivian Andes in sediment of

1070 the Beni and Mamoré foreland basins. *Earth and Planetary Science Letters* **288**: 463-474.

1071 Yanites BJ, Tucker GE, Anderson RS. 2009. Numerical and analytical models of cosmogenic

1072 radionuclide dynamics in landslide-dominated drainage basins. *Journal of Geophysical Research:*

1073 *Earth Surface* **114**(F1).

1074 Zhu S, Wu Z, Zhao X, Li J, Xiao K. 2014. Ages and genesis of terrace flights in the middle reaches of

1075 the Yarlung Zangbo River, Tibetan Plateau, China. *Boreas* **43**: 485-504.
<table><caption>Table 1: Comparison of areas with sedimentary records where the study focus is usually on climate and anthropogenic forcing of fluvial landscape dynamics, and the more erosional and morphological records which are often more focussed on tectonic forcing.</caption><thead><tr><th></th><th>Focus on climatic (+ anthropogenic) forcing</th><th>Focus on tectonic forcing, crustal movements and surface deformation</th></tr></thead><tbody><tr><td>Characteristics of the fluvial response</td><td>Drainage system more likely to respond ubiquitously because when climate change imposes variations in hillslope sediment delivery or discharge, this affects a drainage network everywhere at once, if systems are buffered to this forcing</td><td>Drainage system response dependent on nature of forcing. May be highly localised (e.g. surface deformation from faulting) or more regional (e.g. regional uplift), with impacts propagating over time, for instance upstream of an active fault. Longer-term sediment flux histories as a result of tectonically-driven surface uplift or exhumation</td></tr><tr><td>Field data most commonly used for reconstructions</td><td>Aggradation and sedimentary data</td><td>Erosional and morphological data</td></tr><tr><td>Typical data set characteristics</td><td>Numerous and large data sets (number of observations, ages) available (e.g. Macklin et al., 2012a,b)</td><td>Fewer data sets, often made of small numbers of data (e.g. Demoulin et al., 2015)</td></tr><tr><td>Scale of vertical evolution</td><td>Decimetre- to metre</td><td>Decimetre</td></tr><tr><td>Typical time step in models</td><td>10<sup>-2</sup> to 10<sup>2</sup> years (days to centuries)</td><td>10<sup>0</sup> to 10<sup>4</sup> years (years to tens of thousands of years)</td></tr><tr><td>Models commonly used (non-exhaustive list)</td><td>CAESAR, LAPSUS, WATEM, CybErosion, PARALLEM (Coulthard et al., 2005; Schoorl et al., 2014; Wainwright, 2007)</td><td>CHILD, CASCADE, SSTRIM (e.g. Anthony and Granger, 2007b; Berlin and Anderson, 2007; Tucker, 2009)</td></tr><tr><td>External forcing data required for model input</td><td>Regional (or otherwise appropriate) climate time-series (temperature, precipitation). Time-series of human impacts (e.g. land clearance)</td><td>Regional uplift rates. Fault slip rates.</td></tr><tr><td>Means of evaluating / comparing the field and model data</td><td>Palaeoenvironmental data compilations + reconstructions of paleoclimatic variability and human-impact inferred from it (e.g. Benito et al., 2015; Lewis et al, 2001; Viveen et al., 2014).<br>Downstream data</td><td>Structural geological data, palaeoseismicity investigations (fault history)<br>Erosional morphological features (e.g. Beckers et al., 2014; Cohen et al., 2008; Whittaker and Boulton, 2012)<br>Downstream data</td></tr></tbody></table>
<table><caption>Table 2: Seven potential field data types that can be used to improve field-model pattern comparison.</caption><thead><tr><td>Item</td><td>Field data</td><td>Model data</td><td>References</td><td>Field data and model output requirements for effective comparison</td><td>Quantification of fit between data?