Modelling Post-Tree-Harvesting Soil Erosion and Sediment Deposition Potential in the Turano River Basin (Italian Central Apennine)

land degradation & developmentLand Degrad. Develop. (2013)

Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ldr.2214

MODELLING POST-TREE-HARVESTING SOIL EROSION AND SEDIMENTDEPOSITION POTENTIAL IN THE TURANO RIVER BASIN

(ITALIAN CENTRAL APENNINE)

PASQUALE BORRELLI1*, MICHAEL MÄRKER2,3 AND BRIGITTA SCHÜTT1

1Department of Earth Sciences, Physical Geography, Freie Universität Berlin, Malteserstrabe 74-100, Haus H, 12249 Berlin, Germany2Department of Agronomy and Land Management, University of Florence, Piazzale delle Cascine 14, I-50144 Florence, Italy

3Heidelberg Academy of Sciences and Humanities, ROCEEH, c/o University of Tübingen, Rümelinstr. 19-23, 72070 Tübingen, Germany

Received: 10 December 2012; Revised: 28 February 2013; Accepted: 28 February 2013

ABSTRACT

The overall aim of the paper is the assessment of human-induced accelerated soil erosion processes due to forest harvesting in the UpperTurano River Basin. The spatio-temporal pattern of soil erosion processes was investigated by means of a spatially distributed modellingapproach. We used the Unit Stream Power Erosion and Deposition model. During the soil erosion-modelling phase, the forest cover changeswere mapped via remote sensing. According to this operation, the forest sectors exploited for timber production amounted to about 2781 ha or9�9% of the wooded surface from March 2001 to August 2011. In this period, the average annual net soil erosion rate estimated by means ofmodelling operations totalled 0�83Mg ha�1 y�1 for all the forest lands. The net soil erosion rate predicted for the disturbed forest lands issignificantly higher than the average value for the entire forest (5�34Mg ha�1 y�1). Estimates indicate a soil loss equal to 8521Mg y�1

(net soil erosion 0�34Mg ha�1 y�1) in the undisturbed forest area (254 km2), whereas the 27�8 km2 of disturbed forest area could potentiallylose 14 846Mg y�1. The paper shows that a disturbed forest sector could produce about 74�2% more net erosion than a nine times larger,undisturbed forest sector. Copyright © 2013 John Wiley & Sons, Ltd.

keywords: clear-cut; soil degradation; apennine environment; GIS; USPED modelling; sustainable forest management

INTRODUCTION

Soil erosion is the result of morphogenetic processes acted bysurface running water, which is accelerated under theinfluence of unwise land use (Felix-Henningsen et al.,1997). The scientific community recognizes it as a principalform of land degradation whose on-site and off-site effectsmay affect very large areas (Rodolfi, 2006). Several environ-ments become prone to soil erosion because of its multiplemanifestations and triggers (Cerdà et al., 2010). The majorityof the publications on soil erosion agree in identifyingthis phenomenon as one of the most pressing environmentalproblems, because it may degrade the ecosystem function(Kirkby et al., 2000), decrease agricultural productivity(Boardman & Poesen, 2006), amplify the hydrogeologicalrisk (Verstraeten & Poesen, 1999) and, in severe cases,displace human populations (Opie, 2000). The dynamicsand the related effects of the soil degradation process led bothnational and international organizations, for example, UN(UNCED, 1992), EU (PESERA, 1999), to address this issue.For most countries, recent studies that focused on soil ero-

sion phenomena are still carrying out their research activitiesin agricultural environments (Boardman & Poesen, 2006).The situation underlines that only minor attention has been

*Correspondence to: Pasquale Borrelli, Department of Earth Sciences,Physical Geography, Freie Universität Berlin, Malteserstrabe 74-100, HausH, 12249 Berlin, Germany.E-mail: [email protected]

Copyright © 2013 John Wiley & Sons, Ltd.

given – both in the past and the present – to soil erosionprocesses in other relevant environments such as forestedareas. This situation contrast the fact that studies havepointed out that about 43% of the total 1094Mha of worldareas affected by soil water erosion are due to deforestation,while 29% result from overgrazing, 24% are due to theabsence of conservative management of agricultural landand the remaining 4% can be attributed to over-exploitationof natural vegetation (Walling & Fang, 2003).The Italian environmental conditions do not significantly dif-

fer from the general European soil erosion research context. Sofar, only few studies have addressed the Italian forested areas(among others, Sorriso-Valvo et al., 1995; Porto et al., 2009).Hence, research has only developed a rough understandingof soil erosion processes and their impact on Italian forests. Thissituation contrasts with the fact that about 29% (8�579 *106 haof the total area) (Inventario Nazionale delle Foreste e deiSerbatoi Forestali di Carbonio, 2007) of the Italian territoryis covered by forest vegetation and is highly exploited bysilviculture and pasture practices (Ciancio & Corona, 2000).In addition, 60% of Italian forests are privately owned, andabout 53% are managed as coppice (ISTAT, 2002). As aresult, the forestry wood extraction practices together with thewildfires (Cerdà, 1998) constitute the major sources of severesoil erosion, which, according to Cerdà & Lasanta (2005),represent an ample window of disturbance.As stated earlier, only very few publications deal with soil

