Land use patterns and climate change—a modeled scenario of the Late Bronze Age in Southern Greece

Geomorphologically, the Peloponnese peninsula (21 550 km²) in Southern Greece is characterized by a mountainous relief and including strong altitudinal gradients on relatively short distances. The highest mountains reach altitudes of more than 2400 m above sea level, only few kilometers away from the coast—e.g. the highest peak of the study area, Mt. Ziria/Kyllini (2374 m), is situated only 20 km away from the coast of the Gulf of Corinth (figure 1).

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Limestones and carbonate rich conglomerates dominate the geology of the study area (Morfis and Zojer 1986). Most of the limestones are strongly karstified, which becomes apparent i.a. in typical geomorphological, hydrological features such as large flat plains (poljes). The largest poljes in the study area, Pheneos, Stymphalia and Phlious/Nemea, which surround the limestone massif of Mt. Ziria have an area extent between 30 and 50 km². Depending on the prevailing climate conditions and the human impact on the drainage system at different periods, these plains were covered by large lakes. Substantial water management practices in the study area are known at least since the Late Bronze Age, like the Late Helladic IIIB dam near Tiryns from around 1300 BCE (Zangger 1994). At the turn of the 19th to 20th century CE, most of the poljes were artificially drained to obtain more flat areas for agricultural use, with Lake Stymphalia remaining the only lake that survived until present. Many slopes in the area show signs of agricultural terracing, in some places even in altitudes above 1500 m. However, neither the age of the terraces nor what was cultivated on them is known (an exception are the terraces of Kalamianos, see Kvapil 2012).

The karst waters draining from the mountains and poljes in the west of the study area supply huge springs (e.g. Lerna, Kiveri, Kefalari) in the Argive Plain, which today covers approx. 150 km² at the north-eastern coast of the Peloponnese. The river Erasinos, issuing from the spring at Kefalari, formed Lake Lerna at the coast south of Argos, which existed at least during a period from approx. 4800 BCE until 800 CE (Jahns 1993, Zangger 1993, Katrantsiotis et al 2019).

The area is characterized by a warm temperate climate with hot and dry summers (Csa after Köppen and Geiger; Kottek et al 2006). Data from meteorological stations on the peninsula shows that the annual rainfall exceeds the critical 250 mm isohyet—that is essential for rain-fed agriculture (see Bruins et al 1986)—everywhere with a decreasing trend towards the east. Following Finné et al (2017) and Weiberg and Finné (2018) the climate during the period in question shows a dry episode of some 20 years at 1250 BCE (±30 yrs), but otherwise generally 'wet' conditions, although probably more volatile than during the previous century. Only after 1200/1170 BCE does a trend towards severely arid conditions set in, with increasingly dry conditions until around 1050 BCE.

Around 1200 BCE, after two centuries of Mycenaean dominance in the Aegean world, the palaces at Mycenae, Tiryns, Midea, Thebes, and other former centers were destroyed and not re-built (Dickinson 2006, Maran 2006, Deger-Jalkotzy 2008). For about a century, certain citadels (e.g. Tiryns in the Argolid, Teichos Dymaion in Achaea) witnessed continued activities; these were taking place, however, on a much smaller scale than before and supposedly not under the lead of a palatial authority (maybe with a short-term exception at Tiryns, see Maran 2006).

During the palatial period, the NE Peloponnese was densely populated with a hierarchy of settlements spread throughout the landscape, on the plains, and in the uplands. It is proposed that Mycenae exercised some control over large parts of NE Peloponnese, and hence dominated other palatial centers in the region (Bennet 2013). The Mycenaean polities (see Drake 2012) were dependent on a secure supply from their agricultural economic system, the basis of which were field crop cultivation (barley, einkorn, and emmer, but diversified during Mycenaean times to include also free-threshing wheat, millet, and spelt) and animal husbandry (goat/sheep, pigs, and cattle; Weiberg et al 2019, with references).

