NE Peloponnese, Greece

Assessing Geomorphic Processes and their Potential Relationship with Archaeological Artifact Exposure – NE Peloponnese, Greece

Pamela Elizabeth Tetford

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Geography University of Toronto

Assessing Geomorphic Processes and their Potential Relationship with Archaeological Artifact Exposure – NE Peloponnese, Greece

Pamela Elizabeth Tetford

Master of Science

Department of Geography University of Toronto

2017

Abstract

Landscape change can be important when encountering historical artifacts. Interactions between topography, climate and human activity shape a landscape, making sediment deposits from surface erosion and fluvial transport, and the artifacts they contain, important archives. There is a potential relationship between the rate of geomorphic processes and surface artifact density. This study compares spatially variable estimates of soil loss and stream energy, as indicators of high geomorphic activity, to surface artifact finds of the Western Argolid Regional Project (WARP).

Processes within the Inachos River watershed in the northeast Peloponnese, Greece, are quantified using the Unit Stream Power Erosion Deposition method (USPED) and the specific stream power approach in a Geographic Information Systems (GIS) environment. A statistically significant association is identified between surface erosion and artifact density, with the lowest artifact densities associated with the highest rates of soil loss. Knowledgeable interpretation of artifact distribution enables more accurate reconstruction of human settlement history.

Acknowledgements

I would first like to thank my supervisor, Dr. Joseph Desloges, for his invaluable input over the past year and a half. Ongoing discussions surrounding various approaches and techniques within the field of fluvial geomorphology have challenged me and helped me develop a much stronger understanding of the discipline. His suggestions and critiques of various draft sections of this thesis have led to a much improved final version. I am also greatly appreciative for his assistance in the field, and for navigating the mountainous roads (goat trails?) of the Inachos River watershed with unparalleled confidence. His generous funding of this project has provided me with an extraordinary learning opportunity.

I would also like to thank my committee members, Drs. Joseph Desloges, George Arhonditsis and Dimitri Nakassis for providing valuable comments and suggestions that improve upon the final version of this thesis.

I also offer thanks to Tassos Venetikidis for his assistance with the translation of Greek documents and discussion regarding the tectonic activity of the northeast Peloponnese region.

I also extend my appreciation to the WARP team, for their generous hospitality during fieldwork in Greece. I thank Drs. Sarah James, Scott Gallimore and William Caraher so much for the knowledge they shared regarding the rich history and culture of the Peloponnese region, and taking the time to explain the theory and practices regarding archaeological survey methods. And to Dr. Dimitri Nakassis, I can’t begin to thank him enough for the incredible insight he provided into Greek history and culture, and his tireless assistance in the quest to find the elusive data necessary to make the completion of this project possible! His determination to find sieves and lab space to complete soil analysis was remarkable. I thank the British School of Athens for providing those laboratory facilities.

Lastly, I wish to thank my family. The collective patience and understanding that they have all shown has allowed me to devote the time necessary to complete this project. Particularly to my husband, Murray, and my daughter, Sydney, who have picked up the slack at home while “mom” was working away at the computer, and without whose support this would not have been possible…thank you so much!

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Table of Contents

Acknowledgements ...... iii

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... viii

List of Appendices ...... ix

Chapter 1: Introduction ...... 1

1.1. Conceptual Understanding ...... 1

1.2. Defining the Problem ...... 2

1.3. Research Objectives ...... 4

Chapter 2: Theoretical Background ...... 5

2.1. Variable Soil Loss as an Assessment of Hillslope Stability ...... 5 2.1.1. Evolution of the Revised Universal Soil Loss Equation ...... 5 2.1.2. The RUSLE in a GIS Environment ...... 6 2.1.2.1. The Rainfall Erosivity Factor ...... 8 2.1.2.2. The Soil Erodibility Factor...... 10 2.1.2.3. Slope Length and Slope Steepness Factors ...... 11 2.1.2.4. The Vegetation Cover Factor ...... 12 2.1.2.5. The Land Management Practices/Conservation Factor ...... 13

2.2. Stream Power as an Indicator of Geomorphic Processes ...... 13 2.2.1. Downstream Discharge ...... 14 2.2.2. Bankfull Width ...... 15 2.2.3. Channel Gradient ...... 15

2.3. The Relationship between Geomorphic Processes and Artifact Exposure ...... 16

Chapter 3: Inachos River Watershed ...... 18

3.1. Location ...... 18

3.2. Climate and Hydrology ...... 19

3.3. Physiography ...... 21 iv

3.4. Current Land Use...... 22

3.5. Settlement History...... 22

Chapter 4: Methods ...... 25

4.1. Fieldwork ...... 25

4.2. Soil Analysis ...... 26

4.3. Delineation of the Watershed ...... 26

4.4. RUSLE Calculation ...... 26 4.4.1. Computation of the Rainfall Erosivity Factor, R...... 28 4.4.2. Computation of the Soil Erodibility Factor, K ...... 28 4.4.3. Computation of the Slope Length and Steepness Factor, LS ...... 28 4.4.4. Computation of the Cover Management Factor, C...... 28 4.4.5. Computation of the Support Practice Factor, P ...... 29

4.5. Specific Stream Power Calculation ...... 29 4.5.1. Determination of Slope ...... 32 4.5.2. Determination of Discharge and Width Relations...... 32 4.5.3. Mapping Specific Stream Power ...... 32

4.6. Comparing RUSLE to Artifact Density Distribution ...... 33

Chapter 5: Results ...... 35

5.1. Estimating Potential Soil Surface Erosion and Hillslope Surface Stability ...... 35 5.1.1. The R Factor (rainfall erosivity) ...... 35 5.1.2. The K Factor (soil erodibility) ...... 37 5.1.3. The LS Factor (slope length-steepness) ...... 39 5.1.4. The C Factor (land cover index) ...... 39 5.1.5. The P Factor (land management) ...... 40 5.1.6. The RUSLE ...... 40

5.2. Estimating Fluvial Geomorphic Processes ...... 41 5.2.1. Modern Bed Sediment of the Inachos River ...... 41 5.2.2. Bank Stratigraphy of the Inachos River ...... 42 5.2.3. Estimating Specific Stream Power ...... 44 5.2.3.1. Drainage Area-Discharge and Width Proxies ...... 44

Table of Contents 5.2.3.2. Longitudinal Profile ...... 45 5.2.3.3. Spatial Distribution of Specific Stream Power ...... 46

5.3. Assessing the Spatial Distribution of Geomorphic Processes ...... 47 5.3.1. Comparing the USPED to Artifact Distribution ...... 47 5.3.2. Regression of Density and Soil Loss using Continuous Data ...... 49 5.3.3. Analysis of Density and Soil Loss using Categorical Data ...... 50 5.3.4. Specific Stream Power Distribution Related to Artifact Density ...... 53

Chapter 6: Discussion ...... 55

6.1. Geomorphic Processes of the Upper Watershed ...... 56

6.2. Mid-Elevation Geomorphic Processes ...... 57

6.3. Geomorphic Processes of the Argolid Floodplain ...... 59

6.4. Geomorphic Processes within the WARP Survey Polygon ...... 59

Chapter 7: Conclusions ...... 62

7.1. Summary of Findings ...... 62

7.2. Future Research ...... 64

References ...... 65

Appendices ...... 70

List of Tables

Table 3.1. - The chronology of cultural periods, occupation, land use and landscape .. 23

Table 5.1. - Descriptive statistics for the USPED and five factor components ...... 40

Table 5.2. - Descriptive statistics for artifact density, RUSLE and factors...... 47

Table 5.3. - Regression results of LN USPED and LN (Density +1) ...... 49

Table 5.4. - Fisher Least Significant Difference for categorized USPED ...... 51

Table 5.5. - Fisher Least Significant Difference for categorized USPED > 1 t ha-1 a-1 .. 53

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List of Figures

Figure 3.1. - Location of the Inachos River watershed ...... 19

Figure 3.2. - Mean monthly precipitation values for hydrograph stations ...... 20

Figure 4.1. - Location of study sites within the Inachos River watershed ...... 25

Figure 4.2. - Flowchart of RUSLE modelling process ...... 27

Figure 4.3. - Flowchart of the specific stream power modelling process ...... 31

Figure 4.4. - Artifact density per survey tract for the archaeological study polygon...... 33

Figure 5.1. - The RUSLE and individual factors as raster layers ...... 36

Figure 5.2. - Particle size distribution of small grain sediment (D < 2 mm) ...... 38

Figure 5.3. - River bank exposure (Site R5, south bank) ...... 42

Figure 5.4. - River bank exposure (Site R8, south bank) ...... 43

Figure 5.5. - Scour gullies into red-bed formations (Site R9) ...... 43

Figure 5.6. - Bankfull discharge and bankfull width proxies ...... 44

Figure 5.7. - Drainage area proxy models for bankfull width and bankfull discharge .... 45

Figure 5.8. - Spatial variability of specific stream power ...... 46

Figure 5.9. - Mean values of USPED and factors per survey tract ...... 48

Figure 5.10. - Scatterplot for LN (Density + 1) and LN USPED...... 50

Figure 5.11. - Results of one-way ANOVA for categorical USPED...... 51

Figure 5.12. - Results of one-way ANOVA for categorical USPED (A > 1.0 t ha-1 a-1) .. 52

Figure 5.13. - Spatial variability of specific stream power within the survey area ...... 54

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List of Appendices

Appendix A. Rainfall Erosivity, R factor ...... 70

Appendix B. Soil Erodibility, K factor ...... 70

Appendix C. Vegetative Cover, C factor ...... 72

Appendix D. Rapid Field Surveys ...... 73

Appendix E. Summary Statistics for Specific Stream Power ...... 74

Appendix F. Regression of USPED against Artifact Density...... 75

Chapter 1: Introduction 1.1. Conceptual Understanding

Understanding a changing landscape, and the processes driving that change, can be important when considering the distribution of artifact finds (Fuchs, Lang & Wagner, 2004). Sediment erosion and transport are critical factors in the preservation and discovery of surface artifacts (Bevan & Conolly, 2009). The assessment of surface soil stability in survey areas assists understanding of the complex interface between human activity, surface processes, and artifact location (Gouma, van Wijnigaarden & Soetens, 2011). Better interpretation of the archaeological record and artifact dispersion enables greater accuracy in the reconstruction of human settlement history (Zananiri, Hademenos & Piteros, 2010).

Landscape form and composition are the consequence of complex interactions between topography, climate and human activity (van Andel, Zangger & Demitrack, 2010). These interactions influence processes that detach sediment, which is transported primarily by water and deposited downslope, often with large particles deposited first, then aggregates and fine particles further downslope (Garcia Rodriguez & Gimenez Suarez, 2012). The resulting sediment deposits are important archives of past events (van Andel et al., 2010). Regions of climate variability and/or extensive anthropogenic occupation are particularly vulnerable to soil erosion and subsequent sediment redistribution (Fuchs, 2007). In the Mediterranean, accelerated soil erosion and alluviation associated with early human exploitation has left ancient towns buried and has been well documented (van Andel et al., 2010). In this region, geomorphic processes often condition archaeological deposits by removing entire deposits, burying sites, or selectively transporting exposed artifacts (Byers, Hargiss & Byrd Finley, 2015). Geomorphic processes play a fundamental role in the distribution and exposure of archaeological finds (Gouma et al., 2011). Consequently, it has been suggested that there is a potentially strong relationship between the rate and intensity of geomorphic processes and site stability where relics from human settlement are exposed (Gouma et al., 2011).

1.2. Defining the Problem

Geomorphic investigation can offer information regarding a landscape’s morphology, and the interaction of surface processes, over a period of time (Mexia, 2015). To elucidate the mechanisms associated with long-term landscape evolution, and the behavior of earth’s materials, often requires the use of process studies from which conclusions regarding geomorphic change can be inferred (Trenhaile, 2010, p. 11). Soil erosion has been a dominant factor in the redistribution of sediment in the Mediterranean region since the inception of agriculture during the Neolithic period, with the impact of human activity intensifying the main natural physical forces such as rainfall impact, flowing water, wind, ice, temperature change, and gravity (Fuchs et al., 2004). When the rate of soil loss exceeds the rate at which soil formation may occur, land degradation will alter the physical landscape (Panagos et al., 2015b). To this end, the quantification of soil loss provides an indication of the spatial variability of potential landscape alteration occurring, while accounting for dynamic processes that entrain and transport materials across earth’s surface (Panagos et al., 2015b; Demirci & Karaburum, 2012).

Research quantifying the relationship between geomorphic work and archaeological artifact exposure is limited (Gouma et al., 2011). Bevan & Connolly (2009) argue that archaeology has been relatively slow to develop quantitative methods to examine spatially variable information regarding human deposits. However, spatial modeling can increase understanding of field survey data when developing hypotheses regarding human activities based on artifacts left behind (Bevan & Conolly, 2009). This study quantifies the relationship between soil loss and archaeological artifact exposure by examining the intensity and spatial variability of geomorphic activity in the artifact rich Argolid region of southern Greece. This semi-arid Mediterranean region is vulnerable to erosion (Garcia Rodriguez & Gimenez Suarez, 2012). Hot, dry summers and shoulder seasons, followed by intense winter rainfall, often produce rill erosion and gullying on steep, sparsely vegetated slopes (Ferreira & Panagopolous, 2014). An extensive history of human activity in the region has accelerated soil erosion beyond which would have been expected naturally (Elhag, 2015). This historically significant area contains a concentrated archive of surface artifacts dating to the Early Neolithic period (Fuchs, 2007).

