Established in 1977, the International Center for Agricultural … · 2016. 10. 6. · ICARDA-18/500 /October 2008 About ICARDA and the CGIAR Established in 1977, the International

ICARDA-18/500 /October 2008

About ICARDA and the CGIAR

Established in 1977, the International Center for Agricultural Research in

the Dry Areas (ICARDA) is one of 15 centers supported by the CGIAR.

ICARDA’s mission is to improve the welfare of poor people through

research and training in dry areas of the developing world, by increas-

ing the production, productivity and nutritional quality of food, while

preserving and enhancing the natural resource base.

ICARDA serves the entire developing world for the improvement of lentil, bar-

ley and faba bean; all dry-area developing countries for the improvement of on-

farm water-use efficiency, rangeland and small-ruminant production; and the

Central and West Asia and North Africa (CWANA) region for the improvement of

bread and durum wheats, chickpea, pasture and forage legumes, and farming

systems. ICARDA’s research provides global benefits of poverty alleviation through

productivity improvements integrated with sustainable natural-resource manage-

ment practices. ICARDA meets this challenge through research, training, and dis-

semination of information in partnership with the national, regional and internation-

al agricultural research and development systems.

The Consultative Group on International Agricultural Research (CGIAR)

is a strategic alliance of countries, international and regional organiza-

tions, and private foundations supporting 15 international agricultural

Centers that work with national agricultural research systems and civil

society organizations including the private sector. The alliance mobi-

lizes agricultural science to reduce poverty, foster human well being,

promote agricultural growth and protect the environment. The CGIAR generates

global public goods that are available to all.

The World Bank, the Food and Agriculture Organization of the United Nations

(FAO), the United Nations Development Programme (UNDP), and the International

Fund for Agricultural Development (IFAD) are cosponsors of the CGIAR. The World

Bank provides the CGIAR with a System Office in Washington, DC. A Science

Council, with its Secretariat at FAO in Rome, assists the System in the development

of its research program.

Integrating Expert Knowledge in GIS to Locate Biophysical Potential

for Water Harvesting

Methodology and a Case Study for Syria

Eddy De Pauw, Theib Oweis and Jawad Youssef

International Center for Agricultural Research in the Dry Areas

Copyright © 2008 ICARDA (International Center for Agricultural Research in the Dry Areas)

All rights reserved.

ICARDA encourages fair use of this material for non-commercial purposes, with proper citation.

Citation:E. De Pauw, T. Oweis, and J. Youssef. 2008. Integrating Expert Knowledge in GIS to Locate Biophysical Potentialfor Water Harvesting: Methodology and a Case Study for Syria. ICARDA, Aleppo, Syria. iv + 59 pp.

ISBN: 92-9127-207-2

International Center for Agricultural Research in the Dry Areas (ICARDA)P.O. Box 5466, Aleppo, Syria. Tel: (963-21) 2225112, 2225012, 2213433, 2213477Fax: (963-21) 2225105, 2213490E-mail: [email protected]: www.icarda.org

The views expressed are those of the authors, and not necessarily those of ICARDA. Where trade names are used,it does not imply endorsement of, or discrimination against, any product by the Center. Maps have been used tosupport research data, and are not intended to show political boundaries.

This report is an output of the project ‘Assessment of Water Harvesting and Supplemental Irrigation Potential in Aridand Semi-Arid Areas of West Asia and North Africa’, funded through the CGIAR initiative on ComprehensiveAssessment of Water Management in Agriculture. It describes results from Activity 8 of the project: Developmentand testing of methodologies and models for country-level identification of areas with potential for water harvestingand supplemental irrigation.

ACKNOWLEDGMENTS

The authors are pleased to thank the following for their valuable contributions to this study:Mr Bashar Nseir for his support with GIS programming and area calculations; Dr AdrianaBruggeman for crucial inputs in refining earlier drafts; Ms Jennifer Kinoti, Dr Nabil Sghaier andDr Weicheng Wu for their very useful suggestions during the review process.

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CONTENTS

Abstract iv

1 Introduction 1

2 Evaluating suitability for on-farm micro-catchment systems 22.1 Overview 2

2.1.1 General approach 22.1.2 Evaluated systems 2

2.2 Evaluating suitability of biophysical factors relevant to the selected systems 32.2.1 Datasets 32.2.2 Suitability evaluation procedure 42.2.3 Criteria and constraint values 102.2.4 Adaptation for specific micro-catchment systems 112.2.5 Enhanced visualization 13

2.3 Results 14

3 Evaluating suitability for macro-catchments 163.1 Methodology 16

3.1.1 Overview 163.1.2 Evaluating suitability for catchment use 163.1.3 Evaluating suitability for agricultural use 183.1.4 Combining suitability for catchment and agricultural uses 18

3.2 Results 183.2.1 Suitability for macro-catchment use 183.2.2 Suitability for agricultural use 183.2.3 Suitability for macro-catchment water harvesting systems 18

4 Conclusions and follow up 20

References 20

Annex 1. Soil association composition 21Annex 2. Brief description of soil types in Syria 23Annex 3. Monte-Carlo simulation of sub-pixel level constraints overlap 35Annex 4. Summary of district areas suitable for different water harvesting techniques 38Annex 5. Maps of suitability for various micro-catchment and macro-catchment systems: 44

Map 1. Micro-catchment systems: suitability for ‘Contour ridges, range shrubs’ 44Map 2. Micro-catchment systems: suitability for ‘Contour ridges, field crops’ 45Map 3. Micro-catchment systems: suitability for ‘Contour ridges, tree crops’ 46Map 4. Micro-catchment systems: suitability for ‘Semi-circular bunds, range shrubs’ 47Map 5. Micro-catchment systems: sSitability for ‘Semi-circular bunds, field crops’ 48Map 6. Micro-catchment systems: suitability for ‘Semi-circular bunds, tree crops’ 49Map 7. Micro-catchment systems: suitability for ‘Small pits, range shrubs’ 50Map 8. Micro-catchment systems: suitability for ‘Small pits, field crops’ 51Map 9. Micro-catchment systems: suitability for ‘Small runoff basins, range shrubs’ 52Map 10. Micro-catchment systems: suitability for ‘Small runoff basins, tree crops’ 53Map 11. Micro-catchment systems: suitability for ‘Runoff strips, range shrubs’ 54Map 12. Micro-catchment systems: suitability for ‘Runoff strips, field crops’ 55Map 13. Micro-catchment systems: suitability for ‘Contour bench terraces’ 56Map 14. Macro-catchment systems: suitability for macro-catchment use 57Map 15. Macro-catchment systems: suitability for agricultural use 58Map 16. Macro-catchment systems: neighbouring areas suitable for either catchment

or agricultural use 59

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iv

ABSTRACT

Water harvesting covers various techniques tocollect rainwater from natural terrains or modi-fied areas and concentrating it for use on small-er sites or cultivated fields to assure economiccrop yields. In micro-catchment systems, thesource and target areas are essentially thatclose to each other that they cannot be atscales larger than the field level, and the stor-age facility is either the soil’s root zone forimmediate or a small reservoir for later use. Inmacro-catchment systems, run-off water is col-lected from a relatively large catchment outsidea relatively small target area with storage pro-vided by surface structures, such as small farmreservoirs, and subsurface structures, such ascisterns, or the soil in the target area itself.

In Syria water harvesting is not much adoptedby farmers. One of the reasons is that the agri-cultural research and extension support servic-es in Syria lack specific and systematic knowl-edge on potential areas and suitable locationsfor water harvesting. The objective of this studyis to provide a rapid GIS-based analytical tech-nique to assess suitability for various water har-vesting systems in Syria, with the ultimateobjective to adapt the technique for use at thelevel of the CWANA region.