In the present study, only dietary needs are modeled, and the results therefore indicate the minimum needs of the settlements. The prescribed diet is based on estimations by Weiberg et al (2019) and is cereal based, complemented primarily by legumes, fruits and nuts, dairy, and only a limited amount of meat (see supplemental material 1 available online at stacks.iop.org/ERL/14/125003/mmedia). Our modeling approach uses settlement data for the second half of the palatial period (Late Helladic IIIB period: ca. 1300–1200 BCE), derived from three regions covered by differently intensive archaeological surveys (Wright et al 1990, Jameson et al 1994, Wells and Runnels 1996, Lolos 2011, Cloke 2016, see supplemental material 2. No such data exists for the Argive Plain and for this region we have thus used population estimates for the main settlements only. The population of the Argive Plain has been estimated to around 20 000 inhabitants, of which Mycenae, the largest settlement in the region, held around 6400 inhabitants (Bennet 2013, Bintliff 2016). It should be noted, however, that all population estimates are inherently uncertain (see Osborne 2004). They are generally generated from estimations of settlements sizes, more readily identified in excavated areas than from surface reconnaissance. Hectare values for excavated sites are hence more secure, but even excavations do very rarely cover the full extent of the original settlement in one specific phase. To allow for these uncertainties, we have incorporated three population scenarios, calculating 100, 150, and 200 inhabitants per hectare.

The assessment of the environmental suitability for land use activities (i.e. agriculture, arboriculture, pasture, and wood) is based on the integration of expert-knowledge on land use practices with empirical data on environmental characteristics. Both are combined in a fuzzy rule-based system. Fuzzy logic allows to exemplify our assumptions by stating them as linguistic variables (Zadeh 1965). Furthermore, using fuzzy in place of crisp sets helps to classify data where clear borders are hard to establish. Fuzzy methodology is used in different contexts in (landscape) archaeology; examples range from site location analysis (Jasiewicz 2009, Popa and Knitter 2015), over viewshed methods (Loots et al 1999), to chronology (Nakoinz 2012). We employ the R package FuzzyLandscapes (Hamer and Knitter 2018), inspired by Jasiewicz (2011), to analyze a raster based fuzzy rule-based system.

Different raw data were employed: information on slope in degrees is derived from the European Digital Elevation Model (EU Copernicus programme 2016, version 1.1), via GRASS GIS (GRASS Development Team 2017). Information on precipitation are provided by the Greek Special Secretariat for Water and is derived from 68 climate stations located on the Peloponnese. These data are interpolated using multiple linear regression and the independent parameter latitude, longitude, and elevation. Calculations are conducted using R (Core Team 2019) and the packages dplyr (Wickham et al 2019) and raster (Hijmans 2019). The construction of wells requires suitable geological conditions, in particular highly productive porous or fissured aquifers. These information are derived from International Hydrogeological Map of Europe 1:1500 000 (BGR et al 2008). Soils of very high to medium productivity on the Peloponnese—i.e. Fluvisols, Cambisols, Calcisols, and Luvisols (according to Yassoglou et al 2017)—were accessed from SoildGrids250m, a Global 3D Soil Information System, provided by ISRIC World Soil Information (further information on SoildGrids and the corresponding methodology to create the grids is given by Hengl et al 2017). To reduce computation time, all calculations are conducted with a spatial resolution of 75 by 75 m.

Following Hughes et al (2018), we distinguish between four different categories of land use, for which suitability maps are created based on the following assumptions:

• Agriculture, i.e. cereals and legumes, are considered to grow (a) on areas with little to no slope induced erosion (figure 2(A)), (b) in areas with sufficient rainfall for rainfed agriculture (figure 2(C)), (c) on fertile soils (according to Yassoglou et al 2017, 24f.), and (d) in areas with easy access to groundwater resources (figure 2(C)). Terrace farming is not considered here, since it would introduce another level of eco-social complexity. Not considering terraces leads to an underestimation of available land. However, an appropriate representation of terraces also necessitates extended assumptions on nutrient requirements for which we are lacking empirical data. The same holds true for water-management techniques utilized for irrigating the fields. These are likewise not considered.
• Arboriculture, i.e. the cultivation of fruit trees, nuts, olives, as well as vine, has the same requirements as crops, however, the slope is less restrictive (figure 2(A)).
• Pasture: Pastoral activities are only restricted by very steep slopes, following the assumption that these areas are too steep to have vegetation (figure 2(A)).
• Wood: Forest could potentially grow everywhere (see Beuermann 1956).