This study identifies areas of more intense geomorphic activity (soil erosion) and compares them to the distribution of surface artifact finds in the Inachos River watershed of southern Greece. Surface soil stability is modelled, using Geographic Information Systems (GIS) and the Unit Stream Power Erosion and Deposition (USPED) approach to the Revised Universal Soil Loss Equation (RUSLE), to represent the 3-dimensional complexity of topographic processes occurring from the watershed boundary to the overland convergence that forms the main Inachos River channel. This RUSLE model has proven to effectively estimate erosion and soil loss in the semi-arid southern region of Greece (Kouli, Soupios & Vallianatos, 2009). Model inputs include a rainfall-runoff erosivity factor derived from historical precipitation records, a soil erodibility factor determined by soil sampling and soil maps, a slope length and slope steepness factor extracted from a 5 m Digital Elevation Model (DEM), and a vegetation cover factor and soil conservation practices factor derived from WorldView-3 remote sensing imagery. The RUSLE model presented here is coupled with a stream power model to address processes occurring within the main river channel. This coupling is a way of exploring the potential connections between hillslope derived material and sediment movement in the river valley. Specific stream power is modelled to assess the intensity and variability of erosion occurring within the Inachos River channel from fluvial energy. Specific stream power has been shown to effectively indicate fluvial geomorphic work (Phillips & Desloges, 2014). Model inputs include channel slope extracted from the 5 m DEM, and channel discharge and width estimated from rapid geomorphic field surveys, remote imagery and generally accepted discharge-drainage area relationships.

Modelled hillslope stability and stream channel energy are compared to artifact density records obtained from a spatially intensive, systematic fieldwalk survey conducted by the Western Argolid Regional Project (WARP). The WARP survey identifies and records surface artifact yield over a 30 km2 area of the central Inachos River watershed (WARP, 2015).

A GIS environment is used to calculate and efficiently display each element of this research on a 5 m x 5 m basis. The approach is to effectively illustrate the intensity and spatial variability of potentially high geomorphic activity and artifact yield within the Inachos River basin. The goal is to identify the spatially variability in these linkages with the aim of helping understand archaeological artifact exposure histories.

1.3. Research Objectives

The objective of this research project is to use generally accepted modelling techniques to demonstrate the spatial variation in soil erosion and hillslope stability, and stream energy. By doing so, this study aims to address the following questions:

1. What watershed attributes contribute the most to soil surface erosion and variations of hillslope stability in the Inachos River watershed, a mountainous Mediterranean region vulnerable to intense geomorphic processes? What are the potential soil erosion rates since occupation and how are they distributed?

2. Do the fluvial geomorphic processes of the Inachos River contribute to the erosion and transport of sediment in the watershed and how are they connected to adjacent hillslope inputs of soil and sediment?

3. Is there a relationship between the rate and intensity of geomorphic processes and site stability, and artifact distribution when artifacts from past human settlement are exposed in an artifact rich, Mediterranean region?

Chapter 2: Theoretical Background 2.1. Variable Soil Loss as an Assessment of Hillslope Stability

The erodibility of soil is described as the soil’s ability to resist mechanical separation and breakdown in its aggregate form (Hill & Schutt, 2000). As soil erodes, sediment is entrained, transported and deposited across a surface (Kouli et al., 2009). This is a natural process by physical forces (i.e. rain, wind, gravity, etc.) and can be accelerated by anthropogenic causes (Efthimiou, Lykouydi & Kavaritis, 2014). Regions with a Mediterranean climate are particularly vulnerable to soil loss due to erosion, with hot dry summer and shoulder seasons limiting vegetation growth followed by heavy erosive winter rainfall on steep, only partially vegetated slopes (Kouli et al., 2009). Erosion and land degradation in arid, semi-arid and sub-humid areas of the Mediterranean are consequences of climatic variation and human activities such as land clearance and agriculture (Poesen & Hooke, 1997). Landscapes of the Mediterranean have arguably suffered the most extensive degradation due to anthropogenic impact (Syvitski, 2003). It has been estimated that approximately 75% of the average sediment yield of a Mediterranean basin may be the result of human activity (Syvitski, 2003). Consequently, in an area with extensive human presence and a long agricultural history, such as the semi-arid northeast Peloponnese region of Greece, erosion is accelerated (van Andel et al., 2010), making examination of the downslope flux of sediment an appropriate diagnostic tool to predict land surface stability and geomorphic change (Tsara, Kosmas, Kirkby, Kosmas & Yassoglou, 2005).

2.1.1. Evolution of the Revised Universal Soil Loss Equation

Erosion prediction equations have been used since the mid 1940’s as a tool to assess soil erosion risk and predict soil erosion rates for various soils, land use, landscape characteristics and conservation practices (Spaeth Jr., Pierson Jr., Weltz, & Blackburn, 2003; Tsara et al., 2005). To predict long-term average soil loss (~20 years) due to sheet and rill erosion, Wischmeier and Smith (1965, 1978) developed the Universal Soil Loss Equation (USLE) to estimate long term average soil erosion rates as a conservation planning guide. Although scientifically sound, predictions using the USLE consistently produced higher values than observed values for ungrazed land (Spaeth et al., 2003). According to Renard, Foster, Weesies, and Porter (1991), the USLE did not explicitly represent the interaction of erosion processes. The USLE, as applied, did

5 not address soil deposition and gully erosion within a watershed, lacking the ability to account for deposition along hillslopes and in depressions and channels. Instead, it was designed for straight slope sections (Garcia Rodriguez & Gimenez Suarez, 2012).

Hydrology and erosion research advancements led to a revision of the USLE, the Revised Universal Soil Loss Equation (RUSLE), with indexed factors representing climate, soil, topography and land use, such that

A  R K L S C P (1) where A is the average annual soil loss (t ha-1 a-1), R is the rainfall erosivity factor, K is the soil erodibility factor, L is the slope length factor, S is the slope steepness factor, C is the vegetation/crop cover management factor, and P is the soil conservation support practice factor (Kinnell, 2015). The RUSLE retains the quantification of the USLE but incorporates a number of “adjustments” in determining the weighting of individual factors in the model (Kinnell, 2015).

Improvements incorporate a 10-year frequency storm erosivity index (EI10) in the R factor, modifications to the K factor to reflect moisture extraction by growing crops, new equations for the L and S factors to reflect rill and interrill erosion, subfactors for evaluating the C factor, and new conservation practice values for the P factor (Renard & Freimund, 1994). Changes to the computation of the LS factor account for concavity and convexity using segments of irregular slopes (Mitasova, Hofierka, Zlocha & Iverson, 1996). While the RUSLE does not explicitly account for processes of detachment, transport and deposition, it does reflect the net effects of these processes in the quantification of soil loss (Spaeth, Pierson, Weltz & Blackburn, 2003). However, the methodology of subdividing landscapes to account for concavity and convexity still does not fully consider flow convergence and divergence (Oliveira, 2013). Although better guidelines are imposed for variation of terrain by the incorporation of a two dimensional profile shape, manual calculation of the RUSLE does not represent the three dimensional planform effects on erosion at the catchment scale (Garcia Rodriguez & Gimenez Suarez, 2012).

2.1.2. The RUSLE in a GIS Environment

The concept of incorporating the effect of flow discharge and sediment concentration, through calibration based on observed data, has been explored using algorithms to represent convergence

6 and divergence (Oliveira et al., 2013). In the RUSLE 3D, the upstream contributing drainage area is substituted for slope length to incorporate the impact of flow convergence (Garcia Rodriquez & Gimenez Suarez, 2012). The adoption of a GIS environment has enabled a more explicit representation of the spatial variability of erosion and deposition processes with overland flow (Garcia Rodriquez & Gimenez Suarez, 2012). The RUSLE 3D takes into account the upstream contributing area, whereas the Unit Stream Power Erosion and Deposition (USPED) model is derived using the contribution area and flow accumulation as a representative for water flow in calculation of the LS factor (Oliveira et al., 2013). The USPED assumes that the capacity of water flow determines the amount of sediment transported, and divergence and convergence are expressed by the change in sediment flow (Garcia Rodriguez & Gimenez Suarez, 2012). This model has been proven to better replicate surface processes (Gouma et al., 2011). Using GIS allows integration of topographic factors to provide effective assessment of erosion and deposition at a landscape scale (Mitasova et al., 1996). The RUSLE, therefore, is the most widely accepted and frequently used empirical soil erosion model to assess water erosion, predicting long-term average annual soil loss for specified land management classifications (Gouma et al., 2011; Garcia Rodriguez & Gimenez Suarez, 2012). When examining potential soil loss due to erosion, a watershed provides a defined hydrologic and topographic unit over which the RUSLE can be applied (Elhag, 2015). Using the RUSLE, in conjunction with GIS (and the USPED), and remote sensing, allows quantitative representation of geomorphic processes at a regional scale (Gouma et al., 2011). Remote sensing enables detection of land use change with relative ease and accuracy, and the assignment of attribute values to unsampled areas (Ferreira & Panagopoulos, 2014). Cell by cell processing allows better detailed analysis of individual contributors to soil erosion, such as soil type, slope and land use and enables quantification of a single factor’s influence on the total soil loss (Demirci & Karaburun, 2012). The RUSLE, using the USPED approach, therefore, permits better systematic examination of the spatial variability and role of model components, with an LS factor that more closely resembles expected surface runoff (Garcia Rodriguez & Gimenez Suarez, 2012; Oliveira et al., 2013).

Alternative models of several varieties have emerged in recent decades to assess sediment yield (physical models, conceptual models and empirical models) that differ primarily in complexity, data requirements, watershed characteristics and end use; however, the USPED approach to the RUSLE is useful, conceptually simple, and has fewer data requirements (Ferreira & 7

Panagopoulos, 2014). The USPED has been successfully applied in Crete (Kouli et al., 2009), a similar Mediterranean region to this study.

2.1.2.1. The Rainfall Erosivity Factor

The R factor represents the capability of rainfall to result in erosion based on rainfall intensity and depth for a given period (Kouli et al., 2009). Rainfall intensity, and the amount that occurs at each level of intensity, is determined from recorded rainfall data (Renard & Freimund, 1994). An increase in rainfall intensity, and/or amount, is reflected by an increase in the R factor (Demirci & Karaburun, 2012). The R factor is the sum of individual annual storm erosivity index values, EI, averaged over long periods of time (>20 years). It is defined as

1 n m  R  (E)k (I30)k  (2) n j 1 k1 j

where E is the total storm kinetic energy, I30 is the maximum 30-minute rainfall intensity, j is the number of years used to produce the average, k is the number of storms in each year, n is the number of years averaged to obtain R, and m is the number of storms in each year (Renard & Freimund, 1994).

Lack of long term precipitation data in some areas can make the computation of R, and the subsequent RUSLE, difficult (Renard & Freimund, 1994). In the absence of detailed rainstorm data, mean annual and monthly rainfall have been shown to estimate the rainfall erosivity index (Arnoldus, 1980). This method uses a modified Fournier index such that

∑ = (3)

where F is the modified Fournier index value, pi is average monthly precipitation, and P is average annual precipitation (Arnoldus, 1980). The modified Fournier index has demonstrated strong linear correlation with mean annual rainfall for different European regions, even where precipitation varies seasonally (Kouli et al., 2009). Ferro, Giordano and Iovino (1999) replace the erosivity index with the modified Fournier index to propose an FF index such that

N 12 2 1 pi, j FF   (4) N j 1 i1 Pj

where pij is the monthly rainfall depth of the year j and P is the rainfall total for the same year and N is the number of years over which data were collected. Arnoldus (1980) states, however, that the modified Fournier index must be calibrated between climatic regions. A theoretically derived constant is employed as a coefficient of proportionality (Ferro et al., 1999). Ferro et al. (1999) established a relationship between the FF index and rainfall erosivity for Sicily, Italy such that

1.56 R  0.6120 FF (5)

where FF incorporates the modified Fournier index. Kouli et al. (2009) adopted the mean R values of the Italian and a Moroccan relationship to estimate rainfall erosivity for Northwestern Crete, Greece. Ferro et al. (1999) have demonstrated that hydrologically homogenous regions can be characterized by the same R to FF relationship.

Interpolation, using various techniques, is a common application used to model spatial heterogeneity of natural data using discrete point values derived from data obtained at sampled locations (Bevan & Conolly, 2009). This application has been used successfully to model rainfall erosivity, R, in the Mediterranean, providing a continuous dataset, in raster form, that can be used for visual and computational analysis (Kouli et al., 2009; Terranova, Coscarelli & Iaquinta, 2009). Inverse distance weighting (IDW) uses a linearly weighted function to derive the contribution of neighbouring measured values to be assigned to unmeasured locations (Bevan & Conolly, 2009). This method of interpolation assumes that the influence of a variable decreases with distance. Alternatively, ordinary kriging uses autocorrelation to examine the spatial structure of measured point values for trends and directional influences (Ferreira & Panagopoulos, 2014). Ordinary kriging is considered to be more sensitive to input data than IDW, which uses pre- defined arbitrary distances. Consequently, ordinary kriging is used extensively to describe spatially heterogeneous natural phenomena, producing accurate results (Bevan & Conolly, 2009). Cross validation compares residuals between modelled values and actual point values enabling selection of the most appropriate prediction model for spatial variations in R (Ferreira & Panagopoulos, 2014).

2.1.2.2. The Soil Erodibility Factor

The soil erodibility factor, K, is an empirical measure of soil erodibility determined by soil texture, organic matter, structure and permeability (Kouli et al., 2009). The K factor reflects the ability for soil to separate during rainfall splash or overland flow (Demirci & Karaburun, 2012). Wischmeier and Smith (1978) uses a nomograph of five soil parameters: percent modified silt (0.002-0.1 mm), percent modified sand (0.1-2.0 mm), percent organic matter (OM), soil structure code, s, and profile permeability class, p, to determine soil erodibility. From the nomograph, Wischmeier and Smith (1978) develop an algebraic approximation for calculating soil erodibility when the silt fraction is less than 70% in representing the K factor such that

K  2.1 104 12  OMM1.14  3.25s  2 2.5p  3/100 (6) where M is the product of primary particle size fractions: (% modified silt)(% silt + % sand). K is expressed as ton per acre per erosion unit index. For conversion to SI units, K is divided by 7.59 (Renard, Foster, Weesies, McCool & Yoder, 1997). Wischmeier and Smith (1978) further develop a K factor from the geometric mean particle diameter of 225 global soil classes with less than 10% rock fragments (D > 2 mm) by weight. Soils with more than 10% rock fragments were excluded since mechanical soil analysis generally excludes rock fragments larger than 2 mm. This relationship calculates soil erodibility, K, such that

. = 0.0034 + 0.0405 × −0.5 (7) . where

  di  di1  Dg  exp fi ln . (8)   2 

Dg is the mean particle size, di is the maximum particle diameter, di-1 is the minimum diameter and fi is the corresponding mass fraction (Renard et al., 1997). This relationship is particularly useful when soil composition data are limited (Kouli et al., 2009). Rock fragments (D > 2 mm) affect infiltration and, by consequence, runoff. As part of the particle size range, rock fragments

10 should be considered in the computation of the soil erodibility factor (Renard et al., 1997). To include the effect of rock fragments as part of the soil erodibility factor, the saturated hydraulic conductivity of the soil is reduced by a percentage weight of rock fragments in a soil profile, thereby changing the permeability class as determined by the soil texture (Renard et al., 1997).

