Soil erosion susceptibility maps of the Janare Torrent Basin (Southern

Journal of Maps, 2010, 435-447

Soil erosion susceptibility maps of the Janare Torrent Basin (Southern Italy) PAOLO MAGLIULO Dipartimento di Studi Geologici e Ambientali, Universit`a degli Studi del Sannio, via dei Mulini 59/A - 82100 Benevento, Italy; [email protected].

Abstract Soil erosion susceptibility mapping provides a classification of the land surface into zones each of which has a different likelihood, or risk, of experiencing specific soil erosion processes. Such maps are fundamental to land-use planning aimed at the conservation of soil resources. In this paper, 1:46,500 scale maps showing susceptibility to different soil erosion processes (sheet/rill erosion, gully erosion and landslideinduced mass erosion) are presented. The study was carried out in the 10.5 km2 Janare Torrent basin, located in Southern Italy. The basin was first characterized by lithology, morphology and land-use. Landslides, gullies and landsurfaces affected by intense sheet and/or rill erosion were then surveyed and mapped by integrating the analysis of aerial photos and topographic maps with field observations. Using ArcView GIS 3.2, the densities of each type of erosional landform in each lithological, slope, aspect and land-use class were obtained by means of intersection procedures. From densities, weights expressing the susceptibility to soil erosion of each class were calculated using bivariate statistical analysis. The thematic maps were then reclassified according to the calculated weights and overlaid using a GIS, thus calculating the soil erosion susceptibility value for each point in the study area. The ranges of susceptibility values were subdivided into four classes to produce the final susceptibility maps. The procedure is based on the processing of directly surveyed, mapped and interpreted data, is easy to apply, allows frequent updating of the susceptibility assessment, and is scientifically reliable.

(Received 11th December 2009; Revised 30th June 2010; Accepted 13th July 2010)

435 ISSN 1744-5647 doi:10.4113/jom.2010.1116


1.

Magliulo, P.

Introduction

Soil erosion is associated with about 85% of land degradation in the world (Oldeman et al., 1990). Land-use planning is a powerful tool that can be used to reduce the impact of soil erosion. In particular, susceptibility mapping provides a classification of the land ¨ surface into zones of different degrees of susceptibility to soil erosion (Suzen and Doyuran, 2004a) that are dependent on geoenvironmental conditions or “causal factors” (CF) (Ayalew et al., 2005). The use of geographical information systems (GIS) is fundamental in susceptibility mapping (Aleotti and Chowdhury, 1999) and among the GIS-based approaches, statistical methods are the most frequently used. In particular, in bivariate statistical analysis, each CF map is combined with a distribution map of erosional landforms, and weights based on densities of the erosional landforms are calculated for each CF class. According to probability theory (Carrara and Guzzetti, 1995), such a density corresponds to the susceptibility level of a given class to the erosional process responsible of the development of the erosional landform. In other words, the areal density of erosion landforms in each CF class corresponds to the conditional probability of the same erosion landforms to develop in the future, under the same geoenvironmental conditions (Conoscenti et al., 2008b). The higher the areal density in a CF class, the higher the conditional probability that similar erosion landforms could develop in the future in that CF class. Gravity is an important soil erosion agent, where “mass erosion” can be induced by several processes, such as landsliding, soil creeping and solifluction. Among these processes, however, the largest and fastest removal of soil is unquestionably due to landslides (i.e., landslide-induced mass erosion). A vast literature dealing with landslide susceptibility mapping using GIS and statistics exists (e.g., Guzzetti et al., 1999). However there are very few papers dealing with water-induced soil erosion susceptibility assessment, even though water erosion is the most common type of soil degradation (Bridges and Oldeman, 1999). In the international peer-reviewed scientific literature, Conoscenti et al. (2008b) is probably the only example of this topic, using multivariate statistical analysis. The present is unaware of examples of water erosion susceptibility assessments using bivariate statistics in the literature. In this study, bivariate statistical analysis and GIS were used to process data collected using classical techniques of geomorphological analysis, with the aim of producing susceptibility maps for both water-induced and landslide-induced mass erosion of soil at a basin scale. The study was carried out in the Janare Torrent basin, having an area of 10.5 km2 and located in Southern Italy, between 41◦ 13’22”N and 41◦ 17’20”N latitude and 14◦ 36’22”E and 14◦ 38’28”E longitude (Figure 1).