</td></tr></thead><tbody><tr><td>1</td><td>Estimated volume of observed sediments</td><td>Modelled volume of sediments</td><td>Viveen et al. (2014), Figure 3a; Coulthard et al. (2005), Figure 3b</td><td><ul><li>Spatial definition of area of key sediment body (e.g. fan)</li><li>Borehole records enabling extrapolation of sediment volumes</li><li>Modelled estimate of sediment volumes within specified area</li></ul></td><td>Yes – compare volumes</td></tr><tr><td>2</td><td>Palaeo-hydrological discharge estimates</td><td>Modelled discharge estimates</td><td>Mather and Stokes (2016); Stokes et al. (2012); Van Balen et al. (2010); Westaway &amp; Bridgland (2010, 2011); Busschers et al. (2011)</td><td><ul><li>Specification of location of discharge estimates within system</li><li>Measurements of key channel parameters</li><li>Multiple methods of estimating palaeohydrological discharges</li><li>Modern discharge data from catchment</li><li>Modelled discharges using a comparable methodology</li></ul></td><td>Yes – compare discharge estimates</td></tr><tr><td>3</td><td>Observed borehole sequences</td><td>'Synthetic' boreholes</td><td>Newly proposed</td><td><ul><li>Borehole / section log data with age control to define time periods for comparability</li><li>Modelled time-series data for a spatial location corresponding to the observed sequences</li><li>Algorithm for aggregating erosion and deposition to generate 'synthetic' borehole record</li></ul></td><td>Possibly – depends on details of synthetic borehole algorithm</td></tr><tr><td>4</td><td>Observed river aggradation and incision phases</td><td>Modelled river aggradation and incision phases</td><td>Coulthard et al. (2005), Figure 3</td><td><ul><li>Robustly defined and dated aggradation and incision phases using meta-analysis protocols</li><li>Aggradation / incision time series for a comparable area (catchment / region)</li></ul></td><td>Possibly – depends on chronology and comparability of aggradation data</td></tr><tr><td>5</td><td>Planform characteristics of palaeochannels</td><td>Planform characteristics of river channels</td><td>Nicholas (2013)</td><td><ul><li>Measured wavelengths and widths of modern channels and palaeochannels</li><li>Measured wavelengths and widths of modelled channels</li></ul></td><td>Yes – compare wavelengths</td></tr><tr><td>6</td><td>Measured long profile</td><td>Modelled long profile</td><td>Stange et al. (2014), Figure 4; Veldkamp et al., (2016)</td><td><ul><li>Quantitative measures of modern long profile shape / gradient</li><li>Reconstructed gradients of terrace bodies</li><li>Measures of convergence / divergence / parallelism</li><li>Modelled long profile shape / gradient</li></ul></td><td>No</td></tr><tr><td>7</td><td>Knickpoint mapping / TCN dating</td><td>Knickpoint propagation rates</td><td>Mather et al. (2002); Stokes et al. (2002); Yanites et al. (2010); Crosby and Whipple (2006)</td><td><ul><li>Knickpoint mapping</li><li>TCN dating of erosion waves across catchment</li><li>Modelled knickpoint propagation rates</li></ul></td><td>Possibly but assumptions behind field data</td></tr><tr><td>8</td><td>Cosmogenic-based catchment denudation rates</td><td>Modelled denudation rates</td><td>Yanites et al. (2009); Willenbring et al. (2013), Figure 5</td><td><ul><li>Samples for cosmogenic-based denudation rates in appropriate and well-defined spatial locations</li><li>Directly comparable model output covering the same catchment area</li></ul></td><td>Yes if directly comparable</td></tr></tbody></table>
<table><thead><tr><th>Feature to be investigated</th><th>What catchment activity does it constrain?</th><th>What time-scale is being addressed?</th><th>What techniques can be used?</th><th>Example from literature</th><th>Modelled comparison feature</th></tr></thead><tbody><tr><td>In-cave-deposited alluvium</td><td>Incision episode</td><td>&gt;300,000 years BP</td><td>TCN burial dating (26Al/10Be): rapid and complete burial</td><td>Granger et al., 2001; Anthony &amp; Granger, 2007a; Wagner et al., 2010</td><td>Incisional event</td></tr><tr><td rowspan="3">Fluvial deposit, e.g. accumulation terrace or fan unit</td><td rowspan="3">Aggradational event</td><td>&gt;300,000 years BP</td><td>TCN burial dating (26Al/10Be): isochron method</td><td>Balco &amp; Rovey, 2008; Erlanger et al., 2012; Balco et al., 2013</td><td>Sediment body</td></tr><tr><td>&gt;300,000 