2.1.2.3. Slope Length and Slope Steepness Factors

The product of slope length, L, and slope steepness, S, the dimensionless LS factor, represents the influence of topography on soil loss (Kouli et al., 2009). Slope length is defined as the horizontal distance from the point where overland flow begins (i.e. a watershed boundary) to the point at which deposition begins or accumulated overland flow converges to form a channel (Ferreira & Panagopoulos, 2014). Higher slope length and slope steepness factors contribute to increased overland flow, resulting in higher rates of erosion, with greater sensitivity demonstrated by slope steepness (Kouli et al., 2009). However, traditional methods of the USLE that used a single, uniform slope applied over the entire catchment, consistently underestimated LS values. This method implied uniform runoff, with no representation for sedimentation occurring over complex topography (Garcia Rodriguez & Gimenez Suarez, 2012).

Using a RUSLE 3D model to compute a grid cell value that represents a segment of a hillslope, the LS value at a point r = (x,y) on a hillslope is defined as

() () = ( +1) (9) . .

where m and n are parameters according to the type of flow and soil properties, Ad(r) is the specific catchment area and b(r) is the slope at point r. Using Eq. (9), 22.13 m and 5.143° represent the length and slope, respectively, of a standard USLE plot, by definition. The USPED model incorporates flow concentration, which can increase the spatial variability of soil erosion (Garcia Rodriguez & Gimenez Suarez, 2012). A more explicit representation of converging and diverging terrain with spatial variability uses GIS technology, applying an LS value to each cell of the grid surface employing flow accumulation and slope steepness such that

∗ = ∗ (10) . .

11 calculating LS on a cell by cell scale equivalent to that of the source DEM (Ferreira & Panagopoulos, 2014). The parameters m and n are constants representing water flow and the ratio of rill to interill erosion (Ferreira & Panagopoulos, 2014; Garcia Rodriguez & Gimenez Suarez, 2012). These constants are calibrated based on the prevailing relief forms, plant covering and erosive process (Oliveira et al, 2014).

2.1.2.4. The Vegetation Cover Factor

The vegetation cover factor, C, represents the effect of land/vegetation cover on soil loss (Kouli et al, 2009). Vegetation cover influences overland flow and erosion rates by dispersing the energy of raindrops before making contact with the soil surface (Farhan & Nawaiseh, 2015). Thus, soil loss decreases with increased vegetation cover (Kouli et al., 2009). Spectral information obtained from remote sensing is the most widely used method for estimating vegetation cover (Ferreira & Panagopolous, 2014). The Normalized Difference Vegetation Index (NDVI) is an indicator of vegetation growth derived from remote sensing data. Based on the measured spectral response of the earth surface, the index produces values from -1.0 to +1.0, with positive values representing photosynthetically active vegetation and negative values indicating low or unhealthy vegetation (Kouli et al., 2009). The NDVI measures the spectral response of varying land cover types such that

= (11)

where LNIR Band is the near infrared reflectance and LRed Band is the red reflectance of the spectrum from remote sensing data (Kouli et al., 2009). To generate the C factor, a regression equation model is used such that

NDVI/ NDVI C  exp (12) where  and  are unit-less parameters defining the curve that relates NDVI to the C factor. Ferreira and Panagopoulos (2014) use values of two and one, respectively, and produce different C factor maps varying by season.

2.1.2.5. The Land Management Practices/Conservation Factor

The land management practices/conservation, P, factor represents the effect of tillage and “best practices” land use on soil erosion (Renard et al., 1997). The P factor is the ratio of soil loss using a particular conservation practice to soil loss using upslope-downslope tillage (Farhan & Nawaiseh, 2015). Conservation practices modify flow patterns of surface runoff, reducing the rate of runoff and surface erosion (Ferreira & Panagopoulos, 2014). Cultivated areas typically have higher erosion rates than uncultivated areas (Kouli et al., 2009). Different types of agricultural land management practices include strip cropping, contour tillage, subsurface drainage, terracing and rangeland, applying different P factors for particular applications used (Renard et al., 1997). Mediterranean countries have been found to typically have P factor values in the range of 0.85 to 0.90 (Panagos et al., 2015a). Field observations, and landscape analysis using supervised and unsupervised classification of remote sensing images, allow assessment of existing conservation practices (Arhonditsis, Giourga, Loumou, & Koulouri, 2002).

2.2. Stream Power as an Indicator of Geomorphic Processes

The rate of downstream sediment transport is related to the amount of stream power available (Bagnold, 1966). Consequently, sedimentary processes, such as sediment entrainment, transport and deposition, are reflected by the carrying power of the stream (Eaton & Church, 2011). Analysis of downstream variability of stream power and channel gradient provides a better understanding of fluvial geomorphic processes occurring in a channel (Reinfelds, Cohen, Batten, & Brierley, 2004). Total stream power, , in any given river reach is defined as

  gQS (13) where  is the water density, g is the gravitational acceleration, Q is the discharge and S is the channel gradient. To examine the direct energy applied to a channel bed per unit area, most studies use specific stream power, , as an indicator of fluvial geomorphic processes (Reinfelds et al., 2004). Total available stream power, , is expressed as an average of the cross-sectional stream power to represent the energy a column of water exerts, per unit bed area, on the channel bed (in units of Wm-2) (Phillips & Desloges, 2014). Specific stream power, , is defined as

 gQS    (14) w w where w is the bankfull channel flow width (Reinfelds et al., 2004). Specific stream power varies with increasing discharge and variations in channel geometry, best demonstrating available energy to perform geomorphic work (Phillips & Desloges, 2014). The calculation of specific stream power for an alluvial, self-forming channel in equilibrium would be expected to be close to the energy required to move the existing bedload (Parker, Clifford, & Thorne, 2011). Adjustments to channel geometry in response to channel grain size presumably correspond with a channel width adjustment that reflects inherited local boundary conditions (Golden & Springer, 2006). Specific stream power can be calculated without detailed knowledge of flow properties, such as depth and velocity. It is, instead, computed from overall channel characteristics, incorporating independent variables of channel slope, S, bankfull flood discharge, Qbf, and channel bankfull width, w (Ferguson, 2005).

A significant advantage of employing GIS methods is the ability to illustrate stream power trends in a continuum as opposed to discrete points (Reinfelds et al., 2004). Using GIS tools to extract drainage area and channel slope data allows relatively simple modelling of stream power variation along the channel length (Reinfelds et al., 2004). This can reveal zones of rapidly decreasing energy that correspond to deposition, or zones of increasing energy associated with sediment entrainment (Reinfelds et al., 2004).

2.2.1. Downstream Discharge

Empirical analysis of channel geometry relies on an understanding of the continuity relation,

= (15) where Q represents discharge, w is the width of the water surface, d represents mean hydraulic depth and v is mean flow velocity (Eaton, 2013). Increases in the velocity of flow or the channel cross sectional area result in an increase in stream discharge. As such, site specific at-a-station hydraulic geometry relations can be used to estimate channel flow for a variety of discharges

(Eaton, 2013). Williams (1978) employs regression to estimate bankfull discharge, Qbf, for ungauged rivers such that 14

. . = 4.0∗( ∗ ) (16)

where wbf represents bankfull width, dbf is bankfull depth and S is the surveyed channel slope. A 233 station dataset of both active floodplain and valley flat bankfull discharges, with a variety of climate and geographic conditions, was used. Recurrence intervals ranged from 1 to 32 years, mean annual precipitation ranged from 200 to 2000 mm a-1, drainage areas ranged from 7 to 614,000 km2 and mean diameter of bed material ranged from 0.19 to 190 mm (Williams, 1978). This relation has been shown to be less subject to error than the Manning and Darcy-Weisbach relations (Thayer, Phillips & Desloges, 2016).

Downstream discharge, Qbf, can also be calculated using a drainage area-discharge relationship defined by

z Qbf  aAd (17)

2 where Ad is the continuous downstream drainage area in km (Flint, 1974). Statistical regression of empirical data provides the coefficient a and exponent z (Phillips & Desloges, 2014).

2.2.2. Bankfull Width

A similar drainage area-width proxy is useful to estimate bankfull channel width, wbf, where

= (18) using regression analysis to produce coefficients  and  from empirical channel width data (Phillips & Desloges, 2014). This provides a continuous downstream width proxy necessary for representing the continuous spatial distribution of specific stream power.

2.2.3. Channel Gradient

The use of continuous slope output from a DEM along the channel better reflects local gradient differences that may represent sudden variation of stream energy (Knighton, 1999). Increasing accuracy of GIS technology makes slope extraction from a DEM a reliable substitute for field surveying of the channel (Flores, Bledsoe, Cuhaciyan, & Wohl, 2006). To determine channel

15 gradient, an appropriate average channel length over which to measure slope must be determined (Knighton, 1999). The generalized slope, therefore, is influential in the spatial distribution of specific stream power. Since stream power reflects energy dissipation over unit length, it is most accurately represented using a relatively constant average channel length to determine gradient (Jain, Preston, Fryirs & Brierley, 2006). A vertical slice, or fixed elevation drop, uses only elevations derived from the source DEM, omitting interpolated values (Phillips & Desloges, 2014). Reinfelds et al. (2004) use a 10 m vertical slice, to be consistent with the resolution of the source DEM, to derive gradient. GIS modelling of channel gradient using a vertical slice better distinguishes between high and low gradient reaches in headwater and mid-zone reaches over brief distances (Reinfelds et al., 2004). However, the vertical slice may be unsuitable in low- relief basins (Vocal Ferencevic & Ashmore, 2012). Some studies prefer the horizontal slice approach to determine unit length (Knighton, 1999). Using a 500 m horizontal slice, or step length, Knighton (1999) calculates stream power at 500 m slope intervals. A 5-point moving average is used to even out irregularities while highlighting key differences. Alternatively, Phillips and Desloges (2014) combine techniques used by Reinfelds (2004) and Knighton (1999), using a vertical slice and a multi-pass moving average of 1 to 2 km length to smooth the longitudinal profile while enhancing primary slope features.

2.3. The Relationship between Geomorphic Processes and Artifact Exposure

Research to evaluate the relationship between geomorphic activity and artifact exposure is limited (Gouma et al., 2011). Recent archaeological research has attributed density of artifact finds to the variable sediment deposition of land surface processes and poor management practices (Gouma et al., 2011). Artifact shape, size and weight have been shown to influence the transportability, distance travelled and distribution pattern of surface artifact finds (Byers et al., 2015). Gouma et al. (2011) examined artifact exposure in a region of low and irregular artifact concentration. They found increased artifact concentration in stable/depositional areas and low concentration in areas of high erosion, suggesting that archaeological items never accumulated in these areas. The concentration of these finds suggests that a wide range of geomorphic processes, such as soil erosion (entrainment, transport and deposition), responding to rain and vegetation factors, influence preservation of an archaeological site. Byers et al. (2015) studied the effects of

16 fluvial transport on artifact redistribution, since river proximity is often favourable for human settlement. They determined that artifact weight is the most important factor in transport distance, with lighter artifacts travelling greater distances. The study suggests that additional research is needed to examine other factors such as fluid viscosity and overland flow versus channel flow transport.

Chapter 3: Inachos River Watershed 3.1. Location

The Inachos River watershed is located in the northeast Peloponnese region in southern Greece (Figure 3.1). The Inachos catchment area is approximately 537 km2, occupying the western side of the Argolid (Ministry of Environment, Energy and Climate Change (MEECC), 2011). The length of the primary river bed runs approximately 43 km, from headwaters of Artemisio mountain draining into the Kaparelli basin, flowing through the Lyrkeia valley, through the area of Sterna and across the alluvial plains of the Argolid into the Aegean Sea at Nea Kios. The Inachos watershed has intense topographical variation, with elevations varying from 1771 m to zero m asl at the outlet. The mean elevation is 544 m asl. The Inachos channel is also heavily modified from its mouth to the Derveni confluence to control seasonal flooding. River engineering and flow modification create confluences with the Derveni and Xerias rivers at the north end of the city of Argos. Channel engineering also occurs 2.8 km upstream of the Derveni confluence (MEECC, 2011).

For the purpose of this study the accumulated flow into the Inachos River, determined by topographical elevation, results in a natural catchment area of approximately 243 km2, excluding contributions from the Derveni and Xerias rivers, to the northeast and southwest, respectively.

The polygon of the detailed archaeological survey is approximately 30 km2, located primarily within the Lyrkeia valley section. The elevation of the archaeologic study polygon varies from 541 to 99 m asl, with a mean elevation of 231 m asl.

Figure 3.1. - Location of the Inachos River watershed. Site of the detailed archaeological survey is outlined in gold line. Basemap source: Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community.

