Mapping the State and Risk of Desertification in Italy by means of Remote Sensing, Soil GIS and the EPIC Model. Methodology Validation on

the Island of Sardinia, Italy.

*Edoardo A.C. Costantini, Michele Bocci, Giovanni L’Abate, Andrea Fais, Gabriele Leoni, Giosuè Loj, Simona Magini, Rosario Napoli, Pasquale Nino, Massimo Paolanti, Luca

Salvestrini, Fabrizio Tascone, Ferdinando Urbano (* Contact Person). Experimental Institute for Soil Study and Conservation, p.za D’Azeglio 30, Firenze, 50121, Italy.

e-mail: www.soilmaps.it

Abstract Studies have already been conducted on assessing the risk of desertification in Italy at continental, national and regional level. They combine different climatic, soil, vegetation, and socio-economic attributes to estimate pressure on land and state of soil and vegetation. This study is aimed at presenting the methodology used in the creation of the new atlas of the risk of desertification in Italy. The methodology is based on the use of indicators of pressures, state and response. Response indicators are desertified, sensitive and vulnerable lands. Desertified lands are intended here as tracts of land occurring in dry climates and showing “functional sterility”, that is areas where agriculture and forestry are currently no longer either economically or ecologically sustainable. In vulnerable lands, environmental characteristics are close to that of a desertified area, but some factors, e.g. vegetation cover or irrigation, successfully mitigate the process of desertification. Sensitive lands are surfaces where processes leading to desertification are active, although lands do not yet have functional sterility. Key words : atlas, desertification, methodology, Italy.

Introduction

Studies regarding mapping the desertification risk in Italy have already been carried out in

the ambit of maps produced at global (Eswaran and Reich, 1998) and continental scales (DISMED, 2003). A good first appraisal of the magnitude of the problem can also be obtained by means of the satellite image of the Mediterranean basin, where areas deprived of vegetation are clearly identifiable all around the Mediterranean sea (Fig.1). At national scale, a first approximation of the areas at risk of desertification in Italy, based mainly on climatic and socio-economical data, was produced in the year 1998 by the National Technical Services. The map indicated only small areas of southern Italy and of the major islands (Sardinia and Sicily) as being at risk (Comitato naziona le per la lotta contro la desertificazione, 1998). More recently, using the MEDALUS approach (MEDALUS, MEditerranean Desertification And Land Use, Kosmas et al., 1999) the European Environment Agency (DISMED, 2003) worked out a new map of the desertification risk in Italy at 1:250,000 scale. The areas at risk of desertification were singled out by means of three indices, i.e. quality of climate, soil and vegetation. The map indicated as being at risk large parts of central and southern Italy, as well as of the Po Plain and the Alps. On the other hand, some areas of southern Italy which were formerly considered at risk were now excluded.

Fig. 1 Satellite image showing areas deprived of vegetation all around the

Mediterranean sea. Arrow indicates the island of Sardinia. Other maps of desertification risk have been drawn up/compiled at regional level, using

modified MEDALUS approaches and data coming from regional databases (UNCCD-CRIC, 2002).

In the year 2003, two research Institutes of the Ministry for Agricultural and Forestry Policies, the Experimental Institute for Soil Study and Conservation (ISSDS) and the National Institute of Agricultural Economics (INEA) were entrusted by the Italian Ministry for the Environment with the responsibility of preparing a map of the desertification risk in Italy at the semi-detailed scale (reference scales 1:100,000 – 1.250,000).

The aim of this work is to present the rationale of the research, the methodological approach, and the application and validation experience. The territory used to validate the methodology was the island of Sardinia and the desertification processes considered were: i) soil losses by water erosion and mass movements, ii) salinization, iii) urbanization.

Rationale of the work

The most widely accepted definition of desertification is, at present, the one given by the

United Nations Convention to Combat Desertification (http://www.unccd.int). It defines desertification as “land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities”. Land is here defined as the terrestrial bio-productive system which includes soil, vegetation, other biota and the ecological and hydrological processes that operate within the system. Land degradation means the reduction or loss of the biological and economic productivity and complexity of irrigated and non- irrigated agricultural land, pastures, rangeland, forest and woodland. Arid, semi-arid and dry sub-humid areas are the territories, other than polar and sub-polar regions, in which the aridity index, that is the ratio of annual precipitation to potential evapotranspiration, falls within the range from 0.05 to 0.65.

This set of definitions is tailored for evaluations produced at a global or continental scale, but it is too vague for studies at national or local scales. The scaling down needs a more precise conceptual framework, along with more detailed delimitations of the areas susceptible to desertification.