years BP</td><td>IRSL - pIRIR290</td><td>Cordier et al., 2012; 2014</td><td>Sediment body</td></tr><tr><td>&gt;300,000 years BP</td><td>ESR dating in quartz</td><td>Chaussé et al., 2004; Bahain et al., 2007; Zhu et al., 2014</td><td>Sediment body</td></tr><tr><td rowspan="4">Fluvial deposit, e.g. accumulation terrace or fan unit</td><td rowspan="2">Abandonment time of the landform; onset of incision</td><td rowspan="2">30,000 – 300,000 years BP</td><td rowspan="2">TCN surface exposure dating: concentration depth profile</td><td rowspan="2">Anderson et al., 1996; Brocard et al., 2003; Rixhon et al., 2011</td><td rowspan="2">River incision - both temporal (e.g. chronological control on a terrace staircase) AND spatial (e.g. propagation of an upstream erosion wave - knickpoint diffusion)</td></tr><tr><td></td></tr><tr><td>Aggradational event</td><td>30,000 – 300,000 years BP</td><td>OSL and IRSL - pIRIR290, SAR, sample dependent</td><td>Briant et al., 2006; Cordier et al., 2014; Kars et al., 2012</td><td>Sediment body</td></tr><tr><td>Period of stasis</td><td>30,000 – 300,000 years BP</td><td>U-Series dating of carbonate cements</td><td>Sharp et al., 2003; Adamson et al., 2014</td><td>Surface</td></tr><tr><td>Period of incision</td><td>30,000 – 300,000 years BP</td><td>U-Series dating of travertines</td><td>Veldkamp et al., 2004</td><td>River incision timing and rate</td></tr><tr><td>Fluvial deposit, e.g. accumulation terrace or fan unit</td><td>Aggradational event</td><td>10,000-30,000 years BP</td><td>OSL - SAR</td><td>Choi et al., 2007; Kock et al., 2009</td><td>Sediment body</td></tr><tr><td>Fluvial deposit, e.g. channel within terrace form or floodplain</td><td>Aggradational event incorporating material previously deposited on land surface</td><td>10,000-30,000 years BP - N.B. potential for contamination in samples at the limit of the technique</td><td>Radiocarbon - measurements should be on charcoal, bone or identified seeds and shells only, using appropriate pretreatments</td><td>Briant and Bateman, 2009; Briant et al., 2018; Higham et al., 2006; Bird et al., 1999; Busschers et al., 2014</td><td>Sediment body</td></tr></tbody></table>
<table><caption>Table 3: Table suggesting which chronological techniques are most appropriate for each timescale and event type.</caption><tr><td>Fluvial deposit, e.g. channel within terrace form or floodplain</td><td>Aggradational event incorporating material previously deposited on land surface</td><td>&lt;10,000 years BP</td><td>Radiocarbon – constraining various different events and determining ‘change after’ dates for meta-analysis</td><td>Jones et al., 2015</td><td>Sediment body</td></tr><tr><td>Fluvial deposit, e.g. channel within terrace form or floodplain</td><td>Aggradational event</td><td>&lt;10,000 years BP</td><td>OSL - SAR</td><td>Schulte et al., 2008; Pierce et al., 2011</td><td>Sediment body</td></tr><tr><td>Erosional landform, e.g. strath terrace</td><td>Abandonment time of the landform; onset of incision</td><td>Holocene/Late Pleistocene</td><td>TCN surface exposure dating</td><td>Burbank et al., 1996; Reusser et al., 2004; Seong et al., 2008</td><td>Incisional event and knickpoint diffusion rate in actively uplifting orogens.</td></tr><tr><td>Modern river bedload, sediments of dated terraces, and recycling of sediments in floodplains</td><td>Catchment-scale (palaeo) denudation rate</td><td>1,000 – 1,000,000 years</td><td>TCN concentration (usually <sup>10</sup>Be and/or <sup>26</sup>Al)</td><td>Von Blanckenburg, 2005; Wittmann et al., 2009</td><td>Denudation rates</td></tr><tr><td>Valley walls in orogens or basin strata</td><td>Large-scale (orogen or basin-wide) denudation rate</td><td>Meso-Cenozoic, including Quaternary</td><td>Low-temperature thermochronology</td><td>Willett et al., 2003; Valla et al., 2011</td><td>Denudation rates</td></tr></table>
**Figure 1** – Flow chart for applying Pattern Oriented Modelling (POM) and Pattern Oriented Sampling (POS) within a joint field-model investigation of a specific catchment.