The assessment was undertaken by matching ina geographical information system with simplebiophysical information, systematically availableat country level, to the broad requirements ofthe specified water harvesting systems. Thesystems evaluated include 13 micro-catchmentsystems, based on combinations of six tech-niques and three crop groups, and one general-ized macro-catchment system. The environmen-tal criteria for suitability were based on expertguidelines for selecting water-harvesting tech-niques in the drier environments. They includedprecipitation, slope, soil depth, texture, andsalinity, as well as land use/land cover and geo-logical substratum. The dataset included inter-polated surfaces of mean annual precipitation,a high-resolution digital elevation model, a soilmap of Syria, a land use/land cover map ofSyria, and a geological map of Syria.

The evaluation had two stages: scoring of theland attributes according to individual criteria,followed by the combination of the individualscores in a multi-criteria evaluation. Fuzzymembership functions were used to evaluatesuitability for continuous variables, such as pre-cipitation and slope. For these, the boundarybetween ‘suitable’ and ‘unsuitable’ forms bynature a continuum. The functions are fullydefined by their shape (either sigmoid or linear)and inflection point positions. Other relevant fac-tors, such as soils, land use or geological mate-rials, could at the national level only bedescribed as qualitative constraints. In addition,for these datasets, it is quite normal that the pix-els contain mixtures with different properties.

When several constraints occur, it becomesvery difficult to estimate the total proportion ofthe pixel that is affected by one constraint oranother. Monte-Carlo simulation of sub-pixelconstraint overlap indicated that a reasonableapproximation of total proportion of a pixelaffected by one constraint or another could beobtained by taking the sum of the estimatedproportions of the individual constraints. Theindividual factors were then scored on a com-mon scale (0-100) and combined through theMaximum Limitation Method (MLM) as a specialcase of Boolean overlay.

To identify areas suitable for macro-catchmentsystems, two separate assessments wereundertaken – the first one to evaluate suitabilityto serve as a catchment, and the second one toevaluate suitability as a target area, with theadditional constraint that both areas should bewithin a certain distance of each other. Theevaluation for catchment suitability includedfuzzy membership function for precipitation andslope, in which the scores were adjusted by tak-ing into consideration the soil hydrological prop-erties.

The results of the suitability assessments arepresented as a set of 14 maps and summarizedat provincial and district levels in the form of

tables.

Water harvesting covers various techniques tocollect rainwater from natural terrains or modi-fied areas and concentrating it for use on small-er sites or cultivated fields to assure economiccrop yields. Collected runoff is stored in the soil,behind dams or terraces, cisterns, gullies orused to recharge aquifers.

Water harvesting can thus be considered a spa-tial variant of supplemental irrigation. It is basedon the dryland management principle that it isoften better to deprive part of the land of its lowand unproductive share of rain, in order to add itto another part of the land and obtain economicyields (Oweis et al., 2001).

Water harvesting systems come in a variety ofimplementations, but the common componentsare invariably a catchment or source area, astorage facility, and target or use area. In micro-catchment systems the source and target areasare essentially that close to each other that theycannot be separated at a scale beyond the fieldlevel. In a GIS context, this can be translated assource and target area being located in thesame pixel, of relatively small size (e.g. 100-250m). For such systems the storage facility iseither the soil’s root zone for immediate or asmall reservoir for later use.

In macro-catchment systems, run-off water iscollected from a relatively large catchment out-side a relatively small target area with storageprovided by surface structures, such as smallfarm reservoirs, subsurface structures, such ascisterns, or the soil in the target area itself. In aGIS context, macro-catchment systems arecharacterized by source and target areas beinglocated in different pixels.

In Syria, water harvesting is not much adoptedby farmers. One of the reasons is that they arenot acquainted with traditional systems of waterharvesting, which are widely adopted in otherdryland areas of, amongst others, Egypt,Pakistan, Tunisia or Yemen. Another reason isthat the agricultural research and extension sup-port services in Syria lack specific and system-atic knowledge on potential areas and suitablelocations for water harvesting (Oberle, 2004).

The objective of this study is to provide a rapidGIS-based analytical technique to assess suit-ability for various water harvesting systems inSyria, with the ultimate objective to adapt thetechnique in order to allow assessment ofpotential at the level of the CWANA region.

The assessment of potential for different waterharvesting techniques is undertaken by match-ing in a GIS environment simple biophysicalinformation, systematically available at countrylevel, to the broad requirements of the specifiedwater harvesting systems using an expert-based empirical decision model. The systemsevaluated include both micro-and macro-catch-ment systems. Only those systems that can beconsistently evaluated at country level and donot require very site-specific data, which couldbe difficult or impossible to extrapolate, havebeen retained for evaluation.

The environmental criteria for suitability arebased, with some modifications, on the guide-lines for selecting water-harvesting techniquesin the drier environments, developed by Oweiset al. (2001). They include precipitation, slope,soil depth, texture, and salinity, as well as landuse/land cover and geological substratum.

1. INTRODUCTION

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2

2.1. Overview

2.1.1. General approach

As noted in the introduction, in micro-catchmentsystems runoff is collected from a small catch-ment area in the form of sheet flow over a shortdistance. Runoff water is usually applied to anadjacent agricultural area, where it is eitherstored in the root zone and used directly byplants, or stored in a small reservoir for lateruse. The target area may be planted with trees,bushes, or with annual crops. The size of thecatchment ranges from a few square meters to

around 1000 m2. Land catchment surfaces maybe natural, or cleared and treated to inducerunoff, especially when soils are light. Non-landcatchment surfaces include the rooftops ofbuildings, courtyards and similar impermeablestructures (Oweis, 2004).

From a GIS-modeling perspective, the main fea-ture on-farm micro-catchments have in com-mon, is that surface runoff is generated and col-lected ‘within pixel’ and that the precipitation cri-terion can be considered as almost constant.Since appropriate micro-catchment techniquesexist for almost any slope, soil or crop group,the modeling of suitability at the level of Syriacan be much simplified. Once the precipitationlevel has been evaluated, it can be assumedthat suitability will be determined by the feasibili-ty of applying the necessary terrain modification,inherent to the particular system, using slope,soil, and land use criteria.

The methodology has the following compo-nents:• Determining the particular biophysical factors,

evaluation criteria and factor thresholds forthe micro-catchment systems to be evaluated;

• Evaluating these factors individually;• Combining them in an integrated multi-crite-

ria analysis

2.1.2. Evaluated systems

The main land-based micro-catchment tech-niques for which a suitability assessment can beapplied at the level of Syria are:

• Contour ridges• Semi-circular and trapezoidal bunds• Small pits• Small runoff basins• Runoff strips• Inter-row systems• Contour-bench terraces

For each of these techniques, relevant cropgroups can be considered, resulting into specificmicro-catchment systems that can be evaluated(Table 1).

Table 1. Evaluated systems

Technique Crop groups

Contour ridges Range, Field, TreesSemi-circularand trapezoidal bunds Range, Field, TreesSmall pits Range, FieldSmall runoff basins Range, TreesRunoff strips Range, FieldContour bench terraces All

These systems are briefly described in the fol-lowing paragraphs, based on Oweis et al.(2001) and Oweis (2004).

2.1.2.1. Contour ridgesThese are bunds or ridges constructed along thecontour lines, usually spaced between 5 and 20m apart. The first 1–2 m upstream of the ridge isused for cultivation, whereas the rest is used asa catchment. The height of each ridge variesaccording to the slope’s gradient and the expect-ed depth of the runoff water retained behind it.Bunds may be reinforced by stones if necessary.