erosion assessment using numerical models in Italian forest

P. BORRELLI ET AL.

areas affected by coppice harvesting. Thus, the aim ofthis study is to contribute to research on human-inducedaccelerated soil erosion processes in Italian forests that areinvolved in the wood supply chain. In the selected site, theforest is largely privately owned, and its current management,mainly represented by coppice extracting, is primarily basedon the land ownership structure with a low involvement oflocal administrative bodies. The study was designed toinvestigate the potential spatio-temporal pattern and dynamicchanges of accelerated soil erosion processes in forests of anintermountain watershed of central Italy. The main objectivesof this study consequently are as follows: (i) to detect forestcover change during the period considered (from March2001 to August 2011); (ii) to model soil erosion predictionby means of a spatially distributed approach [Unit StreamPower-based Erosion Deposition model (USPED)]; and (iii)to quantify tree extraction impact in terms of soil loss.

MATERIAL AND METHODS

Study Area

The study site is located in the Upper Turano River watershed,approximately 58 km northeast of Rome (Figure 1). TheTurano River is a tributary of the Velino River, which in turnis the major sub-tributary of the Tiber River. The total surfaceof the area is about 466�7 km2. Elongated and irregular inshape, the watershed is part of a mountain area of the CentralApennines. The landscape within the study area is typical ofthe intermountain Apennine watersheds, showing an evidentheterogeneity of the geological, geomorphological, climaticand vegetation patterns.The relief distribution shows varying structures with eleva-

tions ranging from 536masl at the lowest point (TuranoLake) up to 1907masl at the highest point (Monte Tarino).

Figure 1. Study area. (a) Tiber drainage basin and study site in the Latium–Abruzz1:25 000 scale technical topograp


The geological framework show a high heterogeneity andcomplexity such as the lithology and tectonics show. Thestratigraphic series distinguishes several rock types coveringthe period from Berriasian to Holocene. Accordingly, the ped-ological substrates are also heterogeneous (Cucchiarelli et al.,2006). However, the forest areas, especially the extensivelyharvested ones, are characterized by less-developed yellow-brown (7�5YR 3/2–10YR 4/4) Endoleptic Cambisols onmid-Miocene torbidite in pelitic-arenaceous facies.According to the meteorological station of Posticciola

(576m asl), the average annual rainfall for the period from1954 to 2012 is 1025mm, while the average annual temper-ature of the studied watershed is around 10�2 �C, rangingfrom 5�8 �C on the Simbruini Mountains to 12�6 �C nearthe Turano Lake valley.Within such climatic contexts with abundant rainfall, low

or completely absent summer aridity, and cold stress fromautumn until spring, the area offers a good environmentfor the growth of mesophile broad-leaved trees. In fact, theforest sector of the investigated watershed is largely coveredby forests, dominated by coppice trees (Acerus sp., Quercuspubescens, Ostya carpinifolia and Quercus sp.), Fagussylvatica and to a lesser extent Castanea sativa.

Approach Overview

To achieve the primary aim, the spatio-temporal pattern anddynamic changes of the specific soil erosion processes in theselected physiographic unit were investigated by means of aspatially distributed modelling. To prepare the modellingphase of soil erosion, we mapped the vegetation changesthat are the direct cause of the instability in the naturalbalance and thus responsible for the accelerated erosionprocess. Accordingly, the study activity is divided intotwo phases:

i border, Italy (source: SRTM data V4). (b) Turano drainage basin (based onhic maps I.G.M.I., 1994).

LAND DEGRADATION & DEVELOPMENT, (2013)

MODELLING POST-TREE-HARVESTING SOIL EROSION AND SEDIMENT DEPOSITION

(1) Identification (via remote sensing operations on satelliteand aerial images) of the Turano watershed forest sectorsthat were subject to clear-cut (March 2001/August 2011).

(2) Assessment of the present soil degradation caused byhuman-induced accelerated water soil erosion usingthe USPED model.