3.2. Climate and Hydrology

The northeast Peloponnese region of Greece has a semi-arid climate. Monthly average temperatures vary from approximately 5C in January to approximately 27C in July, with absolute extreme temperatures ranging from -17 to 45C (Helenic National Meteorological Service, 2016). Highest temperatures are associated with lowland and coastal areas, whereas mountainous areas are cooler, especially in winter months (MEECC, 2011). Climate records (1951-2015) for the study area record mean annual precipitation of 621 mm per year with significant spatial and temporal variation. Mean annual precipitation ranges from 290 to 457 mm at the lowest parts of the watershed up to greater than 1000 mm in the highest elevations. Typical

19 of a Mediterranean climate, more precipitation occurs in cooler winter months than hot summer months (Figure 3.2) (Helenic National Meteorological Service, 2016; Hydroscope, 2016). Occasional snowfall occurs in surrounding mountainous areas (MEECC, 2011).

The Inachos River is an ephemeral stream with peak flows occurring in winter months. Flow gauge stations are not present on the Inachos River. Flow studies suggest an average discharge of 134,100 dm3 per annum, including contributions from the Xerias River, and 98,400 dm 3 upstream of the Xerias confluence (MEECC, 2011). Sporadic floods of the Inachos River occur approximately every 15 years, generally lasting one day (Zangger, 1993, p. 18).

Figure 3.2. - Mean monthly precipitation values for hydrograph stations in the northeast Peloponnese, Greece. Station elevation is indicated in brackets.

3.3. Physiography

The northeast Peloponese exhibits sharp crested peaks associated with tilted thrusts and extensive uplift (Anagnostoudi et al., 2010). The mountain ranges consist primarily of flysch, limestone and ophiolites of the Mesozoic through Eocene age (van Andel et al., 1990). The last major tectonic movements occurred during the Pliocene and early Pleistocene periods (Zangger, 1993, p. 6). Underlying geology and transfer faults influence the drainage patterns of the region (Anagnostoudi et al., 2010). The topography of the catchment can be subdivided into four sections: headwater drainage into the enclosed Kaparelli basin, the wide Lyrkeia valley, a narrower segment at Sterna and the wide, lowlands of the Argolid Plain.

The upstream headwater regions generate coarse debris flows that descend steep limestone slopes and channels. Indurated alluvial fans surround the Kaparelli basin and Lyrkeia valley, with a coincident fan occurring at the Karyotikos tributary (near Karya) (Gaki-Papanstassiou, 1991). Fill terraces at 320 m asl possibly correspond to a Mid-Pleistocene base level (Gaki-Papanastassiou, 1991). Base level fall has renewed incision, and further fan deposition of unconsolidated material is reflected in surrounding 3 to 4 m terraces, potentially associated with the Late Pleistocene (Gaki-Papanastassiou, 1991). No terrace is evident in the Lyrkeia valley section (Gaki- Papanastassiou, 1991). The upper segment and the Lyrkeia valley are dominated by wide U- shaped valleys, fringed by the large alluvial fans of the Mid- to Late Pleistocene at the foot of steep bedrock slopes (van Andel et al., 1990). The fans form moderate slopes covered by very dry, poorly sorted sediment (Gaki-Papanastassiou, 1991; Zangger, 1993, p. 17).

Near Sterna, the valley narrows, controlled by a spur of limestone bedrock near the Inachos River channel. The Inachos has incised through a 4 to 5 m terrace of Plio-Pleistocene conglomerates associated with Pliocene uplift (Gaki-Papanastassiou, 1991).

Downstream of Schinochori, the Inachos widens to cross the broad, fluvial-aggraded Argolid plain (Gaki-Papanastassiou, 1991). The plain is covered by Late Holocene streamflood deposits and overbank loams as a result of infrequent floods (van Andel et al., 1990).

3.4. Current Land Use

The Inachos River watershed is the site of intensive agricultural use. Vineyards and orchards dominate the basin floor and the moderate slopes employ terraced and contoured agricultural practices for the farming of olives and apricots. Gullying and ploughing practices are active. Steeper slopes are covered by maquis shrubland, typical of the Mediterranean region and often an indicator of reduced maintenance and grazing stress (Fuchs et al., 2004; Pope & van Andel, 1984). The steeper slopes, with degraded vegetation on conglomerate outcroppings, are primarily used for goat grazing.

3.5. Settlement History

Throughout history, the riverine environment has attracted humans, offering water, food and transportation (Mexia, 2015). The earliest known site of human presence in the Argolid plain is a hunter-gatherer community dwelling in a late Middle Paleolithic cave at Kefalari (approximately 50,000 years ago) (Table 3.1). The Kefalari cave and a cave at Frachthi in the southern Argolid were inhabited during the Upper Paleolithic (35,000-10,500 B.C.) (Zangger, 1993, p. 1). The Early Neolithic period (6,000-5,000 B.C.) brought permanent settlement to the Argolid, with the introduction of agriculture and herding (Fuchs et al., 2004; Zangger, 1993, p.1). A Middle Neolithic site (later than 5,000-4,000 B.C.) in the coastal zone of the Argolid plain, buried by early Holocene alluvium, indicates an alluviation event thought to predate 3,000 B.C., with soil forming on top of the deposits after the landscape stabilized (van Andel et al., 1990). Similarly, several cultural relics have been found from the Copper Age (Fuchs et al., 2004). Important settlements were established in the Early Bronze Age (3,200 to 2,150 B.C.) at Lerna, Mycenae and Tiryns (Zangger, 1993, p. 2). The most significant environmental change occurred during the Early Helladic Period (3,200 to 2,150 B.C.) when a major soil erosion event stripped the Pliocene marls and Pleistocene fans of brown woodland soils, depositing them across the Argolid plain (van Andel et al., 1990). Slope stability returned and remained until the Late Bronze Age, when numerous sites achieved prominence during the Mycenaean period (1,650 to 1,100 B.C.) (Zangger, 1993, p. 4; van Andel et al., 1990). The first advanced civilization on mainland Europe was the Mycenaean culture (Fuchs et al., 2004).

Table 3.1. - The chronology of cultural periods, occupation, land use and landscape events of the Argolid region.

Date Cultural Period Occupation, land use and landscape events

Modern Period

1,833 A.D. -

Ottoman Period

1.453 A.D. -

Medieval Period

610 A.D. -

Roman Period

31 B.C. -

Population increase; Arable land becomes scarce for olive Classical Antiquity and vine cropping, pastures dominate

725 B.C. -

Early Iron Age Argos becomes dominant centre

Flooding and possible anthropogenic erosion buries 1,065 B.C. - Tiryns in alluvium Major soil erosion strips Pliocene marls and Pleistocene Bronze Age fans, depositing sediment on the Argolid plain

3,200 B.C. - Settlement at Lerna, Mycenae and Tiryns

Alluviation event buries Neolithic site, landscape stabilizes Copper Age and soil forms

4,500 B.C. -

Neolithic Permanent settlement; Agriculture and herding

7,000 B.C. -

Mesolithic

9,500 B.C. -

Upper Paleolithic Human presence at Frachthi cave ~ 35,000-10,500 B.C.

40,000 B.C. -

Middle Paleolithic Human presence at Kefalari cave ~ 50,000 B.C.

200,000 B.C. -

During the Late Bronze Age, torrential flooding occurred, coupled with possible anthropogenic soil erosion due to intensified agriculture and land clearing, burying parts of nearby Tiryns under meters of alluvium (van Andel et al., 1990). By the Early Iron Age (1,100 to 675 B.C.), Mycanae and Tiryns had become comparatively insignificant, while Argos became the dominant centre (Zangger, 1993, p. 4). During the Archaic period (675 to 480 B.C.), population increased and arable land became scarce as Argos became one of the most important Greek cities (Zangger, 1993, p. 4). Increasing numbers of Classical, Hellenistic and Roman sites confirm the region was used extensively from Mycenaean times onward (van Andel et al., 1990). Olive cultivation dates back to the Bronze Age, with important olive and vine crops during Classical Antiquity (725 to 31 B.C.), however, pastures dominated agriculture until the Roman period (31 B.C. to 610 A.D.) (Fuchs et al., 2004). Intensive agriculture remains in the catchment, with vineyards dominating the basin floor, olive and apricot trees covering the terraced, gentle slopes, and goat grazing on the hard, steeper conglomerate outcrops (Fuchs et al., 2004).

The landscape has remained somewhat unchanged since approximately 2,000 B.C., except for intermittent overbank loam deposits along the Inachos River and only one meter of sediment deposited in the Argolid plain in the last 3,000 years (van Andel et al., 1990). Consequently, the Argolid is an area of active archaeological survey due to the importance of Mycenaean cultures in the early history of Greece and all of the Mediterranean (Zananiri et al., 2010).

Chapter 4: Methods 4.1. Fieldwork

Fifteen (15) Inachos River sites were identified for data collection based on accessibility and elevation (Figure 4.1). Rapid surveys were completed to assess slope, s, bankfull width, w, and median (D50) grain size. Bankfull channel width was interpreted by changes in vegetation, bank height, sediment texture and indicators of overbank flood events. Median grain size of the channel bed was determined at each site using the Wolman (1954) pebble count method to characterize the grain size distribution of bed material.

Figure 4.1. - Location of study sites within the Inachos River watershed. S refers to soil sample sites and R is the river channel survey sites. Basemap source: Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community.

4.2. Soil Analysis

Fifteen (15) soil sites were identified for data collection (Figure 4.1). Vegetation, aspect, land conservation practices and observed permeability were noted. Soil was sampled from a 0.5 m x 0.5 m plot to a depth of 4 cm at six (6) sites. Large fraction grain size analysis (D > 20 mm) was completed on site, measuring a-, b-, and c-axes. One quarter plot (25%) of the small fraction grain size (D < 20 mm) was collected for laboratory particle size analysis.

Grain size distributions of soil samples taken to the laboratory were developed from manual measurements of the D > 4 mm fraction and sieve results from the D < 4 mm fraction. Grain size distribution for six (6) sites were completed. Loss-on-ignition was performed on selected samples from the 15 sites to determine organic matter content.

4.3. Delineation of the Watershed To identify the total drainage area of the Inachos River basin, the watershed boundary was established. Elevation data were extracted from the National Cadastre & Mapping Agency S.A. (2016) DEM with 5 m x 5 m resolution (Figure 3.1). The Hydrology toolset and Spatial Analyst extensions of ArcGIS version 10.4 were used to establish the downward flow of water surfaces to ensure that the dataset was hydrologically enforced. The steepest downslope flow from each grid cell to its neighbouring cell and consecutive accumulated flow in a downstream direction were determined to identify cells with high flow accumulation. This allows for calculation of the upstream catchment area for each of those cells. The “pour point” for the entire watershed was established based on maximum accumulation at the watershed outlet to the Aegean Sea. Based on this basin outlet, the drainage basin and the watershed boundary were established.

4.4. RUSLE Calculation

A GIS modelling approach (USPED) was used to capture the spatial variability of the RUSLE equation (Eq. 1) across the watershed. Individual GIS layers enabled a 5 m x 5 m calculation for each factor of the RUSLE (R factor, K factor, LS factor, C factor and P factor). The factors were combined to compute the soil loss per unit area (t ha-1 a-1), A, for each grid cell, thus estimating spatial variations of soil loss across the watershed (Figure 4.2).

Figure 4.2. - Flowchart of RUSLE modelling process using GIS applications and algorithms.

4.4.1. Computation of the Rainfall Erosivity Factor, R

The analysis of rainfall erosivity used the modified Fournier index (FF), as described in Eq. 4. Historical precipitation data from 10 rain-gauge stations throughout the northeast Peloponnese, from 1951 to 2015, produced mean monthly and mean annual precipitation (Figure 4.1). Using the R-FF relationship established for Sicily-Italy (Eq. 5), the R factor was calculated for each of the 10 rain-gauge stations. Point information was fit to an exponential model and interpolated across the Inachos watershed.

4.4.2. Computation of the Soil Erodibility Factor, K

Soil erodibility was calculated using the relationship outlined in Eq. 6. This calculation incorporated results from the grain size distribution analysis, loss-on-ignition, and observed soil permeability and structure. Variability of soil texture within the Inachos River basin was verified using the Soil Map of Greece from the European Soil Data Centre (2015). K factor calculations were verified using the Wischmeier and Smith (1978) nomograph. To integrate variability of soil analysis results, K factor values were applied based on land use classification. Using heads-up digitizing, four (4) land use feature classes were established for the catchment: rural-urban, orchard/vineyard, terraced/contoured orchard, and natural/pasture. Topology ensured the accuracy of spatial relationships between adjacent feature classes of the geographic data layer, enforcing rules of data integrity so that there were no overlaps or gaps between polygons.

4.4.3. Computation of the Slope Length and Steepness Factor, LS

The slope length and steepness factor was computed from the 5 m x 5 m DEM in ArcGIS. Using the Spatial Analyst Extension, slope (in degrees) was calculated for each 5 m x 5 m cell within the watershed boundary. Flow accumulation and slope rasters provided the variable values required in Eq. 10 to produce a cell by cell LS factor using Raster Calculator.

4.4.4. Computation of the Cover Management Factor, C

The NDVI was compiled from WorldView-3 satellite imagery for approximately 80 km2 of the watershed that encompasses the archaeological survey polygon. The red band and near-IR band spectral data with 0.3 m x 0.3 m resolution produced an NDVI raster (Eq.11) that was applied to Eq. 12 to compute a cell by cell C factor for the available 80 km2 area. The 0.3 m cells were

28 resampled using weighted distance averaging to produce a C factor raster with 5 m x 5 m resolution to conform to the resolution of the source DEM. A random sampling of C factor values, applicable to land use classifications, was used to approximate the C factor for areas outside of the 80 km2 satellite imagery. An estimated C factor raster (by land use) and the spectral based C factor raster (80 km2 including the survey area) were integrated to create a complete “cover management” layer for the watershed.

4.4.5. Computation of the Support Practice Factor, P

A support practice factor of 0.8 was applied to land use polygons where contour cultivation and terraced agriculture was observed in WorldView-3 imagery and ArcGIS world imagery. A support practice P factor value of 1.0 was assigned to remaining polygons in the catchment where conservation practices were not deemed present.