Contour ridges are one of the most importanttechniques for supporting the regeneration andnew plantations of forages, grasses and hardytrees on gentle to steep slopes in the steppe. Inthe semi-arid tropics, they are used for arablecrops such as sorghum, millet, cowpeas andbeans.

2.1.2.2. Semi-circular and trapezoidal bundsThese are usually earthen bunds in the shapeof a semi-circle, a crescent, or a trapezoid fac-ing directly upslope. They are created at a spac-ing that allows sufficient catchment to provide

2. EVALUATING SUITABILITY FOR ON-FARM MICRO-CATCHMENT SYSTEMS

ON-FARM MICRO-CATCHMENT SYSTEMS

3

the required runoff water, which accumulates infront of the bund, where plants are grown.Usually they are placed in staggered rows. Thediameter or the distance between the two endsof each bund varies between 1 and 8 m and thebunds are 30–50 cm high.

Bunds are used mainly for the rehabilitation ofrangeland or for fodder production, but may alsobe used for growing trees, shrubs and in somecases field crops and vegetables.

2.1.2.3. Small pitsPitting is a very old technique used mainly inWestern and Eastern Africa, but adopted insome WANA areas. It is used for rehabilitatingdegraded agricultural lands. The pits are 30 cmto 2 m in diameter. The most famous pittingsystem is the zay system used in Burkina Faso.This consists of digging holes with a depth of 5-15 cm. Pits are applied in combination withbunds to conserve runoff, which is slowed downby the bunds. This system allows much degrad-ed agricultural land to be put back into use.Pitting systems are used mainly for the cultiva-tion of annual crops, such as cereals. If the pitsare dug on flat instead of sloping ground, theymay be regarded more as an in situ moisture-conservation technique than as waterharvesting one.

2.1.2.4. Small runoff basinsSometimes called negarim, small runoff basinsconsist of small diamond- or rectangular-shapedstructures surrounded by low earthen bunds.They are oriented to have the maximum landslope parallel to the long diagonal of the dia-mond, so that runoff flows to the lowest corner,where the plant is placed. The usual dimensionsare 5-10 m in width and 10-25 m in length.Small runoff basins can be constructed onalmost any gradient, including plains with 1-2%slopes. They are most suitable for trees. Thesoil should be deep enough to hold sufficientwater for the whole dry season.

2.1.2.5. Runoff stripsIn this technique, the farm is divided into stripsalong the contour. An upstream strip is used asa catchment, while a downstream one is cultivat-ed. The strip with crops should not be too wide(1-3 m), while the catchment width is determined

in accordance with the amount of runoff waterrequired. This technique is highly recommendedfor barley cultivation and other field crops inlarge steppe areas of WANA, where it canreduce risk and substantially improve produc-tion. The catchment area can be used for graz-ing after the crop has been harvested.

2.1.2.6. Contour bench terracesContour-bench terraces are constructed on verysteep slopes to combine soil and water conser-vation with water harvesting. Cropping terracesare built in level with supporting stonewalls toslow down the flow of water and control erosion.They are supplied with additional runoff waterfrom steeper, non-cropped areas between theterraces. The terraces are usually provided withdrains to release excess water safely. They arefrequently used to grow trees and bushes, butrarely used for field crops in the WANA region.The historic bench terraces in Yemen are agood example of this system.

2.2. Evaluating suitability of biophysicalfactors relevant to the selected systems

For all micro-catchment systems precipitation,slope, soils, and geological material have beenconsidered of major relevance to evaluate theirsuitability. In addition, there are other generalconstraints related to land use that need to beconsidered. All these are factors that can beevaluated at national level, using either publicdata that are already available, or can be con-verted into a spatially explicit form suitable forevaluation.

2.2.1. Datasets

The data set used in the evaluation includes:• Interpolated surface of mean annual

precipitation• SRTM (Shuttle Radar Topographic Mission)

DEM• Soil map of Syria• Land Use/land Cover Map of Syria• Geological Map of SyriaAll these layers were available for the samespatial extent covering all of Syria and with aspatial resolution conform to the SRTM digitalelevation model (see further).

USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING

4

2.2.1.1. Climate surfacesA climate ‘surface’ is a raster file containing esti-mates of a climatic variable for each grid cell.The surface of mean annual precipitation wasprepared by spatial interpolation of point climaticdata using the thin-plate smoothing splinemethod of Hutchinson (1995) and the ANUS-PLIN interpolation software (Hutchinson, 2000).The resolution of the climate surface was 90 m;the same as the digital elevation model used inthis study (see 2.1.3.2.). The main source of cli-matic data for Syria was the FAOCLIM2 data-base (FAO, 2001).

2.2.1.2. SRTM digital elevation modelThe SRTM (Shuttle Radar Topographic Mission)is a high-resolution global digital elevationmodel (DEM), released in 2000. Its resolution is3 arc-seconds (90 m), suitable for use at scale1:100,000. From this data set, available fromthe Internet, the subset covering Syria was cre-ated and the slopes were derived using theSlope function in the Spatial Analyst module ofArcGIS.

2.2.1.3. Soil map of SyriaSoil information was obtained from the studyundertaken by Van de Steeg and De Pauw(2002). The latter report was itself based on anational soil mapping project undertaken byLouis Berger et al. (1982). The 1:500,000 scalemap produced as part of the study, has a leg-end based on soil associations composed ofSoil Taxonomy classification units (Soil SurveyStaff, 1975). Each association is characterizedby a unique combination of soil types and rela-tive distributions within the association. Thecomposition of each soil mapping unit (associa-tion) is provided in Annex 1. Short descriptionsof each soil unit are provided in Annex 2. Thevector Soil Map of Syria was converted intoraster format, for compatibility with the climatesurfaces and SRTM DEM.

2.2.1.4. Land use/land cover map of SyriaThis map at 1:200,000 scale is a representationof the land use/land cover status in Syria for thebase year 1989/90 (De Pauw et al., 2004). It isbased on a visual interpretation of satellite-derived products (Landsat 5 TM hardcopyimages) and field checking. The map legendconsists of two main classes: homogeneousunits, which can be considered relatively pure

(80-90%), and mixed units, which are complex-es of homogeneous units. The map has 24homogeneous classes were differentiatedbelonging to the following major categories: (i)bare areas with or without sparse cover, (ii) cul-tivated areas, (iii) forests and other woodedareas, (iv) rangelands, (v) urbanized areas, (vi)water bodies. In addition, 43 mixed classeshave been distinguished for which the propor-tions of the homogeneous components havebeen estimated. Also this vector map was con-verted into raster format, for compatibility withthe climate surfaces and SRTM DEM.

2.2.1.5. Geological map of SyriaThe Geological Map of Syria (Technoexport,1967) is at scale 1:500,000. The legend isbased on lithological associations grouped bygeological period. In terms of lithological com-position, the mapping units can be pure ormixed. The map does not provide indications onthe proportions of each lithological group in amapping unit. This vector map was also con-verted into raster format for compatibility withthe other layers.

2.2.2. Suitability evaluation procedure

Evaluating the suitability for a particular waterharvesting system involves two steps:• Scoring of the land attributes according to

the relevant criteria• Combining the individual suitability scores in

a multi-criteria evaluation

The factors can be rated through either a ‘fuzzy’or a ‘crisp’ scoring system, depending onwhether they are continuous variables (e.g. pre-cipitation, slope), or discontinuous.

This is explained in the following sections.

2.2.2.1. Evaluating continuous variablesthrough fuzzy membership functionsThe difference between a ‘fuzzy’ or ‘crisp’assessment method is shown in Fig 1, using theexample of precipitation suitability for water har-vesting. Suitability of precipitation is expressedon a scale between 0 (not suitable) and 1 (suit-able). The crisp membership function on the leftis ‘binary’: the precipitation is either ‘suitable’ or‘unsuitable’ for water harvesting. To be ‘suitable’it should be in the range 150-250 mm on an

annual basis, any area with annual precipitationoutside this range is considered ‘unsuitable’.