Monitoring of the Turano Watershed Forest Changes

The forest cover mapping and change detection performedfor the Turano watershed comprised three phases: (i) crea-tion of a forest/no-forest mask; (ii) forest change detectionbased on the image differencing change detection technique(Singh, 1989) of multitemporal Landsat images; and (iii)validation and integration of the Landsat forest changedetection using high-resolution aerial images.High-resolution orthophotos acquired during June 2005

were used to create the forest/no-forest cover mask. Opera-tions on the Landsat images included 11 scenes, from 2001to 2011, acquired in the period from June to August (Table I).Chosen the best cloud/shadow-free images (GloVis–http://glovis.usgs.gov), they were pre-processed including image resampling(Williams, 2006), dark object subtraction, radiometric normali-zation (Chavez & Mackinnon, 1994) and qualityassessment labelling. Consequently, the multitemporalimage data set was brought to a common radiometric andgeometric scale to facilitate the forest change detectionanalysis. From the Landsat imagery, we derived the RGBband combination and normalized difference vegetationindex band combination. Thus, by means of imagedifferencing change detection technique (Singh, 1989),supervised classification and raster calculator operations usingENVI 4�7 and ArcGis 9�3, the image differences potentiallyrepresenting forest harvesting activities were acquiredThe 11 Landsat annual forest change masks were vali-

dated and, if necessary, rectified through onscreen visualinterpretation, using orthophotos acquired from 2001,2005, 2008 and 2011. After these images had been importedinto ArcGIS 9.3, the validation operation was carried out byvisual interpretation, using the Landsat forest change masksas reference. Thus, a very accurate forest change detectionmapping was performed.

Selection and Application of the Soil ErosionPrediction Model

For this study, given the size of the study area and theinapplicability of physically based soil erosion models

Table I. Landsat imagery database (L1T standard). Image source, GloVScience Center (EROS) of the United States Geological Survey (USGS)

Image data Sensor Path row

9/06/2001 Landsat ETM+ 190/3127/06/2002 Landsat TM 191/3123/06/2003 Landsat TM 190/3124/06/2004 Landsat ETM+ 191/3127/06/2005 Landsat ETM+ 191/3114/06/2006 Landsat ETM+ 191/31


(de Vente et al., 2006), the USPED empirical–conceptualmodel was employed. It may be described as a three-dimensional enhancement of the one-dimensional RevisedUniversal Soil Loss Equation (RUSLE) model (Warrenet al., 2005) as its application requires some specific RUSLEparameters (Mitasova 1997). Despite refinements and revi-sions, RUSLE and its derivatives are not free from criticism.However, the simple but comprehensive structure of themodel provides a good compromise between applicabilityand reliability of the soil loss estimation. Furthermore, un-like the physically based models, the RUSLE-based modelscan be used for soil erosion predictions of large spatialscale, so that it is very useful for environmental planning(e.g. Institute for Environment and Sustainability in Europeand Natural Resources Conservation Service in USA).To apply the USPED soil erosion prediction model, several

input parameters are necessary. These include a high-resolutiondigital elevation model (DEM) as well as the R, K, C and Pfactors of the RUSLE model (Mitasova, 1997). Accordingto Mitasova (1997), these RUSLE model parameters canbe used in the USPED model to incorporate the impact ofrainfall, soil and land cover to obtain a relative estimate ofnet erosion and deposition. Consequently, USPED builds onthe assumption that the sediment flow at sediment transportcapacity can be estimated by the USLE/RUSLE basic equation:

T ¼ R � K � LS � C � P (1)

Values for these factors are determined from variousequations, tables and nomographs based on field measure-ments (Wischmeier & Smith, 1978). Subsequently, theUSPED model substitutes the LS factor as follows:

LS ¼ Am sinbð Þn (2)

Where: A is upslope contributing area, b is slope angle, andm and n are constants that depend on the type of flow andsoil properties, that is, m= 1�6 and n= 1�3 for prevailing rillerosion, whereas m= n = 1 for prevailing sheet erosion(for this study case, m= 1�3 and n= 1�2).Then, the net erosion deposition ED is estimated as a

change in sediment flow rate expressed by a divergence insediment flow:

ED ¼ div t�sð Þ ¼ div T � cos að Þdx

þ div T � sin að Þdy

(3)

Where: a (in degree) is the slope aspect of the terrain in thedirection of steepest slope.

is portal (http://glovis.usgs.gov) Earth Resources Observation and

Image data Sensor Path row

20/07/2007 Landsat TM 190/3131/08/2008 Landsat ETM+ 190/3125/07/2009 Landsat TM 190/3110/06/2010 Landsat TM 190/3129/06/2011 Landsat TM 190/31


http://glovis.usgs.gov

http://glovis.usgs.gov

Table II. Equations for the calculation of the monthly R factor in the Turano watershed area according to Borrelli (2011)

Months applicability Equation

November, December and January Ei30 = 1�283�Monthly Rainfall� 37�001 R2 = 0�928February, March and April Ei30 = 1�226�Monthly Rainfall� 20�303 R2 = 0�838May, June and July Ei30 = 2�377�Monthly Rainfall + 0�104 R2 = 0�790August September and October Ei30 = 3�815�Monthly Rainfall� 93�842 R2 = 0�883