The concept of land degradation, which means impairment of soil qualities, must be separated from that of land desertification, which is a particular kind of land degradation, implying the loss of capability for a sustainable agricultural and forestry production, this loss being irreversible or having very little chance of reversibility. A desertified area is a tract of land occurring in dry climates and showing “functional sterility”, that is an area where agriculture and forestry are currently no longer economically or ecologically sustainable. A desertified area could be cultivated, but the economic and social input needed would be so high that only rarely could it be put into practice. The level of economic and social input can obviously vary from country to country, according to the minimal accepted tenure of life and environmental awareness of the population. Taking as a reference the well know and widely accepted Land Capability classification system (Klingebiel and Montgomery, 1961), soils with functional sterility belong to the last class, the eighth, that is soils which are left set aside, and only considered for ecological purposes. As a consequence of the relative assessment of desertified lands, a proper evaluation should be validated with an inventory of benchmark lands, clearly showing field evidence of desertification.

Fig. 2 South of Italy: bare lands on steeper slopes are desertified, where still

protected by woodland they are vulnerable. Fire or excessive forest exploitation would subject vulnerable areas to rapid desertification.

In transitional zones, such as in Mediterranean environments, desertified lands are very

often intermingled with areas at risk of desertification. An area at risk of desertification is a tract of the earth surface which is vulnerable or sensitive to the processes of desertification. In a vulnerable land, environmental characteristics, like soils for instance, are close to that of a desertified area, but some factors, e.g. vegetation cover or irrigation, successfully mitigate the

process of desertification (Fig. 2). On the other hand, a sensitive land is a surface where the process leading to desertification is active, although the land does not yet have functional sterility (Fig. 3).

With reference to the PSR framework (Pressure, State, Response), desertified lands are the ecosystemic responses to the pressures on the environment. State factors of land desertification are the environmental characteristics (indicators, Enne and Zucca, 2000) which regulate the occurrence of desertification processes, namely climate, soil, vegetation, water availability, human pressure and management. Pressure factors are the processes of desertification, e.g. soil erosion, contamination, salinization, and drought. Urbanization is considered a desertification process in the sense that it induces the irreversible loss of agricultural and forestry functionality, although the land acquires other functions. A major concern is produced by the diffusion of urbanized areas where the problem of lack of water is more acute. This is the case of the coasts of the Mediterranean sea. If we compare the distribution of cities around the Mediterranean basin (Fig. 4) to the satellite image of areas deprived of vegetation (Fig. 1) we can clearly identify the tracts of the coast where there is interaction between urbanization and loss of the vegetation cover.

Fig. 3 An area of Sardinia sensitive to desertification because of excessive grazing

and strong sheet water erosion.

Choice of the indicators of desertification The indicators used changed according to the step of the evaluation (Table 1). The first

step was the delimitation of the area of the country at potential risk of desertification. The indicators used were aridity index, pedoclimatic regimes and climatic regions. The second step consisted in the mapping of the risk at reference scale.

The choice of the indicators was made taking into account the parameters suggested by previous experiences (Enne and Zucca, 2000), as well as the actual possibility of having specific databases at national scale.

Fig. 4 Satellite image showing distribution of main cities all around the

Mediterranean sea. Table 1. Indicators considered in assessing the areas sensitive and vulnerable to the

risk of desertification and drought in Italy. PRESSURE STATE RESPONSE

Slope Rooting depth

Vulnerable lands Soil characteristics

Presence of rills and gullies Sensitive lands

Vegetation cover Normalized Difference Vegetation Index (NDVI)

Desertified and sensitive lands

Number of cattle and sheep Risk increase Protected areas Risk mitigation

SURFACE DENUDATION BECAUSE OF

WATER EROSION

Human activities Burnt areas Risk increase

URBANIZATION Urban areas Desertified lands Distance from the sea Elevation

Vulnerable lands SALINIZATION

Irrigated vulnerable lands Sensitive lands Climatic region Aridity index Soil moisture and temperature regimes (following Soil taxonomy)

Potentially vulnerable lands (national scale)

DROUGHT

Mean annual number of days when the soil is dry Vulnerable lands (regional scale)

Delimitation of the area in Italy at potential risk of desertification We elaborated a series of 656 meteorological stations with the aim of characterizing

aridity index and 140 stations to evaluate pedoclimatic regimes of the whole country. We also considered pedoclimatic regimes because soil is capable of storing water and mitigates drought risk and temperature excursions in the root zone. Soil classification according to Soil Taxonomy (Soil Survey Staff, 1999) considers soil moisture and temperature regimes: “aridic”, “xeric”, “dry xeric” and “ustic” soil moisture regimes can be identified for areas with varying degrees of potential water deficit (Table 2). Moreover, soils with “thermic” and “hyperthermic” temperature regimes refer to lands with high temperatures in the root zone, which can enhance the decaying of the organic matter, especially under agricultural land use (Table 3). Soil moisture regime was obtained using EPIC daily outputs (see below), soil temperature regime from the algorithm proposed by Costantini et al. (2001), which makes use of annual mean air temperature and soil water field capacity.