The fuzzy membership function on the right is acontinuous one, which in essence describes aprobability that the precipitation will be ‘suitable’for water harvesting. If the annual precipitationis zero, suitability for water harvesting is obvi-ously zero (there is nothing to harvest); similarlyif the annual precipitation is 500 mm or more,the suitability is also zero, but for a different rea-son: at this high precipitation level, it can beassumed that there is no need for water har-vesting. If the annual precipitation is in therange 150-250 mm, the suitability is one, beingthe expert-assessed optimal range. For interme-diate annual precipitation levels (<150 mm or>250 mm) the probability of belonging to theclass ‘suitable’ is determined by the particularshape of the membership function, which isuser-determined.

Fuzzy membership functions allow more subtle(and probably more realistic) assessments thancrisp membership functions, since they are suffi-ciently flexible to adopt the shapes determinedby experts as most appropriate. At the sametime, they offer an excellent way to expressuncertainty related to ‘expert’ opinion. Theyavoid, for example, classification traps, such as

a 10% slope being ‘suitable’, and a 9.99% slopebeing ‘unsuitable’.

In this analysis fuzzy membership functions areused to deal with continuous variables such asprecipitation and slope, in which the boundarybetween ‘suitable’ and ‘unsuitable’ by natureforms a continuum.

The fuzzy membership function shown in Figure1 is a sigmoid (“s-shaped”) function, perhapsthe most commonly used in Fuzzy Set theory. Itis described by the position of inflection points(a, b, c, d) on the curve. Different expressionsof the sigmoid membership function are shownin Figure 2. However, there are many othertypes of membership function, most notably J-shaped, linear functions, and functions that areentirely user-defined through control points(Eastman, 2001).

In this study, for the sigmoid type of fuzzy mem-bership function the scores were calculatedaccording to a cosine-type function with the fol-lowing equations (Eastman, 2001):

y = 0 in the interval x≤a

y = in the interval a<x≤b

y = 1 in the interval b<x≤c


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Figure 1. Example of suitability of precipitation for water harvesting, using (a) a crisp membership function (left) and(b) a fuzzy membership function

Figure 2. Variants of the sigmoid membership function (from Eastman, 2001)


6

y = in the interval c<x≤d

y = 0 in the interval x>d

2.2.2.2. Evaluating discontinuous variablesthrough Boolean operatorsThe performance of the systems evaluated may,in some cases, be constrained by factors that,due to the uncertainty within the particular dataset, can only be described in qualitative termsand not as a continuous function. Examples offactors that could only be described, with theavailable data sets, as qualitative constraints atnational level are soils, land use, and geology.

In such cases the variable can for all practicalpurposes be considered as ‘discontinuous’, anda suitability score based on a ‘crisp’ member-ship function, using a Boolean operator, wouldbe more appropriate.

An example of a rule based on a Boolean oper-ator would be:

IF (Land Use = Forest or Land Use = Irrigated)THEN Land Use suitability score = 0 ELSE Land Use Suitability score = 1

Soil constraints can themselves be caused bydifferent soil characteristics that can be evaluat-ed individually. In the case of the available soilmap, it was possible to evaluate the followingsoil characteristics separately and consistently:• Soil depth• Soil salinity• Soil texture

2.2.2.3. Combining individual scores in amulti-criteria evaluationIn conventional land evaluation the combinationof individual factor scores can be done by eitherBoolean overlay or by a weighing mechanismbased on standardizing the individual factors toa common numeric range, and then combiningby means of a weighted average.

These combinations are easy to apply in mostGIS software packages, on condition that therelevant raster layers have the same spatialextent and grid cell resolution.

In the Boolean overlay procedure all criteria are

reduced to logical statements of suitability andthen combined by means of one or more logicaloperators: AND (intersect) in case of a need forall factors to meet the matching criteria, or OR(union) in cases where one factor can compen-sate fully for another factor not meeting the cri-teria. In the weighting procedure, of which theWeighted Linear Combination (WLE) is the bestknown (Eastman, 2001), some level of compen-sation, determined by the weights, is possible.

In this study, the Maximum Limitation Method(MLF) is used as a special case of Booleanoverlay. In this method the most limiting factorsets the final score, irrespective of the (better)scores on other factors.

The use of the Maximum Limitation Method isillustrated in the following example. Assume thatthe factors to be evaluated by fuzzy membershipfunctions are precipitation and slope. In this case:• the suitability score for precipitation is given

by the sigmoid membership functionSprecip = ƒ(precip), in which the function is

parameterized by user-assigned values forthe inflection points;

• similarly, the suitability score for slope isgiven by the sigmoid membership functionSslope = ƒ(slope), in which the function is

parameterized by user-assigned values forthe inflection points;

• the combined suitability score for precipita-tion and slope is then the lowest of the factorscores obtained from the two fuzzy member-ship functions:

S = Min (Sslope; Sprecip)

The Maximum Limitation Method is easy toimplement in GIS by applying the Minimumfunction to the relevant raster layers. This func-tion is available in most software packages.

2.2.2.4. Dealing with heterogeneous pixelsUp to now, it has been assumed that the rasterlayers to be combined in a multi-criteria evalua-tion consist of pixels that are homogeneous.That would be true for the precipitation andslope layers, but is certainly not the case forland use, soils and geology. For these themesderived from low-resolution maps at scales1:200,000 to 1:500,000, it is simply not possibleto capture, at 90 m resolution, the variability at


7

pixel-level by a single classification unit. In suchcases, it is quite normal that the pixels containmixtures with different properties. In the bestcases, e.g. for the land use/land cover or soilthemes, this lack of homogeneity at pixel levelcan be somewhat alleviated if the proportions ofindividual components are known or can beestimated. In such cases, it also needs to beassumed that any given pixel will contain allpossible states as described by these explicitstatements of heterogeneity.

Heterogeneous pixels present special problemsfor multi-criteria evaluations. Whereas constraintscan be handled with relative ease, if there is onlyone of them in a particular pixel, complex combi-nations can arise when several constraints occur.In such cases, it becomes very difficult to esti-mate the total proportion of the pixel that isaffected by one constraint or another.

Two hypotheses are plausible:• a minimal solution (H1): the total proportion

of a pixel affected by one constraint oranother is the highest of the proportions ofthe individual constraintsH1 = Max (pi) i=1, 2,…n

• a maximal solution (H2): the total proportionof a pixel affected by one constraint oranother is the sum of the proportions of theindividual constraints

H2 = Min i=1, 2, …n

The hypotheses were tested by a Monte-Carlosimulation to figure out which of the two cameclosest to reality. A pixel was subdivided in 100blocks and the overlap of a variable number ofblocks, of which the number and position weregenerated by randomization, was simulated.The results, reported in Annex 3, indicate thatthe overlap calculated by H2 comes closer tothe simulated overlap than H1, and that thisagreement gets better as the number of con-straints increases.

Using H2 as rule, the total proportion of a pixelaffected by a prohibitive (non-suitable) soil con-straint is the sum of weighted proportions in thepixel of soils that are either too shallow, or toosaline, or have a severe textural constraint):

pNS,Soil = weightdepth*pNS,Depth +

weightsalinity*pNS,Salinity +

weighttexture*pNS,Texture

The proportions of soil constraints are based onthe particular attribute class each soil unit of theSoil Map of Syria has been assigned (Van deSteeg and De Pauw, 2002). The weights fordepth, salinity or texture are in the range 0-1.These weights are assigned, through expertjudgment, on the basis of the perceived degreeof limitation of the concerned property to a par-ticular water harvesting system. For example,the weight for the factor ‘depth’ could be 1 forthe system ‘contour ridges, field crops’ if the soilis very shallow, or 0.5 if the soil is shallow, oran intermediate value if the soil type itself canbe either shallow or very shallow. The weightsgiven to different values of the soil properties foreach evaluated system are shown in Table 4.