Figure 2. C factor assigned to the disturbed forest sectors. (a) 0�1 in the first6months after clear-cut, while a thick litter cover is still present on the groundsurface. (b) 0�25 from the 7th to the 12th month after clear-cut. Generally, thisphase falls in the cold months when the soil is devoid of herbaceous cover.(c) 0�12 from the 13th to the 22nd month after clear-cut. During this period,trees regrow with a height <100 cm. The trees cover between 15% and20% of soil surface, whereas herbaceous formations cover about 15%.(d) 0�075 from the 23rd to the 34th month. The trees have a height of approx.<200 cm, and their soil surface cover increases up to 40%; at the same time,the herbaceous vegetation is denser in the areas between the trees. (e) 0�009from the 35th to the 46th month. The area is almost entirely covered by treeplants that may reach up to 300 cm in height. (f) Pre-harvesting value from the47th month onwards. The trees may reach 400 cm in height, and the canopy

cover may be considered to be a mature forest.

P. BORRELLI ET AL.

The rainfall erosivity (R factor; Renard et al., 1997) wascomputed on a monthly scale based on the monthly precip-itation data recorded by the 18 meteorological stations ofthe Italian National Hydrographic and OceanographicService (SIMN). The monthly R factors were calculated withfour linear models proposed by Borrelli (2011) (Table II),which show that, in the study area, the predicted values –when compared with the event-based EI 30 calculated afterRenard et al. (1997) – perform better than other equationsgenerally used for RUSLE application in Italy. In a nextstep, precipitation and thus monthly R-factor values wereregionalized in a GIS environment by using the splineinterpolation method (weight: 0�1; points: 15–18) andraster-calculated operations.The soil erodibility K factor is an empirical parameter

based on the measurements of specific soil erodibility(Wischmeier & Smith, 1978). This parameter is generallymeasured based on some intrinsic soil properties such astexture, organic matter, structure and permeability of the soilprofile. In this study, the soil K factor was calculatedaccording to the equation proposed by Renard et al.(1997), which takes into account the soil grain size charac-teristics. The soil regions were obtained from the Eco-pedological Map of Italy (1:250 000), whereas the soilphysical properties (texture and organic matter) necessaryto apply the equation were acquired from the data publishedby Lorenzoni et al. (1995), soil data provided by theRegional Agency for the Development Services (ARSAA,personal communication), as well as 50 additional topsoilprofiles collected in the field. The modified Eco-pedologicalmap was converted from a polygon-shape file into a rasterwith 10m� 10m cellsize. Finally, the K-factor raster wascomputed applying Equation 4.

K ¼ 0:0034þ 0:0405 exp � 12

log Dg

� �þ 1:659

0:7101

� �2" #( )

(4)Where: Dg is

Dg mmð Þ ¼ exp 0:01X

fi lnmi

� �(5)

Where: K is the erodibility factor in Mg hMJ�1mm�1, Dg isthe geometric mean particle diameter following Shirazi andBoersma (1984), fi is the primary particle size fraction inpercent and mi is the arithmetic mean of the particle sizelimits of that size.According to Wischmeier & Smith (1978), the C factor

describes the cover and management factor that measures thecombined effect of all the interrelated cover and management


variables. In this case study, the following operations wereperformed to represent with major accuracy the space-variantand time-variant forest change:

(1) Subdivision of forest sectors into two classes (dense/notdense tree vegetation), followed by the attribution ofC-factor values taken from literature (Wischmeier &Smith, 1978).

(2) A raster file containing the literature-based C factor wascreated and merged with normalized difference vegeta-tion index-based C-factor rasters estimated followingde Jong et al. (1998) by using four cloud/shadow-freeLandsat images (i.e. October 2000, January 2002, April2003 and June 2003).

(3) To attribute the C factor to the disturbed forest sectors,we used a modified version of the method already testedin Italy, under similar forest-harvesting conditions(Garfì et al., 2006). This method assumes that, in termsof functional efficiency with regard to soil protection, aharvested coppice forest regains the normal function ofcoverage of a mature coppice 4 years after clear-cut.Thus, for the woodland sectors affected by forest-harvesting activities (2001–2011) the C values reportedin Figure 2 were used.

The LS-factor parameter, also called the topographic fac-tor, represents the influence of the terrain topography onthe sediment transport capacity of the overland flow andwas calculated by Equation 2 using ArcGis 9.3. This topo-graphic parameter was outlined from a DEM based on


igure 3. Coppice forest harvested from 2001 to 2011 in the Turanoatershed (data base: Landsat TM/ETM+ images and orthophotosrovided by the National Cartographic Portal (PCN), the Regione Lazio

and the Regione Abruzzo).


contour lines of a 1:25 000-scale topographical map (I.G.M.I. cartography, 1994) and enriched with additional elevationpoints. The Turano DEM with a resolution of 10m wasdelineated using the Topogrid sub-module of ArcInfo9.3 (Hutchinson, 1989). The flow accumulation values,representing the specific catchment area, were derived fromfilled DEMs according to the method D-inf flow directionsof Tarboton (1997). With regard to the P factor, fieldsurveys confirmed the absence of conservation practices inthe harvested areas. Thus, it was assumed to be a constant(equal to 1, i.e. no erosion practices).Finally, the USPED-predicted annual net soil loss and

deposition values of the Turano watershed were estimatedby applying Equation 6, where the ED-monthly values werecomputed for each month for the period between March2001 and February 2012 applying Equation 7. Thus, thetemporal variations of the R factor (monthly) and the Cfactor (semestral, i.e. from March to August and fromSeptember to February) across the study period wereconsidered, while the K, LS and P factors were takenas constants.