4.5. Specific Stream Power Calculation

Using the Hydrology toolset and Spatial Analyst extensions of ArcGIS version 10.4, the steepest downslope flow from each grid cell of the 5 m x 5 m hydrologically enforced DEM to its neighbouring grid cell, as well as consecutive accumulated flow in a downstream direction for each cell, was determined (Figure 4.3). Hydrological enforcement verifies the downward flow of stream water surfaces. This identifies cells with high flow accumulation and determines the upstream catchment area for each of those cells. A threshold drainage area1 of 2 km2 was applied to flow accumulations of the catchment to delineate stream flow from overland flow. Based on greatest accumulated drainage area, the primary flow of the Inachos River was isolated from the stream network (i.e. tributaries). Contiguous elevation and flow accumulation cells were converted to point features, positioned at the Universal Transverse Mercator (UTM) location of each cell centre and exported to an algorithm where slope and discharge were calculated. Elevation and downstream distance were used to establish the longitudinal profile of the Inachos River. Contiguous points along the profile are spaced at 5 m intervals (or 7.07 m when stream

1 Determination of the threshold for channel flow varies with climate regime, slope, lithology and land use (Jaeger, Montgomery & Bolton., 2007). Assuming that accumulated flow path follows topography, the model used for this study applied a 2 km2 drainage area surrogate to estimate channel initiation. 29 position moves diagonally). Incremental flow accumulation was used to represent drainage area,

Ad (i.e. flow accumulation x cell area).

Figure 4.3. - Flowchart of the specific stream power modelling process using GIS applications and algorithms.

4.5.1. Determination of Slope

Slope, S, was computed using 500 m horizontal intervals along the profile of the Inachos River (the horizontal slice approach) similar to that used by Knighton (1999). While a vertical slice method has been demonstrated to better maintain location and accuracy of slopes for complex profiles, the horizontal slice was deemed most appropriate to discriminate localized elevation changes over the long flat sections of the archaeological study area that would otherwise go undetected within the thickness of a vertical slice. A 2 km moving window smoothing technique was applied to dampen inherent noise generated by the DEM extraction process and better represent changing slope at a scale relevant to fluvial processes.

4.5.2. Determination of Discharge and Width Relations

At-a-station discharge was computed for 15 sampling sites using the Williams (1978) relation (Eq. 16). Hydraulic geometry values (width and depth) were established by rapid field surveys. Bankfull discharge estimates at each of the 15 field sites along the Inachos River were employed to provide a Qbf proxy. Using the estimated bankfull discharge and cumulative drainage area associated with the 15 surveyed sites, a drainage area-discharge relationship (Eq.17) was established.

Computations of width from the 15 field surveys along the Inachos River and the cumulative drainage area associated with the 15 field sites were used to establish a drainage area-width relationship as outlined by Eq. 18.

4.5.3. Mapping Specific Stream Power

Using spreadsheet algorithms, downstream discharge, Q, slope, S, and specific stream power, , were calculated from a combination of field data and modelled outputs (Eq. 14). Using UTM coordinates, contiguous specific stream power values were imported to point features in ArcGIS, version 10.4, for illustration of the spatial distribution of specific stream power.

4.6. Comparing RUSLE to Artifact Density Distribution

The computed RUSLE model and specific stream power model associated with the archaeologic study polygon were extracted from the Inachos River watershed models. A shapefile identifying 7,364 individual archaeological survey tracts and the corresponding artifact density distribution per survey tract was provided by WARP. The artifact density records summarize surface artifacts collected by systematic, fieldwalk survey over three field seasons (2014, 2015 and 2016). Findings were 22.6% pottery sherds, 76.3% clay tile, 0.1% lithics and 0.9% other. The WARP survey is the most extensive surface artifact collection of this type assembled for this region. Although extensive, conditions associated with a fieldwalk survey will always present some challenges to the development of a complete landscape picture representing artifact density. The WARP records identify the number of surface artifacts per hectare per survey tract within the archaeological study polygon. Each survey tract is identified with an individual object ID (Figure 4.4). This identification enables zonal analysis of the USPED raster, and its contributing raster layers, using Zonal tools in ArcGIS, version 10.4.

Figure 4.4. - Artifact density (# of artifacts/ha) per survey tract for the archaeological study polygon.

Using the Zonal Statistics tool, USPED values for each 5 m x 5 m cell within each survey tract were averaged to obtain an average soil loss (t ha-1 a-1) per survey tract. Summary statistics for each survey tract were exported to an algorithm for multivariate analysis.

Chapter 5: Results 5.1. Estimating Potential Soil Surface Erosion and Hillslope Surface Stability

The study provides a spatially variable estimation of soil loss potential for the Inachos River watershed. The GIS framework enables the expression of soil loss in tonnes per ha per year per unit area.

5.1.1. The R Factor (rainfall erosivity)

Rain gauges from the 10 stations of the Peloponnese region provide precipitation records ranging from 10 to 62 years, for elevations varying from 1020 m asl to 9 m asl. An average annual precipitation of 620.9 mm was recorded, ranging from 289.6 mm at Argos in the lowlands, to 1007.7 mm at Frousiouna in the mountainous region. The variation of average annual precipitation among stations is reflected in the calculation of the modified Fournier index and subsequent R values which range from 421.2 to 1895.0 MJ mm ha-1 h-1 a-1 (Appendix A). The R factor point values were calculated using the relationship between the modified Fournier index and rainfall erosivity, R, for Sicily, Italy established by Ferro et al. (1999). Sicily, Italy is characterized by a similar climate having hot, summers and intense seasonal storms, with an orographic and maritime influence. Consequently, it is considered to have a similar relationship between R and FF (Ferro & Porto, 1999). The range of R values produced were within the range of values obtained by Ferro and Porto (1999) for Sicily, Italy.

To ensure the reliability of interpolated rainfall erosivity point data, several interpolation techniques were applied and tested. Inverse distance weighting and ordinary kriging using a stable model, and an exponential model were compared. Semivariogram analysis checked R factor point data for directional dependency and determined four sectors with a 45 offset. Using this approach to fit semivariance results, the exponential model demonstrated the best cross- validation and root mean square (RMS) results (Appendix A).

(a) (b)

(e) (f)

Figure 5.1. - The RUSLE and individual factors as raster layers: (a) the R Factor, (b) the K Factor, (c) the LS Factor, (d) the C Factor (high resolution in the region with available WorldView 3 imagery), (e) the P Factor, and (f) the RUSLE.

The ordinary kriging interpolator was selected, fitting to an exponential model, to interpolate R factor point data. Prediction errors for this method produced a mean value of -19.9, and RMS value of 367.1, a standardized mean of -0.03, a standardized RMS of 0.9 and average standard error of 379.8 MJ mm ha-1 h-1 a-1. Simulated R factor values were mapped to a 5 m grid for the Inachos basin (Figure 5.1a). Simulated R factor values range from 425.0 to 1503.5 MJ mm ha-1 h- 1 a-1, with a mean and standard deviation of 955.4 and 232.7 MJ mm ha-1 h-1 year-1, respectively.

5.1.2. The K Factor (soil erodibility)

Grain size analysis results for fine grain sediment (D < 2 mm) were categorized by land use as orchard/vineyard, terraced/contoured orchard and natural/pasture. The modified sand mass fraction (3 > % > -1) for orchard/vineyard, terraced/contoured orchard and natural/pasture was computed as 89.7%, 91.6% and 93.7%, respectively, of the total sample mass (Figure 5.2). The particle size distribution was extrapolated to approximate a 1% clay mass fraction (% > 8 ) for all land use categories. Low clay content is characteristic of dry Mediterranean regions (Hill & Schutt, 2000). The Modified Silt mass fraction (8 < % < 3) for orchard/vineyard, terraced/contoured orchard and natural/pasture was calculated as 9.3%, 7.4% and 5.3%, respectively, of the total sample mass.

Rock fragments (D > 2 mm) accounted for the greatest mass fraction of total sample areas for all land use soil classifications. The rock fragment mass fraction (% < -1) for orchard/vineyard, terraced/contoured orchard and natural/pasture was 94.5%, 93.0% and 71.3%, respectively. Soil structure and permeability classes were estimated by field observation of soil texture and aggregate structure, in addition to particle size distribution as determined by the grain size analysis. Structure and permeability was classified in accordance with the United States Department of Agriculture (USDA) structural and textural classes (Appendix B). Soil structure, s, was assessed as structure class 3, medium or coarse granular, for all land use soil classifications. Soil permeability of the fine grain fraction were assessed at permeability class, p = 2, loamy sand, sandy loam for all land use soil classifications. Accounting for the modification of saturated hydraulic conductivity due to the percent of rock fragments (D > 2 mm), the permeability class, p, for orchard/vineyard, terraced/contoured orchard and natural/pasture were assessed as 4, 4 and

3, respectively. Loss on ignition analysis determined the organic matter content (% OM) for orchard land use and natural land was 1.6% and 0.8%, respectively, of the total sample.

K factor values were computed for orchard/vineyard, terraced/contoured orchard and natural/pasture such that K equals 0.0145, 0.0129 and 0.0074 t ha h ha-1 MJ-1 mm-1, respectively. The lowest K factor of 0.0074 t ha h ha-1 MJ-1 mm-1 was applied to rural-urban land use to represent reduced erodility in the rural-urban locations. K factor values were applied to each of the 108 polygons in the corresponding land use feature class. A 5 m raster was generated representing the soil erodibility K factor (Figure 5.1b). Computed K factor values were consistent with values obtained using the soil-erodibility nomograph established by Wischmeier and Smith (1978).

Figure 5.2. - Particle size distribution of small grain sediment (D < 2 mm) by phi value.

5.1.3. The LS Factor (slope length-steepness)

The LS factor was calculated using ArcGIS, version 10.4, to describe the topographic factors using the 5 m DEM (Figure 5.1c). LS factor values range from zero to 2,744.40, with and average LS value of 8.70 and standard deviation of 23.71. As length increases, soil erosion loss per unit increases as a result of increasing runoff accumulation. Likewise, as slope increases, erosion increases due to increased runoff velocity and erosivity (Oliveira et al., 2013). The highest LS factor values, therefore, coincide with the steep mountainous terrain and moderate sloping valley sides of the Inachos basin. The lowest LS factor values occur along the valley bottom. Potential rill erosion and deposition are captured by the USPED model, substituting flow convergence for slope length in the GIS environment, increasing the spatial variability. Values 0.4 and 1.3 were used for the parameters m and n, respectively, consistent with RUSLE (USPED) analyses in other Mediterranean catchments (Demirci & Karaburun, 2012; Saygun et al., 2014; Ferreira & Panagopoulos, 2014). Higher LS factor values are indicated in the steepest headwater regions of the basin representing a maximum soil loss associated with channelized gullying. Lower, more uniform values are indicated for the low gradient basin floor.

5.1.4. The C Factor (land cover index)

Computation of the C Factor employed available WorldView-3 spectral imagery acquired 20 Feb 2015 with a spatial resolution of 0.3 m. The extent of the satellite imagery covered the archaeological survey area. The NDVI was calculated using Red Band (630-690 nm) and Near- IR1 Band (770-895 nm) to produce a raster representative of surface vegetative cover for approximately 80.6 km2 of the basin (Appendix C). From the NDVI, a C Factor raster was computed for the 80.6 km2 area creating a C Factor index ranging from 0.0003 to 2.3440. Random sampling, by land use (n = 20), provided mean C Factors values for orchard/vineyard, terraced/contoured orchard, natural/pasture and rural-urban of 0.12, 0.24, 0.23 and 0.43 respectively. The 0.3 m C Factor raster was resampled using a bilinear resampling technique to generate a 5 m raster to conform to the resolution of the remaining dataset.

Mean C Factor values were applied to the region beyond the extent of WorldView-3 imagery to provide an estimation of C Factor values associated with land use type. A mosaic of estimated C Factor, by land use type, and C Factor established using WorldView-3 imagery generated a C

Factor raster for the Inachos basin with an index ranging from 0.002 to 2.205 (Figure 5.1d). This resulted in a raster with minimum, maximum and mean values of 5.52x10-8, 2.21 and 0.21, respectively, with a standard deviation of 0.13.

5.1.5. The P Factor (land management)

Visual interpretation of satellite imagery (1:2,000 and 1:8,000) and field observations were used to establish fields employing contour and terraced practices. A P factor value of 0.8 was applied to these areas. This generated a P factor raster with a mean P value of 0.98 which is consistent with European P factor values (Panagos et al., 2015a) (Figure 5.1e).

5.1.6. The RUSLE

Summary statistics for the five factor layers are displayed in table 5.1. The LS factor displayed the highest variability with a coefficient of variation of 2.726. The factor displaying the lowest variation was the P factor, with a coefficient of variation of 0.057.

Table 5.1. - Descriptive statistics for the USPED and five factor components.

Standard Coefficient Layer Minimum Maximum Mean Deviation of Variation USPED 0.000 4287.269 15.049 32.951 2.190 R Factor 424.966 1503.505 955.418 232.667 0.244 K Factor 0.0074 0.0145 0.0098 0.0032 0.327 LS Factor 0.000 2744.399 8.698 23.708 2.726 C Factor 5.52x10-8 2.205 0.206 0.131 0.636 P Factor 0.800 1.000 0.982 0.057 0.058

Using ArcGIS, version 10.4 with the Spatial Analyst toolset, integration of the five factor layers (R factor, K factor, LS factor, C factor and P factor) generated a spatially variable raster estimating soil loss for each 5 m grid cell within the Inachos River basin using map algebra. To distinguish overland geomorphic erosion processes from fluvial channel processes, the cells representing channel drainage (i.e. greater than 2 km2 contributing drainage area) corresponding to the Inachos River channel network were removed. The final output estimated potential soil loss values that ranged from zero to 4,287.27 t ha-1 a-1 (Figure 5.1f). The arithmetric mean soil loss and standard deviation were calculated as 15.05 and 32.95 t ha-1 a-1, respectively. However, the values are right skewed (median = 3.1 t ha-1 y-1). Erosion rates exceeding 1 t ha-1 a-1 are potential indicators of irreversible soil degradation, with the rate of soil loss potentially exceeding

40 the rate of soil formation (Panagos et al., 2015b; Gouma et al., 2011). The modelled mean annual soil loss suggests severe erosion risk and is similar to estimates for other Mediterranean watersheds. The mean soil loss rate for European Union is 2.22 t ha-1 year-1, with very high variation. The highest rates are demonstrated in Mediterranean areas with medium to high C factors, and high R factors and LS factors (Panagos et al., 2015b), such as the Inachos River basin. Similar estimates were obtained in Alqueva, Portugal, where A = 15.1 t ha-1 a-1 (Ferreira & Panagopoulos, 2014) and higher estimates were obtained for Calabria, Italy, calculating A = 30.6 t ha-1 a-1 (Terranova et al., 2009) and eight small watersheds on the island of Crete, Greece, where A values range from 77.2 to 205.5 t ha-1 a-1 (Kouli et al., 2009).