The proportion of a pixel with a prohibitive (non-suitable) land use constraint is the sum of pro-portions of that pixel with a prohibited land use,in this case forests or irrigation (i.e. addingwater where there is already enough):

pNS,Land use = pNS,forest + pNS,irrigated

The proportions of land use constraints arebased on the proportion a prohibited land usetype (forest or irrigated land) occupies in eachmapping unit of the Land Use/Land Cover Mapof Syria (De Pauw et al., 2004).

The proportion of a pixel with a prohibitive geo-logical constraint is the (tentative) proportion ofoccurrence of a geological parent material con-sidered highly sensitive to erosion, if at thesame time a slope criterion is met:

if slope < x% then pNS,Geol = 0

else pNS,Geol = psensitive pm

in which x is particular for the system considered.

The proportions of geological constraints arebased on the proportion occupied by an unfa-vorable geological parent material. These pro-portions are estimated on the basis of the1:500,000 scale geological maps of Syria, and,given the low map resolution, have to be con-sidered very tentative (Table 2).

2.2.2.5. Combining scores for homogeneousand heterogeneous pixelsTo combine scores a uniform pixel size of 90 mwas used for all thematic layers.

Using the H2 hypothesis, the proportion of apixel with either a prohibitive land use, geologi-cal or soil constraint is, then, the sum of the pro-portions of the soil, land use and geologicalconstraints:

pNS = pNS,Soil + pNS,Geol + pNS,Land Use

The final score, combining the continuous,homogeneous variables, precipitation and

slopes, with the discontinuous and heteroge-neous variables, soils, land use and geologicalparent materials can then be represented by theformula:

S = Min (Sslope; Sprecip) * (1-pNS )

The overall methodology is illustrated in Figure3. Input layers (left hand column of Figure 3),with the same geographical scope and resolu-tion, are converted into individual factor or con-straint scores (middle column of Figure 3). Thefactor or constraint scores are then combined ina multi-criteria evaluation using the principlesexplained in this section.


8

Table 2. Tentative probabilities of highly erodible parent materials, derived from the 1:500,000Geological Map of Syria

Geological Description Tentative units proportion (%)

N2 Continental conglomerates, sandstones, limestones, clays, 40marls; marine clays, tuff-breccias.

N13 NW Syria: Gypsum, marls, limestones. NW: 33

Al-Furat basin: clays, sandstones, marls, siltstones, gypsum Furat basin: 60(Upper Fars formation)

N12 Nahr El-Kabir basin: marine marls, limestones, sandstones. 33

Anti-Lebanon-Palmyrides: continental clays, sandstones, conglomerates

N12t NW-Syria: limestones, marls, conglomerates, sandstones. NW: 25

Al-Furat basin: gypsum, limestones, marls, clays, sandstones, Furat basin: 33rock salt (Lower Fars formation)

N12h Organogeneous-detrital limestones (pelcypodal, gastropodal Kurd-Dagh: 25and algal varieties.

Eastern slope of Kurd-Dagh: conglomerates, limestones, sandstones, marls

N11 Northwestern Syria: marine limestones, marls, clays, conglomerates, NW Syria: 40sandstones.

Anti-Lebanon-Palmyrides: continental quartz sands A-Lebanon: 100

Pg23 Chalky limestones, marls 50

Pg22 Soft chalky and hard nummulitic limestones, marls 33

Pg1-Pg21 Chalky/nummulitic limestones with flint interbeds;marls, clays 50

Pg1 Limestones and marls 50

Cr2m-d Chalky limestones, marls 50

9

Fig

ure

3.

Pro

cess

ing s

equence

in a

ssess

ing s

uita

bili

ty f

or

wate

r harv

est

ing

te

chn

iqu

es


2.2.3. Criteria and constraint values

2.2.3.1. PrecipitationFor all on-farm micro-catchment systems, theprecipitation criterion is taken as constant andsuitability is approximated using a fuzzy sym-metric sigmoid membership function with fourinflection points A: 0 (0); B: 150 (1); C: 250 (1);D: 500 mm (0). The output is a ‘precipitationscore’ layer.

2.2.3.2. SlopeTo avoid sudden changes in suitability resultingfrom a minor change in actual slope, the slopecriterion is modeled by a fuzzy symmetric sig-moid membership function with inflection pointsdetermined by the requirements of the particularsystem. The slope is expressed as a percent-age of the ratio ‘rise/run’. Thus a vertical rise of100 m over a horizontal distance (run) of 100 mcorresponds with a slope angle of 45° and witha slope percentage of 100%. For a slope angleof nearly 90° the slope percentage approachesinfinity.

The following slope class terminology is used:• Flat (slope range: 0-1%)• Almost flat (slope range 1-2%)• Gentle (2-5%)• Medium (5-15%)• Steep (15-30%)• Very steep (30-50%)• Precipitous (50-100%)• Near vertical (>100%)The output is a ‘slope score’ layer.

2.2.3.3. SoilsAll soils are acceptable unless they are tooshallow, or too saline for agriculture, or havevery severe textural limitations that affect thesystem’s feasibility. In GIS, this can be mod-eled using an exclusion rule, eventually in com-bination with other rules (e.g. texture in combi-nation with slope). The output is a layer showingthe percentage of each pixel that is excluded asbeing unacceptable for the particular water har-vesting system. Alternatively, it can be interpret-ed as a ‘probability’ of not meeting the soilrequirements. If several soil criteria apply, thehighest percentage is considered.

The classes used in assessing whether soil fac-tors are constraints were established by Van deSteeg and De Pauw (2002), and are listed inTable 3.

Referring to the formula for PNS, Soil on page 7,

it may be possible, through expert opinion, toassign weights in accordance with the perceivedstrength of the particular soil constraint. Theweights will depend on the importance andvalue of the particular soil characteristic for eachevaluated system. They are listed in Table 4.

2.2.3.4. GeologyIn some cases severe system problems (e.g.erosion risk on steep soils developed on marlsor clayey limestones), may not be evident fromthe soil characteristics, in which case geologicalparent materials may have higher indicativevalue and be incorporated into the evaluation


10

Table 3. Soil attribute classes

Soil Factor Class symbol Class range

Depth D Deep (from 100 to 150 cm)M Moderate (from 50 to 100 cm)S Shallow (from 30 to 50 cm)V Very shallow (less than 30 cm)M-D Moderate to deep (from 50-150 cm)

Texture Y Very clayeyC ClayeyL LoamyS SandyX Extremely sandyN No soilS-X Sandy and extremely sandyC-L Clayey and loamy

Salinity N No salinityG Salinity due to gypsumS Salinity due to sodium


11

using exclusion rules. The output is a layershowing the percentage of each pixel that isexcluded as being unacceptable for the particu-lar water harvesting system. Alternatively, it canbe interpreted as a ‘probability’ of not meetingthe geological material requirement.

2.2.3.5. General constraints (applicable to allmicro-catchment techniques)Urban areas are automatically excluded (i.e.score = 0), as well as areas within a 1-km bufferzone around roads and within 200 m of surfacewater bodies.