ED ¼XJ¼12

j¼1

EDmonthly� �

j (6)

EDmonthly ¼ Rmonthly�K�LS�Csix-month�P (7)

Validation of the Model Results

The results of the predicted soil erosion values were vali-dated using field measurements on soil erosion at an exper-imental site in the Turano watershed. At the experimentalsite, a 1�97-ha first-order watershed harvested in May2008, 85 metallic stakes were placed in the experimentalwatershed with the aim of measuring the soil surfacechanges in harvested forest and under normal undisturbedforest conditions (Borrelli, 2011). The stakes were moni-tored from September 2008 to January 2010. Thus, the soilerosion and deposition values predicted by the USPEDmodel for the same period of field observation were corre-lated to the soil surface level change measured by the stakesto check the validity of the values predicted by the model. Ifmore than one stake was present in a pixel, the mean valuewas used. Moreover, the results of our present-day soilerosion simulation were also validated using a stereophotoanalysis of active erosion processes and resulting landforms.

RESULTS AND DISCUSSION

Forest Dynamics

At present, according to the forest/non-forest map created forthis study (Figure 3), the Turano watershed is mainly coveredby forest vegetation. In diverse forms the forested surfacecovers about 281�9 km2 (60�4%), leaving mainly valleybottom areas uncovered.The remote sensing monitoring analysis showed that

within the Turano watershed the forest sector exploited by


Fwp

timber production covered about 2781�4 ha (1604 clear-cuts)(Table III), mainly composed of Quercus cerris, Ostyacarpinifolia and Quercus pubescens trees and to a lesser ex-tent of Castanea Sativa trees. Most of the harvested treeswere located on hillslopes with slope gradients ranging from15� to 30�, which equals 52% of the total clear-cut surface.By contrast, about 6% of the harvesting activities focusedon forested areas with an inclination greater than 35�.The middle Miocene flysch in pelitic-arenaceous facies isthe lithological unit of the drainage basin where most of theharvesters are concentrated (about 64%).

Soil Erosion Modelling

The application of the USPED soil erosion model providedinformation on potential monthly and annual average soilerosion rates for the period from March 2001 to February2012. The selected period is representative for the currentconditions of climate and land use in the watershed.Monthly variations of the rainfall erosivity as well asseasonal variations of the canopy cover of the forested areaswere considered in the prediction of soil erosion.

Present-day Scenario

The USPED application provided erosion rates on a 10m10m cell basis of the forested area of the Turano watershed.The annual average net soil loss predicted by USPED inthe Turano watershed is 23 368Mg ha�1 y�1, with anaverage area-specific net soil loss of 0�83Mg ha�1 y�1.The modelling results (Figure 4) were separated into sevenclasses of erosion according to the European Soil Bureau(van der Knijff et al., 1999) with the further introduction


Table III. Characteristics of the detected forest harvesters

Year

Forest harvested (ha)

Total Average Min cut Max cut s

2001 314�1 1�3 0�2 13�7 1�82002 293�8 2�7 0�2 39�3 3�42003 215�4 1�5 0�2 20�6 2�72004 194�4 1�9 0�2 11�2 2�22005 202 1�8 0�2 46�4 4�72006 342�2 2�4 0�2 49�7 5�92007 263�1 2�3 0�2 57�8 5�62008 157�6 2�2 0�2 20�6 3�32009 219�4 1�5 0�2 27�2 2�72010 222�6 1�6 0�2 19�1 2�32011 383�6 2�2 0�2 83�2 6�5

P. BORRELLI ET AL.

of two deposition classes [class 1, moderate deposition(0–5Mgha�1 y�1); class 2, high deposition (>5Mgha�1 y�1)].The erosion rate of 0�83Mg ha�1 y�1 corresponds to the

net erosion value, that is, the fraction of eroded soil particlesthat leaves the study area through the drainage network(Mitasova, 1997). Excluding the accumulation values, thetotal soil particle mobilization (gross erosion) modelledat watershed scale yields an annual average equal to331 146Mg y�1, while the average area-specific gross soilloss is 15�3Mg ha�1 y�1.As shown in Figure 4 and reported in Table IV, net