5.2. Estimating Fluvial Geomorphic Processes

5.2.1. Modern Bed Sediment of the Inachos River

Rapid field surveys were performed at 18 river sites within the Inachos River basin. The Inachos River is a gravel-bed river with poorly sorted bed material consistent with unorganized, episodic deposition. Well-developed levees (1 to 2 m) were present along the low gradient reaches, with occasional breaches, possibly associated with flow backup upstream of contributing tributaries. Evidence of some braided channel development in the active channel suggests active mobility of sediment during episodic flooding events. Substantial vegetation on some channel banks is consistent with infrequent bankfull discharge and flooding. A majority of the low gradient reaches appeared to be more depositional than erosive, with an average grain size of 0.071 m. Active anthropogenic mining of bed material was evident. Some imbrication of the active bed was noted. Unlike other observed low gradient channel sites, R9 displayed heavy channel scouring, gullying and entrenchment in a braided formation into a red bed conglomerate. The location of this site corresponds to the Plio-Pleistocene conglomerates associated with Pliocene uplift, described by Gaki-Papanastassiou (1991), near Sterna. Upper reaches displayed a steeper gradient and larger mean grain size (D50 = 0.138 m). Numerous large relic boulders were observed at R17, consistent with heavy debris flow and rock fall. River channels were frequently disrupted by level road crossings and/or engineered dams.

5.2.2. Bank Stratigraphy of the Inachos River

River bank exposures were examined at nine (9) sites in the low gradient reaches within the archaeological survey polygon. At most sites, three to four variably thick units of clast supported or matrix supported cobble-gravel with occasional small boulders were visible (Figure 5.3). Beds varied from 20 cm to 350 cm. Matrix material was dominated by medium to fine sand and silt. Infrequent layering and lenses associated with fluvial sorting were noted, however, banks predominantly displayed coarse, sub-rounded, unstructured sediment typical of deposition by chaotic, high energy debris flows. Finer grained sand/silt layers suggest input from deltaic fan development, and a greater role of water transport and deposition. A more indurated and oxidized red/brown unit of fine sand/silt observed at sites R8 and R9 indicates arid climate cementation (Figure 5.4; Figure 5.5). Greater bed resistance at site R9 has resulted in a widening of the active channel, with gully scouring where incision through the resistant red/brown layer has occurred. (Figure 5.5). No artifacts were noted in any of the bank or bed materials examined at the 9 sites. Garbage fill is dumped at several channel and bank sites creating some modern infill.

(IV)

(III)

(II)

(I)

Figure 5.3. - River bank exposure, approximate height 4.5 m, (Site R5, south bank) indicating four distinct units: (I) course cobble/pebble, clast supported, primarily massive (0-1.5 m); (II) oxidized medium to fine sand with pebble lenses (1.5-2 m); (III) weekly layered cobble/gravel with horizontally bedded course clasts, medium to fine sand matrix supported (2-3.5 m); and (IV) laminated silt/fine sand (3.5-4.5 m). 42

(IV) ) (III)

(II) )

(I)

Figure 5.4. - River bank exposure, approximate height 8 m, (Site R8, south bank) indicating four distinct units: (I) course cobble/gravel, matrix supported, occasional thin cobble beds (0-1 m); (II) oxidized medium to fine sand with pebble lenses (1-4.5 m); (III) cobble/gravel, medium to fine sand matrix supported (4.5-7 m), clast supported where lenses occur; and (IV) laminated silt/fine sand with thin bands of gravel (7-8 m).

(II) ) (I)

Figure 5.5. - Scour gullies into red-bed formations (Site R9) over a braided plain approximately 60 m wide. (I) Sub- rounded cobble/gravel, matrix supported material. (II) Resistant oxidized red/brown fine grained sediment. 43

5.2.3. Estimating Specific Stream Power

5.2.3.1. Drainage Area-Discharge and Width Proxies

Rapid field surveys of bankfull width, depth and slope were performed for 17 sites of the Inachos

River (Appendix D). Regression analysis of drainage area, Ad, and both bankfull discharge, Qbf, and bankfull width, wbf, are used to establish drainage area proxies for computing a contiguous downstream bankfull discharge and bankfull width. Coefficients of determination for these two relations are 0.806 and 0.471, respectively (Figure 5.6).

Figure 5.6. - Bankfull discharge and bankfull width proxies based on rapid field surveys in the Inachos River basin.

5.2.3.2. Longitudinal Profile

Contiguous point data of the hydrologically enforced DEM were extracted for each 5 m raster cell, relative to the UTM location of the cell centre. Information for each point, including the UTM location, elevation and accumulated flow, was imported to a spreadsheet algorithm for further computation. The Inachos River channel profile was established, using UTM locations and elevations, displaying a concave up form with a total channel length of 42.77 km. (Figure 5.7).

Figure 5.7. - Profile and gradient (raw and smoothed) of the main Inachos River channel.

A 500 m horizontal slice was applied to the raw point series data to establish a channel length over which to determine the raw channel slope. Raw slope data were generalized using a 2 km single pass, moving window smoothing technique to remove noise generated by the extraction process. Figure 5.7 shows the raw 500 m horizontal slice slopes and the generalized final (filtered over 2 km) slope estimates for the watershed.

5.2.3.3. Spatial Distribution of Specific Stream Power

Specific stream power was computed based on the modeled inputs for slope, S, bankfull discharge, Qbf , and bankfull width, wbf, for each contiguous point along the channel profile using spreadsheet algorithms. The spreadsheet algorithm was imported to ArcGIS, version 10.4, using UTM locations to produce a spatially variable specific stream power map of the Inachos River (Figure 5.8) with minimum, maximum, mean and standard deviation of 101, 17163, 2199 and 3570 W/m2, respectively (Appendix E).

Figure 5.8. - Spatial variability of specific stream power in the main branch of the Inachos River.

5.3. Assessing the Spatial Distribution of Geomorphic Processes

5.3.1. Comparing the USPED to Artifact Distribution

As per research objective #3, the USPED values per survey tract were extracted for regression analysis with artifact density within the archaeological survey polygon. Using ArcGIS, version 10.4 with the Spatial Analyst extension, the Zonal Statistics tool was used to compute the mean USPED value per survey tract of the WARP archaeological survey. This process was repeated for each layer of the contributing factors in the RUSLE results (Figure 5.9).

Summary statistics for artifact density, the USPED and each of the components within the archaeological survey area are shown in Table 5.2. Comparisons of the mean, minimum, maximum, standard deviations, coefficient of variation and skewness for each set of results are compared below.

Table 5.2. - Descriptive statistics for artifact density, RUSLE and factors.

Descriptive Statistics Valid Coef. Standard Variable Units Mean Min. Max. Std.Dev. Skewness N Var. - Error Artifact artifacts/ha 7338 485.0757 0.0000 146375.6 3186.393 6.5689 37.1972 29.8010 Density USPED t/ha/y 7338 5.8626 0.0000 445.8336 9.7914 1.6701 0.1143 15.4361 R factor MJ mm/ha/h/y 7338 907.3524 725.0253 111.5090 96.5345 0.1064 1.1269 -0.1134 K factor t ha h/ha/MJ/mm 7338 0.0137 0.0074 0.0145 0.0018 0.1298 2.08x10-5 -2.7987 LS factor 7338 3.0096 0 146.4858 3.8799 1.2891 0.0453 11.5116 C factor 7338 0.1659 0.0002 1.3356 0.1108 0.6680 0.0013 1.9956 P factor 7338 0.9702 0.8 1.0 0.0689 0.0710 0.0008 -1.9751

The high coefficient of variation associated with artifact density corresponds to the high skewness of the data. LS factor values are similarly skewed. The LS factor is a key influence of soil loss. This is reflected in the high skewness of the USPED data.

(a) (b)

(e) (f)

Figure 5.9. - Mean values per survey tract for (a) R factor, (b) K factor, (c) LS factor, (d) C factor, (e) P factor, and (f) soil loss, within the archaeological survey area.

5.3.2. Regression of Density and Soil Loss using Continuous Data

Regression analysis assumes a linear relationship exists between the predictor variable and the dependent variable. To assess the relationship between the USPED and artifact density distributions in the archaeological survey polygon, the distribution of residuals was compared to the expected normal value using the artifact density value per survey tract as the response variable and the mean USPED value per survey tract as the independent variable. Based on a normal probability plot of residuals, it was determined that a natural logarithmic transformation was appropriate for artifact density and the USPED (Appendix F).

Regression of LN-transformed (artifact density values + 1) and LN-transformed USPED values showed the relationship between artifact density and soil erosion rate to be statistically significant (Table 5.3). Due to the large number of survey tracts with zero artifact density, the density value plus one enabled the inclusion of survey tracts with zero artifacts to be LN-transformed.

Table 5.3. - Regression results of LN USPED and LN (Density +1) demonstrating a statistically significant relationship with 95% confidence (A) F=31.4, p<0.05, (B) t=79.9, p<0.05.

(A) Test of SS Whole Model vs. SS Residual (Stats per tract 11 29) Dependent SS - df - MS - r² F p Variable Residual Residual Residual LN (Density+1) 0.004317 56076.80 7245 7.740069 31.41112 0.000000

(B) Parameter Estimates (Stats per tract 11 29) Sigma-restricted parameterization LN LN LN LN LN LN Effect (Density+1) (Density+1) (Density+1) (Density+1) (Density+1) - (Density+1) - Param. - Std.Err - t - p Beta (ß) - St.Err.ß Intercept 3.199685 0.040049 79.89398 0.000000 LN USPED -0.129882 0.023174 -5.60456 0.000000 -0.065703 0.011723

The variation in artifact density has a very low level of explained variance by the USPED. The coefficient of determination, r2, is 0.004 or only 0.4% (F = 31.4). The standard error of the estimate was 0.02 artifacts per hectare. Although only 0.4% of the variance is explained, the model is statistically significant at the 95% confidence level. As erosion potential increases, artifact density decreases. The scatterplot provides visualization of the association between LN Density and LN USPED (Figure 5.10).

Scatterplot of LN (Density+1) against LN USPED Stats per tract 11 29 16v*7338c LN (Density+1) = 3.1997-0.1299*x; 0.95 Conf.Int. 14

LN (Density+1) 4

-2 -10 -8 -6 -4 -2 0 2 4 6 8 LN USPED

Figure 5.10. - Scatterplot for dependent variable, LN (Density + 1) and independent variable, LN USPED.

5.3.3. Analysis of Density and Soil Loss using Categorical Data

Soil loss values were subdivided into four categories: 0.00 – 0.99, 1.00 – 4.99, 5.00 – 14.99, and 15.00 + t ha-1 a-1. One-way ANOVA determined a statistically significant relationship between soil loss as the independent predictor variable and LN (Density + 1) as the dependent variable, indicating F = 20.4, p < 0.05.

Results indicate that artifact density decreases as soil loss increases. The plot below suggests that high soil loss values are a better predictor of artifact density whereas across the lower two soil loss categories there is no significant difference in artifact density (Figure 5.11).

Categorical USPED; LS Means Current effect: F(3, 7334)=20.357, p=.00000 Effective hypothesis decomposition Vertical bars denote +/- standard errors 3.4

3.3

3.2

3.1

3.0

2.9

2.8

LN(Density+1) 2.7

2.6

2.5

2.4

2.3 1 2 3 4 Categorical USPED

Figure 5.11. - Results of one-way ANOVA for categorical USPED as the predictor for LN (Density + 1). Standard error bars are indicated for each category.

A Fisher Least Significant Difference analysis indicated a statistical difference between three of the four categories, with no difference between categories 1 and 2 (Table 5.4).

Table 5.4. - Results of a Fisher Least Significant with categorized USPED as predictor variable and LN (Density + 1) as the dependent variable.

LSD test; variable LN (Density+1) (Stats per tract 11 29) Probabilities for Post Hoc Tests Error: Between MSe = 7.7201, df = 7334.0 Cell No. Categorical {1} {2} {3} {4} USPED 3.2418 3.2504 2.7981 2.5520 1 1 0.918815 0.000002 0.000000 2 2 0.918815 0.000000 0.000000 3 3 0.000002 0.000000 0.048334 4 4 0.000000 0.000000 0.048334

To examine the sensitivity of category selection, the lowest USPED values (0 < A < 1.0 t ha-1 a-1) were removed. Artifact density was compared to estimated soil loss for areas where landscape degradation is occurring (A > 1.0 t ha-1 a-1). Soil loss values were subdivided into four categories: 1.0 – 2.9, 3.0 – 4.9, 5.0 – 14.9, and 15.0 + t ha-1 a-1. One-way ANOVA determined a statistically significant relationship between soil loss as the independent predictor variable and LN (Density + 1) as the dependent variable, indicating F = 21.74, p < 0.05.

Results indicate that artifact density decreases as soil loss increases. The graph suggests that high soil loss values are a better predictor of artifact density than low soil loss values (Figure 5.12).