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Agriculture and arboriculture are labor intensive. Accordingly, the suitability maps are combined with shortest walking distances to archaeological sites (calculated using GRASS GIS; GRASS Development Team 2017): suitable areas that are more than 2 km distant from a site decrease in suitability until the areas are treated as unsuitable at distances larger than 3 km (2.5 km commonly used for agricultural societies; Flannery 1976, Bintliff 2012, Farinetti 2016, Bonnier et al 2019). Pasturage is less labor intensive hence the determining distance for pastoral activities is 5 and 6 km. Wood resources are increasingly difficult to acquire after 5 km distance from a site (figure 2(B)).

The calculation of the areal requirements per site is an extended version of the approach presented by Hughes et al (2018), implemented in the R package LandUseQuantifieR (Knitter et al 2019). It is based on assumptions about the general nutrient, i.e. kilo-calorie (kcal) as well as protein requirements of an individual with an active lifestyle and information on population size (for further details and exact empirical values see Knitter et al 2019). These values are combined with the data on diet characteristics (see supplemental material 1) and information on the nutrient density of plants and animals (for exact empirical data see Knitter et al 2019) in order to assess the minimum area that is required to fulfill the calorie and protein needs of the population.

The calculated areal demands are mapped in relation to the modeled suitability of the area: each grid cell has a certain suitability for agriculture, arboriculture, pasture, and wood (see section 3.2). The algorithm for the distribution of these area requirements is based on a combination of the shortest walking distance to archaeological sites and the Euclidean Norm—the square root of the sum of squares of the distances in each dimension—of suitability for the different land uses. The assignment of land use to the individual pixels is done in an iterative process. The initial value for the iteration equals the Euclidean Norm of the maximal suitability value of a land use category that has minimal distance to an archaeological site. The iteration runs through all cells, ordered consecutively from smallest to largest distance. In each step, the land use category with the largest quotient, i.e. LU_k, is assigned to the cell base upon:

$$\begin{eqnarray*}&&{{LU}}_{k}=\max \left(\displaystyle \frac{{E}_{k,0..i}}{{E}_{k,0..i-1}}\right),\end{eqnarray*}$$

where E is the Euclidean Norm, k is the land use category (i.e. agriculture, arboriculture, pasture, or wood), 0..i are all as k categorized cells including the current cell, and 0..i − 1 are all hitherto as k categorized cells.

In order to test the effects of dry climatic conditions during the Late Helladic IIIB period (see Finné et al 2017, Weiberg and Finné 2018), we employ modern climate station information and relate them to the standardized precipitation-evapotranspiration index (SPEI, McKee et al 1993). The idea is based on the study of Beckers and Schütt (2013) and assumes that general atmospheric dynamics are similar between today and the period under study. The R package SPEI (Beguería and Vicente-Serrano 2017) is employed to calculate SPEI values. Since the index is related to the general precipitation record, negative values indicate times where less precipitation occurs than expected. Accordingly, it can be translated to be a measure of the strength of rainfall variability. Droughts impact the ability to conduct rain fed agriculture. Thus, areas with generally lower precipitation values are more vulnerable to variable rainfall than other areas.

The SPEI is calculated for each year between 1970 and 2000 for a data set of 68 climate stations on the Peloponnese. Subsequently, the SPEI values of the individual stations are interpolated using multiple linear regression with the independent variables latitude, longitude, and elevation. Based on this an areal representation of SPEI for the Peloponnese for each year is achieved. Afterwards the drought maps were summed up to arrive at a drought frequency map for the area. If there were more than 15 droughts, i.e. in more than 50% of the cases in the recorded period, we regarded this area as increasingly unsuitable for rain-fed agricultural purposes (figure 2). Note that the findings have to be treated with caution, since the utilized time series is rather short and its dynamics might not be similar to earlier periods.

Based on this analysis, it gets obvious that drought conditions are heterogeneously distributed throughout the study region, threatening sustainable rainfed agriculture (see figure 3). This threat to experience a drought is implemented as scenario by integrating it as fuzzy variable to the suitability for agriculture, arboriculture, and pasture: the higher the risk of droughts, the lower the suitability of an area for these land use practices (figure 2(C)).

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