The aridity index was obtained applying the Hargreaves-Samani methodology (1982) on the long term mean monthly temperature and rainfall values of the 656 meteorological stations, and it was spatialized with the Inverse Distance Weighting method (Fig. 5).

The aridity index map and pedoclimatic regimes were matched with the climatic regions of Italy (Finke et al., 1998; Righini et al., 2001). Potential desertification risk resulted in all climatic regions with a Mediterranean type climate (Fig. 6). The area seems to match well with the satellite image of Figure1.

Table 2. Soil moisture regimes (Soil taxonomy 1999, simplified) Aridic: i) the Soil Moisture Control Section (SMCS) is completely dry for more than 180

d; ii) the SMCS is completely moist for less than 45 d in the 4 months following the winter solstice.

Dry xeric: i) exclusive of aridic conditions; ii) the SMCS is completely dry for more than 89 d; iii) the SMCS is completely moist for more than 44 d in the 4 months following the winter solstice.

Xeric: i) exclusive of aridic and dry xeric conditions; ii) the SMCS is completely dry for at least 45 d in the 4 months following the summer solstice, iii) the SMCS is completely moist for more than 44 d in the 4 months following the winter solstice.

Ustic: i) exclusive of aridic, dry xeric and xeric conditions; ii) one or more layers of the SMCS are dry for more than 89 d.

Udic: remaining conditions. Table 3. Soil temperature regimes at 50 cm depth (Soil taxonomy 1999, simplified) Cryic: the annual mean soil temperature is lower than 8 °C and the summer temperature is

lower than 15 °C Frigid: the annual mean soil temperature is lower than 8 °C Mesic: the annual mean soil temperature is between 8 and 22°C Thermic: the annual mean soil temperature is between 15 and 22°C Hyperthermic: the annual mean soil temperature is higher than 22 °C The Iso prefix is added when mean summer and winter soil temperatures differ less than 6

°C.

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Genoa

Aosta

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Venice

CagliariCatanzaro

Rome

Bari

Milan

Ancona

Pescara

Perugia

Trieste

Bolzano

Bologne

Palermo

Florence

NaplesPotenza

ARIDITY INDEX

0.20 - 0.50

0.50 - 0.65

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>1.00 METEOROLOGICAL STATION

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Fig. 5 Aridity index and meteorological stations

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CLIMATIC REGION

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Fig. 6 Pedoclimatic drought risk and climatic regions of Italy

Calibration of the Epic model

The EPIC (Environmental Policy Integrated Climate, former Erosion-Productivity Impact

Calculator, Sharpley and Williams, 1990) model (version 3090) was used on a daily time step. Daily inputs were minimum and maximum air temperature, rainfall, and radiation. Potential evapotranspiration was calculated according to Priestley-Taylor (1972). Monthly means came from long-term series of data, daily statistics from long-term or a limited number of years. The reference crop was a stable meadow, grown without irrigation and maintained at about 0.20 m high. Optimal temperature for plant growth was 20 °C for warmest climate and 15 °C for the others. Soil inputs were horizon depth, texture, rock fragments, bulk density, water content at field capacity and wilting point, and organic carbon. Field capacity and wilting point were measured in laboratory or estimated according to Baumer’s method (provided by EPIC). Soil bulk density was measured in the field or estimated by EPIC. Soil horizons depth was arranged to have four layers corresponding to the moisture control section of each studied

soil. EPIC was initialized at field capacity and, using the weather generator, EPIC ran for a 50-year period of time. Daily soil moisture of the layers forming the control section was extracted from the output file of EPIC with a software created with visual basic language

A set of linked spreadsheets of Excell were used to: i) draw graphs of the climate and soil water content of each layer; ii) elaborate daily data and classify soil moisture and temperature regimes of the 50 years generated by Epic. The methodology was tested in experimental fields, where soil water content was measured weekly or biweekly (Costantini et al., 2002) and applied at regional and national scales on benchmark soils and meteorological stations.