**Figure 2** - The type of records that should be sampled in a Pattern Oriented Sampling approach if occurring in the investigated research area.

**Figure 3a** – mapped and simulated sediment thicknesses for terrace levels from the northwest Iberian lower Miño River basin redrawn from Viveen et al. (2014), Figures 10-12. Median = dot, minima and maxima are given by the error bars. Three scenarios were modelled, as shown. The authors argue that model Scenario 3 matches the mapped sediment thicknesses most closely.

**Figure 3b** – comparison of modelled sediment yield from four upland catchments in northern England with sediment preservation as recorded by the frequency of radiocarbon dated units in these catchments. Redrawn from Figure 15 of Coulthard et al. (2005).

**Figure 4** – a) observed and mapped sub-parallel long profiles of both the present day Segre River (solid line), Pleistocene terrace remnants (coloured boxes) and Pliocene-Quaternary pedimentation surfaces (labelled). b) ‘best-match’ model output from the only one of four modelled scenarios which shows sub-parallel development of terraces. This scenario is continuous Quaternary uplift and climate variability. Redrawn from Stange et al. (2014), Figures 4 and 12.

**Figure 5** – plot comparing nested basin-wide erosion rates and relict proportion of nested watershed upstream of each sample site for nine sampling locations within the South Fork Eel River catchment. Solid lines show sampling locations 0-20 km upstream of the catchment outflow; dashed lines show locations 20-60 km upstream and dotted lines 60-100 km upstream. Black dots show measured detrital $10Be$ denudation rates and their close match with part of the modelled curves is given as evidence of the usefulness of the model across the full range of the curves. Redrawn from Figure 1G of Willenbring et al. (2013).
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<figure><img src="image_4.png" /><figcaption>Figure 1 – Flow chart for applying Pattern Oriented Modelling (POM) and Pattern Oriented Sampling (POS) within a joint field-model investigation of a specific catchment.</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>Figure 2 - The type of records that should be sampled in a Pattern Oriented Sampling approach if occurring in the investigated research area.</figcaption></figure>

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### SCENARIO 1 – Regional uplift
North bank data

<figure><img src="image_6.png" /></figure>

### SCENARIO 2 – Basin subsidence
North bank data

<figure><img src="image_7.png" /></figure>

### SCENARIO 3 – Combined scenarios 1 and 2 and localised differential uplift
a) North bank data

<figure><img src="image_8.png" /></figure>

b) South bank data

<figure><img src="image_9.png" /></figure>

Figure 3a – mapped and simulated sediment thicknesses for terrace levels from the northwest Iberian lower Miño River basin redrawn from Viveen et al. (2014), Figures 10-12. Median = dot, minima and maxima are given by the error bars. Three scenarios were modelled, as shown. The authors argue that model Scenario 3 matches the mapped sediment thicknesses most closely.

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<figure><img src="image_10.png" /><figcaption>Figure 3b –comparison of modelled sediment yield from four upland catchments in northern England with sediment preservation as recorded by the frequency of radiocarbon dated units in these catchments.
Redrawn from Figure 15 of Coulthard et al. (2005).</figcaption></figure>

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<figure><img src="image_11.png" /><figcaption>Figure 4 – a) observed and mapped sub-parallel long profiles of both the present day Segre River (solid line), Pleistocene terrace remnants (coloured boxes) and Pliocene-Quaternary pedimentation surfaces (labelled). b) ‘best-match’ model output from the only one of four modelled scenarios which shows sub-parallel development of terraces. This scenario is continuous Quaternary uplift and climate variability. Redrawn from Stange et al. (2014), Figures 4 and 12.</figcaption></figure>

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<figure><img src="image_12.png" /></figure>

Figure 5 – plot comparing nested basin-wide erosion rates and relict proportion of nested watershed upstream of each sample site for nine sampling locations within the South Fork Eel River catchment. Solid lines show sampling locations 0-20 km upstream of the catchment outflow; dashed lines locations 20-60 km upstream and dotted lines 60-100 km upstream. Black dots show measured detrital 10Be denudation rates and their close match with part of the modelled curves is given as evidence of the usefulness of the model across the full range of the curves. Redrawn from Figure 1G of Willenbring et al. (2013).

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