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Figure 1. Location of the study area.

2.

Materials and methods

A 10 m cell digital elevation model (DEM) was created from contour lines and elevation points digitized from 1:10,000 scale maps, using ESRI ArcView 3.2. A slope angle map (Figure 2A) and a slope aspect map (Figure 2B) were automatically derived from the DEM. A field survey was then carried out, using both 1:10,000-scale panchromatic orthophotos and 1:10,000-scale topographic maps. The resulting data were integrated with published data (Agenzia per la Protezione dell’Ambiente e per i Servizi Tecnici , 2005; Magliulo, 2005) and imported into ArcView to produce a lithology map (Figure 2C) and a land-use map (Figure 2D). In the lithology map, the names of the lithological complexes reflect both the type and the relative abundance of the different lithotypes. A geomorphological survey provided the data required to create a map of erosional landforms (Figure 3). Both the areas of each “causal factor” class (i.e., lithological, slope, aspect and land-use class) and erosional landforms were automatically calculated using ArcView. The densities of erosional landforms in each CF class were then calculated. For areas subject to intense sheet and/or rill erosion, the density was obtained by simply intersecting the GIS layer on which these areas were digitized with each thematic ¨ map. For landslides, following a method proposed by Suzen and Doyuran (2004b), buffer zones were added uphill from each landslide scarp with the aim to extract pre-failure conditions. The width of the buffer was set at 10 m, i.e. equal to the cell side extension, as suggested by Conoscenti et al. (2008a). Ten metre buffers were also added around each gully. Both the landslide scarp buffers and the gullies buffers were converted into polygons and intersected with each thematic map to calculate their areal density within each class. 437

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Lithological complexes Gravelly-sandy-silty complex (Holocene) San Lorenzo Gravelly-sandy complex Maggiore (Middle Pleistocene) Arenaceous-sandy-marly complex (Tortonian - Lower Messinian) Clayey-marly-calcareous complex (?Upper Cretaceous - Miocene)

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Figure 2. Thematic maps of the study area: (a) slope angle, (b) aspect, (c) lithology and (d) land-use

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On the basis of the density values, numerical weights (Wi ) expressing the susceptibility of each class to the selected erosional processes were calculated by using the following formula:

Wi = ln

DensClas DensM ap

 = ln 

class−i ACF landf orm−j ACF class−i P CF class−i A P landf orm−j ACF class−i

 

in which Wi is the weight for the CF class i; DensClas is the areal density of the erosional landform type (or buffer) in the CF class i; DensM ap is the overall density of the class−i considered erosional landform (or buffer) in the study area; ACF landf orm−j is the extension of the consideredP erosional landform (or buffer) in the CF class i; ACF class−i is the area class−i of the CF class i; ACF landf orm−i is the Poverall extension of the considered erosional landform (or buffer) in the study area; ACF class−i is the extension of the whole study area. This formula is a modification of that originally proposed by Yin and Yan (1988) and by Van Westen (1993), in which the number of pixels that formed the polygons of the CF classes and/or the erosional landforms were taken into account to calculate the weights, instead of their areas. Each CF map was then reclassified on the basis of the calculated weights. Overlay procedures allowed calculation of, for each cell of the study area DEM, the sum of the weights (i.e. the susceptibility value). The obtained range of susceptibility values was then subdivided in four classes, according to Van Westen (1993). The results were graphically represented by means of susceptibility maps (Main Map). In particular, to create the soil erosion susceptibility map, the layers of landslide scars buffers, gullies buffers and areas affected by severe rill and/or sheet erosion were merged. Among the verification methods reported in the existing literature (e.g. Chung and Fabbri, 2003, and references therein), the one proposed by Magliulo et al. (2008; 2009) was used. The method relies on the assumption that the higher the calculated susceptibility of an area to a given erosional process, the higher the percentage of surface affected by the corresponding erosional landforms. To calculate this percentage, each susceptibility map was intersected with the map on which the spatial distribution of the corresponding erosional landform was reported. The area of each type of erosional landform (e.g., gullies, landslide scars, etc.) in each susceptibility class was automatically calculated and divided by the total area of the susceptibility class. The results were then expressed as a percentage.