erosion was predicted for 76�7% of the study area, whereasdeposition or no erosion was predicted for the remaining23�3%. Accordingly, 35�5% of the total watershed area(165�4 km2) experiences very low and 23�3% of the arealow erosion rates (class 1 and class 2). These areas aremainly located on flat plains and gentle slopes, especiallythose that are densely vegetated. Moderate erosion appearson about 4�8% of the modelled area (class 3). High (class 4),severe (class 5) and very severe erosion values (class 6) aresimulated for 4�3%, 3�3% and 2% of the watershed area,respectively. Moreover, the areas facing extremely severeerosion (class 7) account for 3�5% of the total area.Most of the forested area surface falls into the low

soil erosion class. The average soil loss in forest that wasleft undisturbed during the 11 years considered is equal to0�34Mg ha�1 y�1. Although this value of predicted net soilerosion is rather high for areas covered by dense naturalforest [0�02 and 0�04Mg ha�1 y�1 (USA) Patrick, 1976;0�15Mg ha�1 y�1 (Mediterranean EU) Cerdan et al.,2006], it is in line with the values generally measured inItalian semi-forested watersheds, without arable lands(0�1 to 5�6Mg ha�1 y�1 – Bazzoffi et al., 1996). In compar-ison to the other Italian landscapes, undisturbed watershedsin mountain environments tend to be little affected by soilerosion processes (van der Knijff et al., 1999). However,in our case study, the situation significantly changes ifharvesting activities are included in the erosion rate calcula-tion. The harvested areas (Figure 5), which are almost totallydevoid of vegetation for about 12months, experience an av-erage net soil loss equivalent to 5�34Mg ha�1 y�1 according


to the USPED model. This value, which apparently does notexceed the rate of 10Mgha�1 y�1 that is generally consideredto be the critical threshold for soil erosion (Morgan, 2005),stands for the net erosion, that is, the portion of the erodedsoil that leaves the modelled area without being re-deposited.Recent studies in Europe converge on identifying net soilloss greater than 1Mgha�1 y�1already unsustainable on thelong term, representing a great concern for maintaining waterbodies quality (Verheijen et al., 2009).If we consider predicted gross erosion values in

undisturbed forests, only 12% of the area shows values thatexceed 5Mg ha�1 y�1, whereas the areas involved in treeharvesting increase to around 70%. The average gross ero-sion in disturbed forest is 43�5Mg ha�1 y�1. This impliesthat a certain amount of sediment is removed from thehillslopes and transported downslope (Figure 5(c)). As sim-ulated with the USPED model, about 86% of the eroded soilparticles are redeposited on the footslopes, while theremaining 14% enter the river network. The predicted severesoil erosion rates in the headwaters, on the other hand, rep-resent on-site effects such as soil degradation, whichinvolves: (i) a breakdown of soil structure (Bryan, 2000);(ii) the reduction of infiltration and water storage capacities(Iovino, 2009); (iii) a decline in nutrients and organic matterin the soil (Hornbeck, 1975); (iv) the alteration of niches foranimals (Pimentel & Kounang, 1998); and (v) the loss ofbiodiversity (Ciancio & Nocentini, 2004). On the otherhand, because drinking water reservoirs in Italy are primar-ily located in forested mountainous watersheds, they receivelevels of environmental pollutants in sharp contrast to theUE Water Framework Directive (Verheijen et al., 2009).A separate analysis of the influence of the different factors

triggering soil loss – that is, age of cut, rainfall, slope gradi-ent, soils – allows us to assess in greater detail how mucheach of these factors contributes to the overall productionof sediment mobilization.The age of the cut in this specific case is obviously the pri-

mary factor influencing the predicted soil erosion values.The low net soil erosion rates of the forestland due to densetree cover rise to a mean value of 26�1Mg ha�1 y�1 duringthe first 12months after clear-cut. The soil erosion rates


Figure 4. Predicted average annual soil erosion/deposition for the Turano watershed (March 2001 - February 2012). Legend shows annual soil erosion/depositionrates in Mg ha�1 yr�1. This figure is available in colour online at wileyonlinelibrary.com/journal/ldr.


remain high during the second year after cut with a meanvalue equal to 16�8Mg ha�1 y�1, but decrease considerablyduring the third year after clear-cut already, showing a meanvalue of 9�6Mg ha�1 y�1. The sediment budget analysisreveals that around 50% of the total soil loss due to acceleratedsoil erosion processes in disturbed forest occurs during thefirst 12months after tree harvesting.