2.2.4. Adaptation for specific micro-catchment systems

2.2.4.1. Contour ridges, range shrubsSlopes. Any slope between ‘medium’ and‘steep’ is optimal. At a slope range higher than‘steep’, another technique, contour-bench ter-racing, may be more appropriate. Inflectionpoints of the sigmoid function are A: 1 (0); B: 5(1); C: 30 (1); D: 50 (0).

Soil constraints. Soils with the following classesare excluded:• Depth class V• Salinity class S• Texture class X and class S-X (50%) if slope

>20%

Geological constraints.Geological units N2,Pg22 and Pg23 are excluded if the slopeexceeds 20%.

2.2.4.2. Contour ridges, field cropsSlopes. ‘Medium’ slopes (5-15%) are optimal.Inflection points of the sigmoid function are A: 2(0); B: 5 (1); C: 15 (1); D: 30 (0).

Soil constraints. Soils with the following classesare excluded:• Depth classes V and S• Salinity class S• Texture class X and class S-X (50%) if slope

>20%

Geological constraints. Geological units N2,Pg22 and Pg23 are excluded if the slopeexceeds 20%.

Table 4. Suggested weights for constraint strength

Constraint Micro-catchment water harvesting system

1 2 3 4 5 6 7 8 9 10 11 12 13

Depth Class:• D 0 0 0 0 0 0 0 0 0 0 0 0 0• M 0 0 0.5 0 0 0.5 0.25 0.75 0.25 1 0 0.5 0.5• S 0 0.5 1 0 0.5 1 1 1 1 1 0.5 1 0• V 1 1 1 1 1 1 1 1 1 1 1 1 1• M-D 0 0 0.25 0 0 0.25 0 0.5 0 0.5 0 0 0.75

Salinity Class:• N 0 0 0 0 0 0 0 0 0 0 0 0 0• G 0 0 0 0 0 0 0 0 0 0 0 0 0• S 1 1 1 1 1 1 1 1 1 1 1 1 1

Texture Class:• Y 0 0 0 0 0 0 0 0 0 0 0 0 0• C 0 0 0 0 0 0 0 0 0 0 0 0 0• L 0 0 0 0 0 0 0 0 0 0 0 0 0• S 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0• X 1 1 1 1 1 1 1 1 1 1 1 1 1• N 0 0 0 0 0 0 0 0 0 0 0 0 0• S-X 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.75 0.75 0.5• C-L 0 0 0 0 0 0 0 0 0 0 0 0 0

Notes: Numbers in top row refer to systems as follows:01: contour ridges, range shrubs 02: contour ridges, field crops 03: contour ridges, tree crops04: semi-circular bunds, range shrubs 05: semi-circular bunds, field crops 06: semi-circular bunds, tree crops07: small pits, range shrubs 08: small pits, field crops 09: small runoff basins, range shrubs10: small runoff basins, tree crops 11: runoff strips, range shrubs 12: runoff strips, field crops13: contour bench terraces


12

2.2.4.3. Contour ridges, tree cropsSlopes. ‘Gentle’ to ‘medium’ slopes (2-15%) areoptimal. Inflection points of the sigmoid functionare A: 1 (0); B: 2 (1); C: 15 (1); D: 30 (0)

Soil constraints. Soils with the following classesare excluded:• Depth classes V, S and M, M-D (50%)• Salinity class S• Texture class X and class S-X (50%) if slope

>20%

Geological constraints. Exclude geological unitsN2, Pg22 and Pg23 if slope >20%

2.2.4.4. Semi-circular bunds, range shrubsSlopes. ‘Medium’ slopes (5-15%) are optimal.Inflection points of the sigmoid function are A: 1(0); B: 5 (1); C: 10 (1); D: 15 (0)

Soil constraints. Soils are excluded with• Salinity class S• Texture class X and class S-X (50%)• Depth class V

2.2.4.5. Semi-circular bunds, field cropsSlopes. ‘Medium’ slopes (5-15%) are optimal.Inflection points of the sigmoid function are A: 1(0); B: 5 (1); C: 10 (1); D: 15 (0)

Soil constraints. Soils are excluded with• Salinity class S• Texture class X and class S-X (50%)• Depth classes V and S

2.2.4.6. Semi-circular bunds, tree cropsSlopes. ‘Medium’ slopes (5-15%) are optimal.Inflection points of the sigmoid function are A: 1(0); B: 5 (1); C: 10 (1); D: 15 (0)

Soil constraints. Soils with the following classesare excluded:• Salinity class S• Texture class X and class S-X (50%)• Depth classes V, S and M, M-D (50%)

2.2.4.7. Small pits, range shrubsSlopes. ‘Gentle’ to ‘medium’ slopes are optimal.Inflection points of the sigmoid function are A: 1(0); B: 2 (1); C: 10 (1); D: 15 (0).

Soil constraints. Soils with the following classesare excluded:

• Salinity class S• Texture class X and class S-X (50%)• Depth classes V and S

2.2.4.8. Small pits, tree cropsSlopes. ‘Gentle’ to ‘medium’ slopes are optimal.Inflection points of the sigmoid function are A: 1(0); B: 2 (1); C: 10 (1); D: 15 (0).

Soil constraints. Soils with the following classesare excluded:• Salinity class S• Texture class X and class S-X (50%)• Depth classes V, S, M

2.2.4.9. Small runoff basins, range shrubsSlopes. Almost flat to gentle slopes are optimal.Inflection points of the sigmoid function are A:0.5 (0); B: 2 (1); C: 5 (1); D: 8 (0)

Soil constraints. Soils with the following classesare excluded:• Salinity class S• Texture class X and class S-X (50%)• Depth class V and S

2.2.4.10. Small runoff basins, tree cropsSlopes. Almost flat to gentle slopes are optimal.Inflection points of the sigmoid function are A:0.5 (0); B: 2 (1); C: 5 (1); D: 8 (0)

Soil constraints. Soils with the following classesare excluded:• Salinity class S• Texture class X and class S-X (50%)• Depth classes V, S and M, M-D (50%)

2.2.4.11. Runoff strips, range shrubsSlopes. ‘Gentle’ to ‘medium’ slopes are optimal.Inflection points of the sigmoid function are A: 1(0); B: 2 (1); C: 10 (1); D: 15 (0)

Soil constraints. Soils with the following classesare excluded:• Salinity class S• Texture classes S, X and S-X• Depth class V and S

2.2.4.12. Runoff strips, field cropsSlopes. ‘Gentle’ to ‘medium’ slopes are optimal.Inflection points of the sigmoid function are A: 1(0); B: 2 (1); C: 10 (1); D: 15 (0)


13

Soil constraints. Soils with the following classesare excluded:• Salinity class S• Texture classes S, X and S-X• depth class V, S and M

2.2.4.13. Contour bench terracesSlopes. This system is suitable in areas withstrongly dissected topography. Most fitting slopeclasses for this system, subject to appropriatewall fortification, are ‘steep’ to ‘very steep’. Inflection points of the sigmoid function are A:10 (0); B: 20(1); C: 50 (1); D: 100 (0)

Soil constraints. In evaluating the presence ofsoil constraints, one has to be aware that thecut-and-fill operations involved in the construc-tion of contour bench terraces create such adegree of soil modification that the originaldepth and stoniness properties are permanentlychanged. Concentration of agricultural soil onthe terrace flat is a common practice, whereasthe terrace walls are strengthened with thecoarse fragments, stones and rocks. For thisreason, the presence of shallow and stony soilsis considered a bonus, since it means that thenecessary wall materials are already in place.At the same time, given the separation of thesoil fractions involved in building the terraces, itis not possible to evaluate the soil constraintsseparately for tree or field crops.