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3.

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Geo-environmental framework of the study areaa

Despite the relatively small extent (∼10.5 km2 ), the study area shows fairly high variability in the causal factors (i.e., lithology, land-use, slope angle and aspect). The bedrock of the study area consists of five lithological complexes (Figure 2C). The oldest (“marly-calcarenitic complex”, Upper Cretaceous-Oligocene aged; Bergomi et al. (1975) is made up of reddish and greenish marls alternated with calcarenites. These deposits overthrust both the “arenaceous-sandy-marly complex” (Tortonian-Lower Messinian; Bergomi et al. (1975), consisting of sands, lithoid sandstones and marls, and the “clayey-marly-calcareous complex” (Upper Cretaceous?-Miocene) made up of multicoloured clays and marls with calcareous interbeddings. On these lithological complexes, Quaternary-aged alluvial deposits unconformably rest. The oldest deposits (“gravelly-sandy complex”, Middle Pleistocene in age; Magliulo (2005) consist of polygenic and heterometric gravels with a scarce sandy matrix, while the youngest (“gravellysandy-silty complex”, Holocene in age; Magliulo (2005) are made up of loose gravels, sands and silts. From a topographic perspective, slopes with angles ranging from 6◦ to 15◦ predominate (Figure 2A). Steeper slopes almost exclusively occur in the northernmost sector of the study area, where the most conservative lithotypes crop out. More than 80% of the slopes face from East to South or from South to West (Figure 2B). Flat landsurfaces are rare, small-sized and scattered in the landscape. With regard to land-use (Figure 2D), the study area can be schematically subdivided into two sectors. In the northernmost sector, natural vegetation consisting of forests and shrub and/or herbaceous vegetation associations strongly predominates. The central and southernmost sectors are used for agriculture. Olive groves and vineyards are particularly common. No industrial settlement is present. Finally, the climate is of a Mediterranean type, with hot and dry summers and mild and wet winters.

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4.

Results

4.1

Gully erosion

Magliulo, P.

In the Janare Torrent basin, 30 gullies were detected mainly in the far northern and north-western borders of the study area (Figure 3). The width of these incisions never exceeds 3 m, while the length ranges from 0.06 to 0.4 km. The highest density of gullies, highlighted by the highest weights (Table 1), occur on slopes formed on the clayey-marly-calcareous complex, covered by shrub and/or herbaceous vegetation associations, with slope angles ranging from 6◦ to 10◦ , and facing from West to North. Gullies are rare where the bedrock consists of gravelly-sandy-silty deposits, on which heterogeneous agricultural zones occur, with slope angles ranging from 16◦ to 20◦ and falling in the “flat” aspect class. The gully erosion susceptibility map (Main Map) shows that areas with the lowest susceptibility predominate, accounting for 33.4% of the basin. Most of these areas are north of S. Lorenzo Maggiore village and in the far southern sector of the basin. Landsurfaces with the highest gully erosion susceptibility account for 24% of the study area and are mainly concentrated to the south of San Lupo village. Areas displaying moderate-low and moderate-high gully erosion susceptibility account for the 28.3% and 14.3% of the study area, respectively. The results of the verification procedure confirmed an increase of the calculated susceptibility, with the percentage of the susceptibility classes actually affected by gully erosion. The percentages ranged from 0.3% in the areas with the lowest gully erosion susceptibility to 1.3% on the most susceptible landsurfaces. Intermediate values were obtained for the moderately-low and moderately-high susceptible areas, as expected.