With regard to rainfall, the annual average erosivityfactor computed for the Turano drainage basin totals about2000MJmmha�1 h�1 y�1. The temporal variability of therainfall erosivity across the year shows high values during theautumn period and during late spring. According to themonthlyvalues based on sub-hourly rainfall data (2001–2011), Augustand September were the months with the highest rainfall


Table IV. Soil gross erosion rates predicted by the USPED model for the undisturbed and harvested forest sectors

Class number

Rate of erosion

Erosion risk

Soil loss

Harvested forest Undisturbed forest Total

(Mg ha�1 y�1) (km2) (%) (km2) (%) (km2) (%)

1 0–1 Very low 1�3 6�6 98�6 50�4 100 35�52 1–3 Low 2�6 12�7 62�9 32�1 65�4 23�33 3–5 Moderate 2�3 11�5 11�2 5�7 13�5 4�84 5–10 High 4�3 21�4 7�9 4�1 12�2 4�35 10–20 Severe 4�6 22�8 4�8 2�4 9�3 3�36 20–40 Very severe 2�4 12 3�3 1�7 5�7 27 >40 Extremely severe 2�6 13 7�1 3�6 9�7 3�5

20�1 100 195�9 100 216 76�7Deposition1 1–5 Moderate 2�3 35�4 44�3 75 46�6 16�62 >5 High 4�2 64�6 14�7 25 18�9 6�7Total 6�5 100 59 100 65�5 23�3

P. BORRELLI ET AL.

erosivity (about 33% of the annual total), whereas themaximum erosivity of single rainstorm events was generallymeasured in August. Apart from affecting the temporal inci-dence of the soil erosion process, rainfall erosivity also highlyinfluences the spatial distribution of erosion rates. The spatialdistribution of the annual average rainfall erosivity varies highlywithin the drainage basin area, with 36%difference between thelowest values (1552MJmmha�1 h�1 y�1) and the highestvalues (2415MJmm ha�1 h�1 y�1).After vegetation cover and rainfall, slope gradient is, as

already mentioned, a very important factor in triggering ero-sive processes. In areas with no forest harvesting, alarmingvalues of net soil erosion do not occur and class of low ero-sion is not exceeded (3Mg ha�1 y�1). In the forest-harvestedareas, on the other hand, net soil loss significantly increases(>5Mg ha�1 y�1) at slope gradients between 10� and 15�,becoming severe on slopes steeper than 20�. This is becausesuch topography encourages both inter-rill and rilldenudational processes, which tend to increase as the slopegradient increases. Moreover, in mountainous environments,the combined action of overland flow and the detachmenteffect of raindrop impact greatly increases the soil erosionrates on steep slopes with a sparse soil cover owing to theeffect of the so-called lateral jets of water (Torri & Poesen,1992). Direct morphological and micro-morphometricalobservations at an experimental site within the study area(experimental site EX-01, Borrelli, 2011) suggest thatrainsplash erosion has a greater impact in such environmentsthan wash-out processes, with the lateral jets of waterplaying a major role. These data confirm the findings ofvan Asch (1980), which stated that the rainsplash effect isresponsible for up to 95% of soil particles detached duringthe erosion process in the Italian Southern Apennines.Soil parameters constitute an important source of

uncertainty in watersheds with the spatial and topographicalcharacteristics of the Turano. The grain size analysis (laserdiffraction spectrophotometer method) of the soil samplescollected in the pelitic-arenaceous lithological sector showed


similar distribution patterns of the soil particles in the studyareas. The silt content is the dominant soil fraction (on average51%), whereas the clay content is generally low, ranging from4�9% to 10�4%. The prevalent textural class is silt loam(60�6%). The RUSLEK-factor computation in the Turano wa-tershed reveals that Dystri Leptic Cambisols of the carbonaticlithological units are the most erodible soils (0�038MghMJ�1

mm�1). These soils account for twice as much erodible soil asthe Endolepti Calcaric Cambisols covering the pelitic-arenaceous turbidites (0�017Mgh�1MJ�1mm�1). Accordingto field observations, these K-factor values reveal ‘structuraldeficiencies’ of the RUSLE in estimating the soil erodibilityfactor in such a complex watershed. Because, in both cases,they are largely shallow soils, we assume that the lithologicalsubstratum plays a more determinant role in the erosionalprocess (Bono&Capelli, 1994). In the experimental fieldworkused for the (R)USLE model elaboration, the effect of thelithological basement on soil erosion processes was not con-sidered because it is irrelevant in well-developed agriculturalsoils with gentle slope gradients. On the contrary, neglectingthe lithological characteristics in this kind of mountainous areawould indirectly result in a limitation of the soil erodibilityfactor estimation; inasmuch, the run-off formation is not ascorrectly represented.A final aspect to consider about the environmental im-

pacts of forest harvesting in mountain areas concerns theroadways. In general, a rather dense road network in theareas subject to forest harvesting facilitates the movementof trucks and harvesting machines. By means of visual inter-pretations of aerial photos acquired between 2001 and 2011in a sub-watershed of the Turano drainage basin (Ovito,13�4 km2), approximately 56�8 km (4�2 km/km2) of pathsfor handling machinery were mapped. European (Arnáezet al., 2004) and American studies (Fu et al., 2010) empha-sized the negative effects of these roadways with respectto water quality and threats of soil erosion. The forestroads, even if unpaved, may cause substantial changes inthe local soil properties and the hydrogeomorphic behaviour


Figure 5. Representation of the forest cover change detected in the central sector of the Turano watershed and the result of the USPED model application in a samplearea. a) Area overview (OrthophotoRegione Lazio June 2005; in yellow the harvested areas); b) Orthophoto ‘Volo Italia’ June 2007 (in yellow the harvested area detectedvia remote sensing for the year 2007); c) USPED modelling results for the year 2007. This figure is available in colour online at wileyonlinelibrary.com/journal/ldr.