Soils with the following classes are excluded:• Depth classes V, D, M• Salinity class S• Texture class X and class S-X (50%)

2.2.5. Enhanced visualization

Figure 4 shows a sample area from the suitabili-ty map for the technology ‘micro-catchments/small runoff basins/ range shrubs’. For diversepurposes, such as reproduction on small-scalemaps, more concise pattern delineation or sim-ply reduction of file size using file compression,it may be useful to apply a noise-reduction pro-cedure. To achieve this, the suitability rankingswere regrouped into 6 classes as follows:• Class 1: 0 (no suitability)• Class 2: 0 – 20 (very low) • Class 3: 20 – 40 (low)• Class 4: 40 – 60 (moderate)• Class 5: 60 – 80 (suitable) • Class 6: 80 – 100 (high suitability)

This layer containing class values was thenseparated into 6 different layers, each contain-ing either the value x (where x equals the classnumber) or zero (not belonging to class x). Toeach layer a majority filter was applied (Fig. 5).In case the majority function leads to ‘no data’values (when there is no majority within theneighbors), the original pixel value was retained.

Figure 4. Sample area with unprocessed suitabilityrankings

Figure 5. Focal majority filter (ESRI, 2005)

Figure 6. The same sample area with aggregatedsuitability rankings

Finally, the ‘maximum’ function was applied onthe six layers for each pixel, in order to promotethe higher suitability ranking. The results of thisprocedure are shown for the same sample areain Figure 6.

2.3. Results

The results of the analysis are summarized inTable 5 and in Annex 4. In addition, a set ofmaps on the CD shows the suitability domainsfor the different water harvesting technologies. Table 5 shows the percentages of eachprovince with high or very high suitabilityscores. A similar table with a breakdown by dis-trict is provided in Annex 4.

Figure 7 shows the location of the provinces.Homs, Hama, Dayr Az Zor and Raqqa have thehighest concentrations of areas with high suit-ability for various water harvesting techniques.In large parts of these provinces, the precipita-tion is in the optimal range for water harvesting,while at the same time soils are generally notprohibitive by depth or soil texture, althoughsalinity is present in some areas. AlthoughDamascus and Sweida provinces also havelarge areas with a suitable precipitation rangeand slopes, the soils are generally too shallowor stony for using the in-situ harvested water.

In these higher-potential areas for water har-vesting, the highest scores for range shrubs (interms of percentage of the province with highand very high suitability scores) are for smallrunoff basin systems, followed by contour ridgesand small circular bunds. Small pits have thelowest scores whereas runoff strips have inter-mediate scores. In the case of field crops, therelative performance of the different technolo-gies is similar, but the scores are much lower,except for the small pits, where they are thesame. The results also seem to indicate that fortree crops the most suitable WH techniques arecontour ridges and small runoff basins. Withsmall runoff basins, the best performing technol-ogy for shrubs and field crops, tree crops arelikely to perform less well under the particularclimatic, soil and terrain conditions.


14

Figure 7. Provinces of Syria

Table 5. Percentages of Syrian provinces with high suitability scores for different water harvesting techniques

WH system Contour Contour Contour Semicircular Semicircular Semicircular ridges - ridges - ridges - bunds - bunds - bunds -

Range shrubs Field crops Tree crops Range shrubs Field crops Tree crops

Muhafaza % % % % % % % % % % % % % % % % % %(Province) S VS VS+S S VS VS+S S VS VS+S S VS VS+S S VS VS+S S VS VS+S

As_Suweida 2 1 4 0 0 1 0 1 1 2 1 3 0 0 1 0 0 1Dara 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0Al_Qunaytirah 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Damascus 5 3 8 1 2 3 6 3 9 4 2 6 2 1 4 2 1 3Tartous 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Homs 6 9 15 5 3 8 12 9 20 6 8 14 6 3 9 3 2 5Hama 2 4 6 2 2 4 1 8 9 2 4 6 3 2 4 1 1 3Al_Ghab 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Lattakia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Idleb 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Dayr_Az_Zor 3 5 8 4 1 4 12 4 16 3 4 7 4 1 5 1 0 2Raqqa 2 4 6 2 1 3 7 7 14 2 4 6 2 1 4 1 1 2Aleppo 2 1 3 0 1 1 2 2 4 1 1 3 1 1 1 0 1 1Hassakeh 2 1 2 0 0 1 2 2 4 2 0 2 1 0 1 0 0 1

Notes: %S: percentage of the Province with suitability score 0.6-0.8 ( or 60-80% on percentage basis)%VS: percentage of the Province with suitability score 0.8-1 ( or 80-100% on percentage basis)%S+VS: sum of above


15

Tab

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SV

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SV

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01

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00

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21

32

13

72

97

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21

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00

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25

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21

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82

16

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b0

00

00

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00

Dayr

Az

Zor

10

21

02

13

51

81

44

18

41

51

02

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0R

aqqa

11

21

12

78

15

78

15

31

41

12

00

0A

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01

10

11

22

42

24

10

10

01

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01

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10

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: perc

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with

suita

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core

0.6

-0.8

( o

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enta

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s)%

VS

: perc

enta

ge o

f th

e P

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nce

with

suita

bili

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core

0.8

-1

( or

80-1

00%

on p

erc

enta

ge b

asi

s)%

S+

VS

: su

m o

f above

3.1. Methodology

3.1.1. Overview

Macro-catchment systems are sometimesreferred to as ‘water harvesting from longslopes’ or as ‘harvesting from an external catch-ment’ (Oweis et al., 2001). The modeling of suit-ability for macro-catchments is more complicat-ed than for micro-catchments, because the run-off is generated outside the pixel to be evaluat-ed, and is a largely unknown quantity.

Using the current simple rainfall, soil, terrainand land use criteria at country level, the evalu-ation of suitability for macro-catchmentsrequires the separate evaluation of suitability fora ‘catchment’ area and for a ‘use’ area. The cri-teria for the ‘catchment’ and ‘use’ areas are dif-ferent:• for the catchment area, strongly sloping land

with soils that are shallow, rocky, or havepoor infiltration capacity is preferable;

• for the use area, level or gently undulatingland with deep soils and no other limitationsto agricultural use is preferable

• in addition, land suitable for use as a catch-ment, must be within a certain distance ofland suitable for agricultural use.

The problem of identifying, in a GIS environ-ment, land with these contrasting requirementsis then reduced to a separate assessment ofsuitability for catchment and agricultural purpos-es, followed by an assessment of the constraintimposed by distance between these two differ-ent environments.

It is to be noted that in this assessment the suit-ability for reservoir construction has not beentaken into consideration.

3.1.2. Evaluating suitability for catchmentuse

The following factors are considered:• precipitation• slope• hydrological properties of soils• geological materials

3.1.2.1. PrecipitationFor all macro-catchment systems the precipita-tion criterion is the same as for the micro-catch-ment systems, and suitability is approximatedusing the same fuzzy symmetric sigmoid mem-bership function with four inflection points A: 0(0); B: 150 (1); C: 250 (1); D: 500 mm (0). The‘precipitation score’ layer Sprecip = ?(precip) istherefore the same.

3.1.2.2. SlopeAny surface can act as a catchment as long asit has some slope, very limited permeability forprecipitation and no obstacles. As a first approx-imation, one could consider the slope as non-limiting, as long as it is not near zero. This con-dition can be simulated by a fuzzy symmetricsigmoid membership function with two inflectionpoints A: 0% (0); B: 5% (1) and the ‘slope score’layer is then Sslope = ƒ (slope)

3.1.2.3. Soil hydrological propertiesSoils have different hydrological properties andas such are a major factor in the run-off gener-ating potential of catchments. The SoilConservation Service of the US Department ofAgriculture (1969) differentiates four majorhydrological classes:

• Class A (low run-off potential): deep sandysoils;

• Class B: shallow sandy soils and medium-texture soils with above average infiltrationrates;

• Class C: shallow soils of medium to heavytexture with below-average infiltration rates;

• Class D (high run-off potential): clay andshallow soils with hardpan, high groundwatertable etc.