4.2

Sheet and rill erosion

The geomorphological survey revealed the occurrence of 74 areas in the studied basin affected by severe sheet and/or rill erosion (Figure 3). On the panchromatic orthophotos, these areas appeared as whitish spots, often covered by scattered herbaceous vegetation. The field survey revealed that the whitish colour was due to the erosional exhumation of carbonate-rich subsoil horizons and also highlighted the occurrence of erosional micro-features, such as rills, and evidence of water-induced ground loss (e.g., exhumation of plant roots and/or soil pillars). Approximately 14.3% of the basin consisted of these landsurfaces.

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Figure 3. Map of erosional landforms.

The calculated weights (Table 1), pointed out that the areas mostly susceptible to rill and/or sheet erosion were those having a marly-calcarenitic bedrock, covered by shrub and/or herbaceous vegetation associations, displaying a slope angle >20◦ and facing from West to North. Less susceptible areas were those formed on the deposits of the gravelly-sandy-silty complex, having a slope angle ranging from 0◦ to 5◦ and falling in the “flat” aspect class. The sheet and rill erosion susceptibility map (Main Map) shows that 45.1% of the studied basin displayed a low-very low susceptibility (sensu Van Westen, 1993) to these erosional processes. The low and very low susceptible areas are mainly concentrated in the surroundings of the S. Lorenzo Maggiore village and in the far southern sector of the basin. The most susceptible areas accounted for 23.9% of the studied basin and are mainly concentrated at the far north-western and north-eastern borders. The verification procedure highlighted that the percentage of surface of each susceptibility class actually affected by intense sheet and/or rill erosion effectively increased with the calculated susceptibility. In particular, field evidence of these phenomena were

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Journal of Maps, 2010, 435-447 Causal Factors (CF) classes Sheet/rill erosion

Weights (W i) Gully erosion Mass erosion

Total erosion

Slope angle classes 0◦ - 5◦ 6◦ - 10◦ 11◦ - 15◦ 16◦ - 20◦ > 20◦

-0.786 -0.056 -0.235 -0.023 0.644

0.062 0.577 -0.302 -0.801 -0.696

-0.832 0.058 0.148 0.001 -0.121

-0.323 -0.032 -0.215 -0.076 0.540

Slope aspect classes Flat North-to-East East-to-South South-to-West West-to-North

-0.716 -0.040 0.046 -0.027 0.229

-0.310 -1.057 -0.257 0.241 0.832

-1.074 -0.727 0.040 0.197 -0.648

-0.714 -0.101 0.043 -0.001 0.188

-1.078

0.042

0.276

-0.771

0.546 0.470

-0.408 0.687

-0.251 0.101

0.468 0.444

-2.023 -6.859

-1.458

-1.455 -1.998

-1.961 -3.741

-0.713 -0.657

-0.122 -1.446

-0.568 0.153

-0.651 -0.548

0.121 0.799

0.326 0.448

0.426 0.024

0.149 0.733

-0.703

-2.466

Lithological complexes Arenaceous-sandy-marly complex Marly-calcarenitic complex Clayey-marly-calcareous complex Gravelly-sandy complex Gravelly-sandy-silty complex Land-use classes Forests Heterogeneous agricultural zones Permanent crops Shrub and/or herbaceous vegetation associations Urban areas

-3.290

Table 1. Weights of the selected causal factors (CF) classes.

observed on only 3.4% of the total surface of the areas classified as lowly or very lowly susceptible and increased up to 33.5% for areas classified as highly or very highly susceptible.

4.3

Landslide-induced mass erosion

A total of 43 landslides were detected in the studied basin (Figure 3). Flows, rotational slides, both shallow and deep-seated, and soil-slips are the most frequent types. Rock falls are extremely rare, while topples were not observed. The area of the landslides