Categorical USPED2; LS Means Current effect: F(3, 5645)=21.740, p=.00000 Effective hypothesis decomposition Vertical bars denote +/- standard errors 3.6

3.5

3.4

3.3

3.2

3.1

3.0

2.9 LN (Density+1) LN 2.8

2.7

2.6

2.5

2.4 1 2 3 4 Categorical USPED2

Figure 5.12. - Results of one-way ANOVA for categorical USPED (A > 1.0 t ha-1 a-1) as the predictor for LN (Density + 1). Standard error bars are indicated for each category.

A Fisher Least Significant Difference analysis indicated a statistical difference between all four categories, except between categories 2 and 3 (Table 5.5).

Table 5.5. - Results of a Fisher Least Significant with categorized USPED (A > t ha-1 a-1) as predictor variable and LN (Density + 1) as the dependent variable.

LSD test; variable LN (Density+1) (Stats per tract 11 29) Probabilities for Post Hoc Tests Error: Between MSe = 7.7500, df = 5645.0 Cell No. Categorical {1} {2} {3} {4} USPED2 3.3746 3.0380 2.8735 2.5891 1 1 0.001324 0.000000 0.000000 2 2 0.001324 0.144067 0.000084 3 3 0.000000 0.144067 0.009017 4 4 0.000000 0.000084 0.009017

5.3.4. Specific Stream Power Distribution Related to Artifact Density

The basin scale specific stream power within the archaeological survey polygon was extracted, establishing a minimum, maximum, mean and standard deviation of 687, 2009, 1256 and 267 W/m2, respectively, for the archaeological study region (Figure 15.13). The maximum specific stream power within the archaeological survey polygon is less than the mean specific stream power for the Inachos River and demonstrates decreasing specific stream power values in the downstream direction within the archaeological study polygon. Some variability of values is present, with elevated values generally associated with tributary confluences. No areas of relatively high specific stream power exist within the archaeological survey polygon. Implications for artifact density and stream power variations within the survey polygon are considered in the discussion to follow,

Figure 5.13. - Spatial variability of specific stream power within the archaeological survey polygon.

Chapter 6: Discussion

To better understand the geomorphic changes occurring in the Inachos River basin, this study has developed a quantitative, spatially variable estimate of potential soil loss and specific stream power. The results presented in this analysis demonstrate the sediment yield and stream energy using modern conditions, but do not necessarily address longer term rates of geomorphic processes influencing landscape change over the late Holocene. The spatial variability of current potential soil loss and stream power, however, is useful for the interpretation of spatially variable long-term geomorphic processes occurring within the watershed.

The geomorphic changes occurring in the Inachos River watershed can best be described as event driven. The semi-arid, Mediterranean climate, with episodic hydrological events, make the limestone hillslopes and lower colluvial fans of the region vulnerable to weathering and erosion. Continued differential uplift promotes incision by the Inachos River into the Pliocene-Pleistocene fans that mantle the lower valley walls of the middle watershed. Extensive anthropogenic activity has accelerated erosion, from the introduction of agriculture and herding in the Early Neolithic period to the present-day tilling and ploughing associated with intensive agricultural land use.

Consequently, the average predicted soil loss calculated by the USPED for the Inachos basin is 15.0 t ha-1 a1. The results are positively skewed, indicating values as high as 4287.3 t ha-1 a-1. This variability is reflected in the relatively high standard deviation of 33.0 t ha-1 a-1. The estimated rate computed in this study is comparable to results obtained in Alqueva, Portugal (A = 15.1 t ha-1 a-1) and lower than results obtained for Calabria, Italy (A = 30.6 t ha-1 a-1) and Crete, Greece (77.2 < A < 205.5 t ha-1 a-1) (Ferreira & Panagopoulous, 2014; Terranova et al., 2009; Kouli et al., 2009). According to the continental scale assessment by Panagos et al. (2015), Greece has a mean soil loss rate, variable by region, that is greater than the pan-European average of 2.46 t ha-1 a1, but Italy has the highest mean soil loss rate in the European Union.

In this study, the highest rates of potential soil loss are associated with the steep, mountainous region of the Inachos River watershed. Similarly, the highest specific stream power values occur in the headwaters of the Inachos River. Both are a reflection of the rapid changes in elevation in the headwaters of the watershed, demonstrated by the river profile. The lowest estimates of soil loss and stream power occur in the lowlands of the Argolid plain. Gradient is a key component

55 driving the variability of potential soil loss and stream energy for the basin. The geomorphic processes of the Inachos River watershed can, therefore, be examined by elevation and the corresponding variation in landscape: the headwater processes, the mid-elevation valley processes and the Argolid plain processes.

6.1. Geomorphic Processes of the Upper Watershed

Channelized debris flows dominate the steep, mountainous, headwater channels of the upper watershed. This corresponds to rock-weathering processes occurring on the steep limestone hillslopes as a result of carbonate dissolution. Observed outcrops, with pits and furrows, are characteristic of dissolution. In addition, karst features, such as caves, are present. Roots of maquis vegetation, that covers much of the hillslopes, may also create additional stress, breaking down the limestone. High rates of erosivity in the steep headwater regions were well represented by the R factor suggesting the potential for weakening of surface layers and rapid sediment removal due to more abundant precipitation. Similarly, the LS values were highest in the mountainous regions, representing the effects of hillslopes and channelized flow on erosion and transport of sediment. Consequently, the highest USPED values occurred within the channelized gullies of the upper watershed. Land use identified as natural/pasture at high elevations produced a mean estimated soil loss of 19.6 t ha-1 a-1. Many exposed bedrock surfaces are observed at the upper elevations. These outcrops were misrepresented by the USPED, since these surfaces have been stripped of very limited developed soil. The bare rock reduces infiltration during rainfall events, contributing to rapid channelized runoff and the infill of gullies with eroded sediment. Colder temperatures in the upper elevations, falling below freezing, could also play a role in physical weathering, with freeze/thaw effects occurring during winter months when local precipitation is highest.

Specific stream power values were, similarly, highest in the steep, mountainous headwaters. Significant changes in elevation drive the energy of the Inachos River in this region. Channelized overland flow during episodic, high-energy, precipitation events, mobilizes detached sediment in the upper elevations, producing high stream power which transports significant volumes of sediment to downstream reaches. High sediment supply, and variable and episodic high discharges, replenishes debris material in the steep headwater channels. Reaches of the upper watershed were characterized by large cobble/boulders, and evidence of scouring within the bed 56 and banks of the active channel was observed. Channelized flow generated by large drainage areas results in high stream energy during precipitation events. High-magnitude events that produce bankfull discharge can potentially entrain the large cobble/boulders observed in the active channel, transporting them to reaches at lower elevation.

6.2. Mid-Elevation Geomorphic Processes

Extensive agricultural land use has conditioned the large, indurated alluvial fans at mid- elevations. Ploughing and tilling, associated with agricultural land use and permanent cropping of olive and apricot trees, reworks the large alluvial fan deposits and promotes the mobility of sediment. Terraces and contoured plowing are used to limit soil loss due to water, disrupting typical downslope sediment pathways. As a result, a lack of gullying was observed on the sloping valley sides. Although distinct sediment pathways are not visible (i.e. limited rill and gully erosion), drainage ditches and roadways associated with anthropogenic activity enable downslope sediment transport during high-magnitude precipitation events, potentially accelerating soil loss. The historic extensive and intensive agricultural practices (i.e. tilling/terracing) occurring on the lower elevation slopes would potentially weaken soils in these areas. Some colluvial deposits are present.

Land use areas identified as terraced/contoured produced a mean estimated soil loss of 14.8 t ha-1 a-1. Decreasing R factors, due to relatively lower precipitation than upper elevations, and reduced LS factors, reflecting less gullying in the mid-elevations, has resulted in a reduction of potential soil erosion compared to the upper watershed. Although less than the upper elevations, relief on the Pliocene-Pleistocene fans drives a potential soil loss that exceeds the rate of soil formation. A relatively high LS factor and moderate R factor produces a rate of erosivity and sediment transport that allows soil loss to exceed the rate of physical weathering and soil formation. The extensive landscape degradation is reflected in the exaggerated concave up profile of the Inachos River.

The lowest rates of soil erosion risk obtained at mid-elevations are congruent to the Inachos River floodplain. These areas have high vegetative cover and low gradient. Thick alluvial deposits have filled the valley bottom with Pliocene-Pleistocene sediment. This was indicated by bank

57 stratigraphy and is consistent with the findings of van Andel et al. (1990). Valley infill has provided a relatively flat, cultivatable, agricultural terrain.

Specific stream power within the mid-elevation reaches of the Inachos River is less than the level of energy modelled in the headwaters. Some spatial variability is noted among mid-elevation reaches. These reaches were characterized by a gravel bed, with an abundant sediment supply from the headwater regions producing coarse-grained braided alluvial deposits throughout the lower gradient reaches. Entrenchment of the main river channel within the Kaparelli and Lyrkeia reaches of the valley limits channel splitting. Active anthropogenic mining of the bed material was observed within this channel demonstrating ongoing high sediment fluxes. Breached levees observed at selected river banks (sites R2 and R3), and the presence of rounded to sub-rounded larger clast material at Site S5, suggest that infrequent deposition of over-bank alluvium is helping to maintain some alluvial sediment supply to the floodplain. Apparent flow back-up during flooding events due to large tributary confluences enables this. Increased energy associated with tributary confluences is reflected in the specific stream power model. Site R3 indicates elevated values in specific stream power at a large tributary confluence. Overbank flooding had occurred at Site R3, between the 2015 field season and the 2016 field season, requiring reinforcement of the road parallel to the river. Higher specific stream power values are similarly noted near Sites R5 and R8, where short but steep tributaries emerge from the outcropping adjacent to the south bank. Higher values of specific stream power are also indicated at level road crossings, where engineered levelling of the roadway, perpendicular to the river flow, has resulted in a subsequent drop in elevation on the downstream side of the road. This drop in gradient generates an increase in the specific stream power. High specific stream power, also indicated at R9, is associated with the resistance of the hard red-bed formation throughout the reach. Less entrenchment has occurred in this section of the Inachos River due to bed resistance to erosion. In response to high-magnitude precipitation events, flow appears to expand outward across the 60 m braided plain. Where occasional erosion has occurred through the resistant red beds, deep scours were observed, suggesting high energy.

Extensive valley infill of Pliocene-Pleistocene deposits has enabled greater erosion of river banks during episodic, high energy fluvial events associated with winter floods for most reaches at mid- elevations. The ability of the Inachos River to widen or deepen in response to high discharge

58 events, and the decreasing gradient, have contributed to the comparatively lower stream power downstream of Kaparelli.

6.3. Geomorphic Processes of the Argolid Floodplain

The lowest rates of soil erosion risk are indicated in the downstream portion of the watershed on the Argolid Plain. High vegetative cover due to intensive agriculture, extremely low gradient across the aggraded plain and arid conditions promote relative soil stability in this area, with vegetation roots anchoring the soil more effectively. Prior to engineer training of the downstream reaches, flooding events contributed alluvial sediment to the Argolid floodplain via distributary channels. Downstream reaches easily widen and meander. However, extensive anthropogenic channelizing near Argos controls flow for the downstream reaches to the outlet at Nea Kios.

6.4. Geomorphic Processes within the WARP Survey Polygon

The average predicted soil loss associated with the tracts of the archaeological survey is 5.9 t ha-1 a-1. These results are right skewed, with pixels in steeper survey regions predicting soil loss up to 445.8 t ha-1 a-1 (standard deviation = 9.8 t ha-1 a-1). Lower USPED values correspond to lower LS factors for the majority of the survey.

Simple regression of artifact density and the mean rate of soil erosion for each survey tract indicates a weak, although statistically significant relationship (p < 0.05) with 95% confidence, between artifact exposure and the rate of soil erosion. One-way ANOVA, using four categories of soil loss (0 to 0.9; 1.0 to 4.9; 5.0 to 14.9; and greater than 15.0 t ha-1 a-1), also indicated a statistically significant relationship between soil loss and artifact exposure density. However, results showed that high erosion rates were more strongly associated with low artifact density rates than the association between low erosion rates and high artifact density. One-way ANOVA removing areas of soil stability (i.e. A < 1.0 t ha-1 a-1) produced a more defined downward trend suggesting that the ANOVA analysis is sensitive to category selection. This implies that there may be a distinct threshold where the association between erosion potential and artifact density becomes significant. Above this threshold the spatial arrangement of artifacts and erosion rates are highly associative, whereas below there is too much variance in both variables. Since variability of USPED values was driven primarily by the LS factor for the watershed, it follows that a high LS factor has the highest association with low artifact density. 59

The highest specific stream power values modelled within the archaeological study area are indicated immediately downstream of Site R3. The higher specific stream power values correspond to energy introduced by a large tributary confluence. While weaker banks and some over-bank deposition at this site imply potential lateral movement of the active channel, density records indicate a high density of artifact finds for this area. This suggests that, although overbank flooding occurs, channel entrenchment is sufficient to limit lateral movement. Other reaches demonstrating elevated specific stream power within the archaeological polygon are Site R6, R7 and R9. Site R6 displayed signs of potential lateral channel movement, with weakened levees and engineered reinforcing using gabions to restrict further lateral movement. While high artifact density was found on the north bank of the channel near Site R6, low artifact density was present on the south side of the channel. Fewer marginal artifacts along the south bank suggests that overbank river flow during high-magnitude events may be impacting the number of archaeological finds. Site R7, similarly displays high energy, with bedrock restricting the north bank and a high cutbank on the south side. High artifact density along both banks suggests bedrock and heavy entrenchment has restricted channel movement and thus enhanced preservations of artifacts. At Site R9, extensive over-bank flow was indicated due to resistant bed material. Channel marginal survey tracts along the south bank also indicate a low density of artifact finds. While channel migration is uncertain in this area due to the resistance of bed material, entrainment due to episodic overbank flow may impact the number of artifacts found.