Figure 6. Forest paths for the movement of lumbermen and harvesting machinery in the Ovito sub-watershed (2009). (Left) Detailed picture of a pathexcavated into soils as well as unaltered rocks. (Right) Longitudinal profile of the path along a steep slope. This figure is available in colour online at

wileyonlinelibrary.com/journal/ldr.


of the hillslopes. This can result in hydrological changessuch as the disruption of natural dynamics of the surfaceand subsurface flows (Wemple et al., 2001). This involvesa general increment of the overland flow that in turn can


increase the sediment yield in the watersheds (Wempleet al., 2001). In addition, the complex topography of themountainous areas hinders the upward transportation of thenecessary harvesting machinery. To deal with this, soils as


P. BORRELLI ET AL.

well as unaltered rocks are excavated to provide solid pathsfor the lumbermen and the harvesting machinery that have ahuge impact on the land (Figure 6).

Validation of the model resultsThe modelling results of the current conditions were asfollows: (i) visually compared with the mapped soil erosionprocesses and (ii) statistically correlated with field measure-ments of soil surface changes. The high resolution of aerialphotos (stereophotos and orthophotos) allowed an analysisof the soil erosion processes, which show good coincidencebetween the modelled high erosion/deposition values and themapped present-day features. This relative analysis was cor-roborated by the comparison of the modelled soil erosionvalues with the ones measured in an experimental first-ordersub-watershed of the Turano watershed. Although it was notpossible to observe the model performance in the net erosionrates prediction by this validation method, the comparison ofmeasured (metallic stakes) and predicted (USPED) gross ero-sion rates showed rather similar values: 3�8mmy�1 for thefield measurements and 2�1mmy�1 for the modelling. In ad-dition, a coefficient of determination equal to 0�618 wasobtained from the statistical correlation between the predictedsoil erosion/deposition values and the measured ones by themetallic stakes (n=13; a< 0�05). In the undisturbed forest,gross erosion rates equal to 0�3mmy�1 for the measuredand 0�002mmy�1 for modelled operation were predicted.The modelling operation brought encouraging results,

which however have to be further improved in terms of anadequate calibration of the C-factor input parameters.Specifically, experimental field observations of the coppiceforest re-growth rates and a major subdivision of post-tree-harvesting C-factor stages may significantly improve themodel performances.

CONCLUSION

The results of this study show how well the erosion preven-tion measures of the examined forest areas counteract thesubstantial soil erosion processes due to intense and highlyerosive precipitation and steep slope gradients. The amountof gross soil loss estimated for the undisturbed forest ismuch lower than the critical threshold currently indicatedin literature. Only about 6% of the undisturbed forest has asoil erosion rate higher than 10Mg ha�1 y�1. However, thissituation changes significantly in the area of wood extrac-tion. The values obtained by the application of the USPEDmodel in sensitive mountain environments emphasizedhow forestry activities such as clear-cut wood extractioncause a noticeable increment in erosion rates. According tothe model results, about 75% of the harvested forest isexperiencing soil erosion. The affected areas lost on average38mm of soil during the 11-year period, thus producingabout 1 330 895Mg of eroded soil, of which 163 311Mgwere transported away in the river network. In turn, thisshows that the predicted soil erosion for the 27�8 km2 ofharvested forest potentially produced an amount of mobilized


sediment equal to 46�2% of the amount mobilized in the total254 km2 of undisturbed forest (gross erosion equal to2 878 372Mg). Moreover, the application of a soil erosion/de-position model such as USPED has shown that it is a suitabletool to make assessment of accelerated soil erosion in forestenvironments. Moreover, this model can also be appliedusing scenarios integrating pre-forest-harvesting and post-forest-harvesting soil conservation techniques. Consequently,future research should focus on the comparison betweenharvested forest areas where clear-cut activities take intoaccount soil conservation practices and areas where theseconservation techniques are neglected. Once the impactof forest conservation techniques is quantified by fieldobservation, they can be parameterized and integrated in themodelling operations.

ACKNOWLEDGEMENTS

This project was funded by the Elsa Neumann fellowshipprogramme of the Federal State of Berlin and the GermanResearch Foundation (DFG) in the context of the Clusterof Excellence 264 TOPOI. We thank two anonymousreviewers for their constructive criticism.

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Documents

Modelling Post-Tree-Harvesting Soil Erosion and Sediment Deposition Potential in the Turano River Basin (Italian Central Apennine)