Use of the Epic model

Soil moisture regime and average cumulative days per year when the soil moisture control

section is completely dry were estimated by means of the EPIC model, the National Soil Database and long term climatic data. The EPIC model was also used to estimate the mean annual number of days when the soil moisture control section is dry. A multiple regression deriving the number of days from long term mean air temperature, annual rainfall and available water capacity (difference between soil water at field capacity and wilting point; AWC) was found (R2 = 0.55; F<0.00001; n=260). The regression was applied to the AWC of 13,000 soils and to air temperature and rainfall of the soil sites, obtained through ordinary kriging of the long term annual values of 1067 (total rainfall) or 944 (mean temperature) meteorological stations. The indicator “mean annual number of days when the soil moisture control section is dry” was finally added to each soil of the National Soil Database.

Application of the method to the island of Sardinia

The National Soil Database, having data of 792 profiles in Sardinia, was queried to select

soil characteristics used to qualify the state indicators “rooting depth”, “presence of rills and gullies”, and “mean annual number of days when the soil is dry” (Figs. 7 and 10).

Soil information was generalized using land components of the land subsystems (1:250,000), linked to the National Soil Database. Land subsystems are geographical units with a characteristic pattern of lithologies, morphologies and land uses (Costantini et al., 2003). A land component is a part of a land system, not delineated in the GIS but stored in the database, formed by a combination of morphology, lithology and land use, with legends created on the basis of pedolandscape perception at the reference scale.

NDVI analysis of “natural areas” (woodlands and pastures) was calculated on Landsat TM images at 30 m resolution. The result was classified in different land typologies by means of field evidence and sample points on aerial photographs. Desertified and sensitive lands were selected from the classification (Fig. 8).

An original land cover database was created at 1:100,000 by means of aerial photointerpretation, which was used in combination with the other databases as well as in the delineation of urban areas and irrigated lands. A Digital Elevation Model at 20 m resolution was used to calculate slope, distance from the sea and elevation.

The remaining state indicators came from specific databases.

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Fig. 7 Lands sensitive and vulnerable to erosion because of soil characteristics. State indicators “rooting depth”, “presence of rills and gullies”, and “slope”

Fig. 8 Desertified and sensitive lands because of lack of vegetation cover. State indicator “NDVI analysis”

Fig. 9 Lands sensitive and vulnerable to salinization. State indicators “distancefrom the sea”, “elevation”, and “irrigated lands”

Fig. 10 Lands vulnerable to drought. State indicator “mean annual number of days when the soil is dry”

0 50 10025Kilometers

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3 Site: IrgoliSensible area because of grazingon steep slopes and shallow soils

Site: TerteniaSensible area because of fires on steep slopes and shallow soils

PHOTO-ATLAS

0 50 10025Kilometers

Site: TeuladaSensible area because of fires and overgrazing on shallow soils

Fig. 11 GIS of the “true points”

Validation with “true points” on the field The validation of the method was obtained by means of a set of field observations, stored

in a specific database. Each observation was georeferentiated and briefly described in terms of state and causes of desertification. A picture of the landscape and an aerial photograph of the site was added to the site information. The observation was linked to the geographic database and therefore had information about geology, morphology, land use and soils of the sites (Fig. 11).

Conclusions

The elaboration of the atlas of the desertification risk in Italy is not completed yet,

however, the first results are promising. NDVI analysis resulted a powerful tool to single out desertified and sensitive lands, especially because it was combined with a land use database, however its use was limited to the not ploughed soils.

The evaluation of sensitive and vulnerable lands because of water erosion, through the analysis of soil characteristics, gave important information both in agricultural and not agricultural areas, but envisaged a numerous and well organized soil database, harmonized and linked to a proper geography at the national scale.

The use of the EPIC model permitted the evaluation of the drought risk at the national level, through the estimation of the soil moisture and temperature regimes, as well as at the

regional level, by means of the estimation of the long term mean annual number of days when the soil is dry. The evaluation of potentially vulnerable lands through the first pedoclimatic indicators, in particular, resulted far more accurate than that obtained with the traditional climatic indicator, the aridity index. On the other hand, the indicator “mean annual number of days when the soil is dry” was particularly useful for providing information about the land vulnerability in agricultural areas, where NDVI analysis could not be performed.

In conclusion, it must be stressed that the success of the proposed methodology relied on the relevance and straightforwardness of the indicators used at the different scales, as well as on the field survey and storage inside a GIS of tangible benchmark sites, where desertification state and risk were clearly identified, explained and shown using pictures.

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