Extensive terracing and plowing from agricultural practices that disrupt the natural transport and deposition of sediment, have obscured distinct pathways of sediment transport (i.e. rills and gullies) within the study polygon. The same may be true for the movement of artifacts. Anthropogenic activities that have resulted in accelerated geomorphic processes within the basin, and the rate of soil loss occurring faster than the rate of soil formation, may also influence the redistribution of artifacts. Consequently, it is easier to predict a lack of surface artifacts in areas of extensive degradation. Similarly, stream energy alone cannot predict the impact on artifact abundance. Extensive Pliocene-Pleistocene deposition of sediment (i.e. alluvial fans) allows easy vertical entrenchment of the Inachos River, increasing its lateral stability. As a result, high stream energy associated with high-magnitude precipitation events is more likely to promote further entrenchment rather than lateral movement. The lack of artifacts observed within the Inachos

River banks support the concept that the Inachos River has degraded into valley infill sediments that pre-date occupation of the watershed.

It is perhaps too easy to think in terms of a causal relationship between contemporary soil surface stability and artifact density. Under this scenario soil erosion would remove and reduce densities assuming some initial spatial similarity in artifact distribution. This is highly unlikely. More likely is an associative relationship between artifact density and geomorphic processes on hillslopes away from the main channel. Occupation and agricultural activity over the millennia would have most likely been on lower gradient slopes and clustered in specific locations favourable for human activity. Thus, low LS factors show a better association with artifact concentrations around centres like Lykeria and Sterna with preferential topography, access to water, and arable land, as well as areas of reduced river disturbances.

Chapter 7: Conclusions

7.1. Summary of Findings

As a result of the Mediterranean climate with strong seasonal differences, the geomorphic processes of the Inachos River watershed are event driven, and influenced by the steep, mountainous terrain and continued differential uplift. Completion of a spatially variable estimation of the soil erosion rate and specific stream power model, in conjunction with rapid geomorphic surveys has led to the following conclusions:

 The watershed attributes that contribute the most to soil surface erosion and variations of hillslope stability in the Inachos River watershed - a mountainous Mediterranean region vulnerable to intense geomorphic processes - are gradient, and rainfall erosivity.

 Since occupation, soil erosion rates have exceeded the rate of soil formation. The average soil erosion rate determined by this study, using modern day conditions, is 15.0 t ha-1 a-1, with the highest rates of erosion occurring in the steep, mountainous regions of the watershed. This is a consequence of physical rock-weathering and carbonate dissolution of steep limestone hillslopes, and channelized gullying in the headwaters of the Inachos River. High levels of potential erosion are also indicated at the valley margins, due to gradient and reworking of Pliocene-Pleistocene deposits that form the large alluvial fans that fringe the watershed valley. Soil stability exists in the lowest flat regions of the Inachos watershed and across the aggraded Argolid plain.

 The estimated soil loss rate of 15.0 t ha-1 a-1 is similar to estimates obtained by Ferreira and Panagopoulous (2014) in Alqueva, Portugal, where A = 15.1 t ha-1 a-1 and lower than estimates by Terranova et al. (2009) for Calabria, Italy (A = 30.6 t ha-1 a-1) and values calculated by Kouli et al. (2009) for eight small watersheds on the island of Crete, Greece, where A values range from 77.2 to 205.5 t ha-1 a-1. The mean soil loss rate calculated by Panagos et al. (2015b) for the European Union is 2.22 t ha-1 year-1, with very high variation. Panagos et al., (2015b) note that the highest soil loss rates for Europe occur in Mediterranean areas with medium to high C factors, and high R factors and LS factors

(Panagos et al., 2015b), such as the Inachos River basin. Soil loss rates exceeding 1 t ha-1 a-1 are considered irreversible, with soil loss exceeding the rate of soil formation.

 The hillslope and fluvial geomorphic processes of the Inachos River contribute to the erosion and transport of sediment in the watershed. Episodic high-magnitude precipitation events generate debris flows that transport sediment downstream from the steep, mountainous hillslopes. The abundant sediment supply from headwater reaches replenishes bedload as the Inachos River responds to continued differential uplift primarily with vertical incision during high-energy precipitation events. The resulting entrenchment typically limits lateral migration of the Inachos River.

 Large sloping alluvial fans of the Pliocene-Pleistocene period fringe the wide, U-shaped valley, supplying soil and sediment to the Inachos River. It is likely that prior to occupation, the middle and lower slopes of these fans were extensively rilled and gullied, Rills and gullying since occupation are minimized by terracing and contoured ploughing, but drainage ditches and roadways continue to enable the downslope transport of sediment. Occasional over-bank flooding of the Inachos River, replenishes some floodplain sediment.

 There is a weak, but statistically significant, association between the rate and intensity of geomorphic processes and site stability, and surface artifact distribution in the Inachos River basin when considering the full range of data. However, clustering of the data into specific categories of erosion potential help discriminate that the very lowest artifact densities are associated with the very highest erosion potentials and vice versa. However, the association is less clear in the middle categories.

 It is tempting to conclude that potential soil loss and specific stream power are good predictors of the spatial variability in artifact density. However, it is most likely there is a strong association with preferential topography, access to water, and arable land, as well as areas of reduced river disturbances.

7.2. Future Research

High resolution satellite imagery covering the entirety of the Inachos watershed would enable a refinement of the variability of the USPED by identifying bare bedrock areas within the headwater regions. While some outcroppings were observed in the field, these areas were misrepresented in the computation of the USPED. Higher resolution would also enable better determination of increased agricultural practices (i.e. terracing and contour ploughing) in areas where 1: 8,000 resolution had been used. More detailed soil analysis may further refine the results obtained in this study. It is unlikely that refinement of the spatial variability of surface soil loss potential would result in a significant difference in the overall computation of mean rate of soil loss compared to results obtained by this study.

Further cluster analysis of artifact density and the USPED may provide additional insight into the relationship between the location of surface artifact finds and geomorphic processes. This study examined the relationship by survey tract, whereas tract clustering may provide valuable information at a scale more relevant to historic human settlement areas.

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Appendices Appendix A. Rainfall Erosivity, R factor

Table A1. Modifed Fournier index and R factor for 10 hydroscope stations of the NE Peloponnese.

Enhydris Decimal Degrees UTM Elevation Length of Station F R Factor Station # Latitude (°) Longitude (°) Easting (m) Northing (m) (m) Record (yrs) F Argos 37.63417 22.73756 653314 4166646 23.60 10 67.09 421.22 Dervernakia 1712 37.78960 22.72386 651787 4183871 265.30 62 97.91 768.29 Fichti 37.71559 22.73275 652722 4175673 95.00 11 76.30 516.81 Frousiouna 37.71667 22.41667 624860 4175324 1020.00 14 172.76 1894.98 Leonti 2141 37.79900 22.59210 640167 4184708 379.70 47 137.58 1319.46 Nemea 2353 37.82641 22.65780 645898 4187850 305.60 58 103.44 838.36 Neochori 2360 37.66605 22.48392 630877 4169799 703.50 56 108.48 904.18 Nestani 2362 37.61210 22.46045 628900 4163781 647.50 62 110.15 926.51 Tiryns 37.59950 22.79960 658862 4162903 9.00 16 82.61 586.41 Tripoli 37.51559 22.38274 622198 4152969 655.00 11 95.03 732.65

Table A2. RMS results from interpolation of R factor point data.

Interpolation Model Type Mean RMS Technique IDW -20.28 390.1 Ordinary Kriging Stable -19.14 371.73 Ordinary Kriging Exponential -19.88 367.08

Appendix B. Soil Erodibility, K factor

Table B1. Soil structure classes, adapted from Wischmeier and Smith (1978) nomograph.

Structure classes Soil structure 1 Very fine granular 2 Fine granular 3 Medium or course granular 4 Blocky, platy, or massive

Table B2. Soil permeability class, adapted from Renard et al. (1997).

Saturated hydraulic Texture Permeability code conductivity (in/hr) Silty clay, clay 6 < 0.04 Silty clay loam, sand clay 5 0.04 – 0.08 Sandy clay loam, clay loam 4 0.08 – 0.2 Loam, Silt loam 3 0.2 – 0.8 Loamy sand, sandy loam 2 0.8 – 2.4 Sand 1 > 2.4

Table B3. Loss on ignition results for two soil samples: natural/pasture and orchard.

Crucible Wet Weight Dry Weight 550 Weight % Soil Sample % OM Weight (g) (g) (g) (g) Sediment Natural Veg. 11.307 13.305 13.250 13.145 0.79% 99.21% Orchard 9.849 11.854 11.761 11.574 1.59% 98.41%

Appendix C. Vegetative Cover, C factor

Figure C1. Map illustrating NDVI, computed using available WorldView-3 imagery. Archaeological survey polygon is indicated by the gold line.

Appendix D. Rapid Field Surveys Table D1. Rapid field survey results and approximation of bankfull discharge.

Drainage Q bf Site Area w bf (m) d bf (m) Slope (m3/s) (km^2) Eq.16 R1 63.25 35.0 3.0 0.008854 297.1 R2 57.10 24.9 3.6 0.012164 268.2 R3 54.11 20.9 2.0 0.022453 126.5 R4 71.50 30.9 3.0 0.016465 304.0 R5 78.41 15.6 4.9 0.010054 207.1 R6 147.80 29.8 4.0 0.008148 333.3 R7 152.25 29.9 4.5 0.010395 419.5 R8 180.91 22.9 6.3 0.008922 437.3 R11 17.20 13.5 2.0 0.042955 89.4 R12 13.00 12.9 4.0 0.025472 168.3 R13 20.96 6.6 5.0 0.115178 150.2 R14 21.64 6.7 3.6 0.035432 73.9 R15 30.97 8.9 3.6 0.033050 101.5 R16 64.79 12.8 3.6 0.018523 135.2 R17 8.58 8.2 4.0 0.080878 134.4 R18 2.47 11.7 1.8 0.037911 63.6 R19 11.81 8.6 2.0 0.051527 54.7

Figure D1. Monitored runoff for the Inachos River by the Ministry of Environment, Energy and Climate Change, Special Secretariat for Water, Supportive Review Report, 21/11/11

Appendix E. Summary Statistics for Specific Stream Power

Table E1. Summary statistics for specific stream power of the Inachos River. Specific Stream Power - Inachos Basin

Mean 2199.4 Standard Error 41.79895 Median 1117.704 Mode #N/A Standard Deviation 3570.57 Sample Variance 12748968 Kurtosis 7.160288 Skewness 2.856902 Range 17062.74 Minimum 101.108 Maximum 17163.85 Sum 16049022 Count 7297 Confidence Level (95.0%) 81.93803

Table E2. Summary statistics for specific stream power of the Inachos River within archaeological study polygon.

Specific Stream Power - Study Polygon

Mean 1265.803 Standard Error 5.604081 Median 1256.35 Mode 1256.36 Standard Deviation 267.4738 Sample Variance 71542.25 Kurtosis -0.038 Skewness 0.132309 Range 1322.04 Minimum 686.54 Maximum 2008.58 Sum 2883500 Count 2278 Confidence Level (95.0%) 10.98964

Appendix F. Regression of USPED against Artifact Density

P-Plot: USPED 4

0 Expected Expected Value Normal -1

-2

-3 -50 0 50 100 150 200 250 300 350 400 450 500 Observed Value Figure F1. Normal probability plot of residuals for USPED.

P-Plot: Density 4

Expected Normal Expected Value 0

-1

-2 -20000 0 20000 40000 60000 80000 1E5 1.2E5 1.4E5 1.6E5 Observed Value

Figure F2. Normal probability plot of residuals for artifact density.

Variable: Density, Distribution: Normal Kolmogorov-Smirnov d = 0.43950, p < 0.01, Lilliefors p < 0.01 Chi-Square: ------, df = 0 , p = --- 10000 9500 9000 8500 8000 7500 7000 6500 6000 5500 5000 4500 4000

No. of observations No. of 3500 3000 2500 2000 1500 1000 500 0 -20000 0 20000 40000 60000 80000 1E5 1.2E5 1.4E5 1.6E5 -10000 10000 30000 50000 70000 90000 1.1E5 1.3E5 1.5E5 1.7E5 Category (upper limits) Figure F3. Test for normality of artifact density frequency distribution.

Variable: USPED, Distribution: Normal Kolmogorov-Smirnov d = 0.27467, p < 0.01, Lilliefors p < 0.01 Chi-Square: ------, df = 0 , p = ---

16000 15000 14000 13000 12000 11000 10000 9000 8000 7000 6000 No. of observations No. of 5000 4000 3000 2000 1000 0 -50 0 50 100 150 200 250 300 350 400 450 500 Category (upper limits) Figure F4. Test for normality of USPED frequency distribution.

Variable: LN (Density+1), Distribution: Normal Kolmogorov-Smirnov d = 0.26156, p < 0.01, Lilliefors p < 0.01 Chi-Square test = 15598.82134, df = 11 (adjusted) , p = 0.00000 3500

3000

2500

2000

1500 No. of No. observations 1000

500

0 -2 -1 0 1 2 3 4 5 6 7 8 9 1011121314 Category (upper limits) Figure F5. Test for normality of LN (Density +1) frequency distribution.

Variable: LN USPED, Distribution: Normal Kolmogorov-Smirnov d = 0.04522, p < 0.01, Lilliefors p < 0.01 Chi-Square test = 466.52112, df = 7 (adjusted) , p = 0.00000 2500

2000

1500

1000 No.ofobservations

500

0 -10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Category (upper limits)

Figure F6. Test for normality of LN USPED frequency distribution.

Table F1. Results of one-way ANOVA for categorical USPED against LN (Density + 1).

Categorical USPED2; LS Means (Stats per tract 11 29) Current effect: F(3, 5645)=21.740, p=.00000 Effective hypothesis decomposition Cell Categorical LN (Density+1) LN (Density+1) LN (Density+1) LN (Density+1) N No. USPED2 - Mean - Std.Err. - -Std.Err - +Std.Err 1 1 3.374597 0.063649 3.310947 3.438246 1913 2 2 3.038029 0.083221 2.954808 3.121251 1119 3 3 2.873487 0.075880 2.797607 2.949367 1346 4 4 2.589052 0.078087 2.510965 2.667139 1271