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ranged between 0.0007 and 0.10 km2 . The total area affected by landslides was 0.81 km2 , which is 7.8% of the whole study area. The highest weights, indicating the highest density of landslides (Table 1) were obtained for landsurfaces formed on deposits of the arenaceous-sandy-marly complex, where permanent crops were present, displaying a slope angle ranging between 11◦ and 15◦ and facing from South to West. The lowest weights were obtained for landsurfaces shaped in the deposits of the gravelly-sandy-silty complex, displaying a slope angle ranging from 0◦ to 5◦ and falling in the “flat” aspect class. The landslide-induced mass erosion susceptibility map (Main Map) indicates a strong predominance of areas displaying a moderately low susceptibility. Among the other susceptibility classes, the landsurfaces characterized by a low-very low susceptibility slightly predominated over those displaying a high-very high susceptibility. Finally, the areas displaying moderately high landslide-induced mass erosion susceptibility accounted only for the 8.2% of the basin area. The percentage of surface actually consisting of landslide scarps was 0.6% for areas displaying a low-very low susceptibility, 2.4% where the calculated susceptibility was moderately low, 2.9% for areas characterized by a moderate-high susceptibility and 4.7% for the most susceptible landsurfaces. Thus, an increase with the calculated susceptibility of the percentage of the susceptibility classes actually consisting of fieldevidence of landslide-induced mass erosion was observed, confirming the correctness of the weights calculation procedure (Magliulo et al., 2008).

4.4

Total soil erosion

Water-induced and landslide-induced mass erosion processes occur together in a given landscape. Thus, an assessment of the susceptibility to total soil erosion in the study area was performed. The calculated weights (Table 1) highlighted that the landsurfaces displaying the highest susceptibility to soil erosion were those having a marlycalcarenitic substratum, covered by shrub and/or herbaceous vegetation associations, displaying a slope angle >20◦ and facing from West to North. The lowest susceptibility was obtained for the landsurfaces occurring on the deposits of the gravelly-sandy-silty complex, whose slope angle ranged from 0◦ to 5◦ and falling in the “flat” aspect class. The soil erosion susceptibility map (Main Map) shows that 37.8% of the studied basin displayed a low-very low soil erosion susceptibility. Areas displaying moderate-low susceptibility were also widespread in the studied area. Moderately-high susceptible

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areas were infrequent, while the landsurfaces with the highest susceptibility to soil erosion accounted for the 22.6% of the study area. The results of the soil erosion susceptibility assessment verifications demonstrate that only 4.4% of the areas characterized by the lowest calculated soil erosion susceptibility are affected by erosional landforms. This percentage increased to 36.8% for the most susceptible areas. Intermediate values were obtained for the classes displaying an intermediate susceptibility.

5.

Conclusions

This study has produced soil erosion susceptibility maps at the basin scale for an area in southern Italy, whose economy is exclusively based on agriculture. In these areas, it is essential to carry out studies aimed at land-use planning for the conservation of the “soil resource”. The interpretation of both detailed orthophotos and topographic maps, performed concurrently with field-surveys, allowed the detection, interpretation and careful mapping of geomorphological evidence for water-induced and mass, landslide-induced soil erosion processes. The large amount of territorial data was easily processed using a GIS. The use of bivariate statistical analysis allowed the calculation of weights expressing the relative propensity of each lithological, land-use, slope angle and aspect class to soil erosion processes. To the knowledge of the present author, bivariate statistics has not previously been used to assess soil erosion susceptibility at a basin scale. Unquestionably, the reliability of the method used in the present study is affected by several factors, such as the resolution of the DEM and the use of slope-angle, aspect and landuse classes based on pre-classified values. However, these shortcomings are common to most of the methods aimed at assessing susceptibility at basin scale. Notwithstanding these shortcomings, the reliability of the bivariate statistics based method used in the present study was confirmed by both the geomorphological coherence of the calculated weights and by the results of the verification procedures. Furthermore, the method is relatively easy to apply and update, as recommended by Aleotti and Chowdhury (1999), allowing frequent updates of the susceptibility assessment procedures.

Software ESRI ArcView GIS 3.2. was used for all analysis and map production. 445


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Acknowledgements The author is grateful to the reviewers Professor Dramis, Dr Entwistle, Dr Pain and Mr MacDonald, whose useful comments and suggestions greatly improved the quality of the manuscript and the readability of the map. The author also gratefully thanks Dr Elena Cartojan and Dr Antonio Di Lisio for useful discussions concerning GIS analysis. Finally, many thanks are due to Dr Mike Smith for the editing work.

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