Using this framework and expert judgment, thesoils of Syria have been reclassified into hydro-logical classes (Table 6). For a summarydescription of the soil types of Syria is referredto Van de Steeg and De Pauw (2002) and toAnnex 2.

The interpretation of Figure 8 is that if, forexample, the soil in a particular pixel belongs tohydrological class D, there will be no reduction

16

3. EVALUATING SUITABILITY FOR MACRO-CATCHMENTS

in runoff if the slope is 3% or higher; if, on theother hand, the soil belongs to hydrologicalclass C, a reduction factor of .5 will be appliedas compared to the optimal slope range for thisclass (> 8%).

It is useful to use for Class D, with its very lowpermeability, the analogy of a plastic sheet. Nowater will run away from the sheet, if the slopeis zero. However, the slightest slope will because for runoff. At the other end, one couldvisualize for Class A the same plastic sheet, butfull of holes. Water poured over the sheet willdrain through the holes. To generate runoff, theslope must be quite steep for the water to runoff before it has the time to seep through theholes. Classes B and C have intermediatedrainage properties.

Also in this situation, one has to consider thecase of heterogeneous pixels. Since several soiltypes may occur in a pixel, eventually with dif-ferent hydrological properties, a weighted reduc-tion factor is calculated as follows:

Referring to Table 6, the reduction factor for aparticular soil hydrological class i is then:

if Slope ≥a then RFi = 0

if Slope ≤b then RFi = 1

if Slope between (a,b) then

and

with RF= reduction factor for pixel p; i = class A,B, C or D; wi is weight (percentage) of hydrolog-ical class A, B, C, or D; RFi the reduction factorfor hydrological class i.

The soil-corrected score for slope is, then, takenas the lowest value of either the slope score orthe reduction factor as follows:

Sslope,cor = Min( Sslope, 100-RFp)

The final score for suitability as a catchment is,then, taken as the lowest of the precipitationscore and the soil-corrected slope score:

S = Min (Sslope,cor; Sprecip)

3.1.2.4. Geological materialsSoils on certain parent materials may havesomewhat different hydrological properties thanestimated in Table 4. The soils for which anadjustment of the hydrological class is consid-ered necessary are the shallow soils and rockoutcrops on limestones and dolomites withstrong karstic properties. These soils are shownin bold in Table 4. These soils should be

MACRO-CATCHMENTS

17

Table 6. Soils of Syria: hydrological classes

Hydrological class Soil a b

A 21a; 21b; 21c; 22; 23 40% 15%B 1; 3a; 3b; 4a; 4b; 7a; 7b; 8; 9a; 9b; 10; 15b; 16; 17; 25a; 25b;

25c;30a;30b; 30c; 30d; 32; 37; 42c 15% 8%C 7c; 12a; 12b; 12c; 15a; 19; 31a; 31b; 31c; 38; 39a; 39b; 40a;

40b; 41 8% 3%D 2a; 2b; 2c; 5; 6a; 6b; 11; 13; 14; 18a; 18b; 18c; 18d; 18e; 20a;

20b; 20c; 20d; 20e; 24; 26a; 26b; 26c; 26d; 27; 28; 29; 33; 34; 35a; 35b; 36; 42a; 42b 3% 0%

Figure 8. Reduction factors for soil hydrological classes


18

processed as if they belong to hydrologicalclass B if they coincide with the following parentmaterials, identified from the 1:200,000-scaleGeological Map of Syria:• N1h: Neogene, Helvetian organogenic lime-

stones

• Pg : Paleocene, middle Eocene clayey

limestones, limestones and nummulithiclimestones

• Cr2cm-t: Cretaceous, Cenomanian andTuronian undifferentiated limestones anddolomites

These parent materials were located within theareas of potential suitability by the combineduse of the 1:200,000 geological maps andLandsat imagery.

The hydrological soil classes are then used toapply a reduction factor on the score for slope,as indicated in Table 6 and shown in Figure 7.

3.1.3. Evaluating suitability for agricultural use

3.1.3.1. PrecipitationThe same criterion as for all WH methods andthe same sigmoid membership function withfour inflection points A: 0 (0); B: 150 (1); C: 250(1); D: 500 mm (0).

3.1.3.2. Slopes‘ Flat to gentle’ slopes are optimal. This is simu-lated by a monotonically decreasing sigmoidmembership function, with inflection points A: 0(1); B: 15 (0).

3.1.3.3. SoilsSoils with the following classes are excluded:• Depth class: S,V• Texture class: X• Stoniness class: A,D• Salinity class: S

3.1.3.4. Land useForest and irrigated areas are excluded.

3.1.3.5. Scoring for the agricultural areasScore for agricultural area:

Min(Sslope, Sprecip) – pNS

with pNS = pNS,LULC + pNS,Soil

and pNS,LULC = pforest +pirrigated

and pNS,Soil = pNS,Depth+pNS,Salinity+pNS, Texture

(see also sections 2.2.2.4 and 2.2.2.5)

3.1.4. Combining suitability for catchmentand agricultural uses

The combined suitability for catchment and agri-cultural purposes is assessed by identifyingthose areas where suitable catchments andagricultural areas are close together. The limit-ing distance between the two is taken as 2 km.

3.2. Results

3.2.1. Suitability for macro-catchment use

Map 14 in Annex 5 shows the suitability scoresfor macro-catchment use across Syria.

3.2.2. Suitability for agricultural use

Map 15 in Annex 5 shows the suitability scoresfor agricultural uses.

3.2.3. Suitability for macro-catchment waterharvesting systems

Map 16 in Annex 5 shows the areas that aresuitable for either macro-catchment use or agri-cultural use, in the assumption that they arewithin a close distance from each other.

Figure 9 summarizes the process for identifyingareas suitable for macro-catchment systems.Two small inset maps on the bottom right showtarget areas that meet both requirements ofsuitability for catchment and target use and areclose to each other.

2

2

Fig

ure

9.

Sequence

in a

ssess

ing s

uita

bili

ty f

or

macr

o-c

atc

hm

ent

wate

r harv

est

ing t

ech

niq

ues

19

MACRO-CATCHMENTS

A methodology has been developed for theassessment of suitability for water harvestingfrom a biophysical perspective. The approach isbased on the pixel-level scoring of individualconstraints that have been identified by technol-ogy experts, and their subsequent combinationin a multi-criteria evaluation. The application ofthis methodology has resulted in suitability mapsfor 13 variants of micro-catchment systems anda single generalized macro-catchment system.

The approach can, subject to the availability ofsimilar data, be applied without modifications toother dryland countries. In addition, it is a rela-tively straightforward exercise to modify themethodology to develop a regional or globalmap. In a follow-up study, a regional study willbe presented that extends the current methodol-ogy to all dryland areas.

It needs to be remembered that the method is akind of modeling exercise and therefore,requires validation. This validation will be takenup in the form of a targeted field survey that willcompare the results predicted by the model withan assessment of actual conditions in samplelocations.

REFERENCES

Allen R.G., Pereira L.S., Raes D. and Smith M.1998. Crop evapotranspiration. Guidelines forcomputing crop water requirements. FAOIrrigation and Drainage Paper 56, 300 pp.,FAO, Rome

Choisnel E., de Villele O., Lacroze F. 1992. Uneapproche uniformisée du calcul de l’évapotran-spiration potentielle pour l’ensemble des paysde la Communauté Européenne

Centre Commun de Recherche, Commission desCommunautés Européennes, EUR 14223, 178pp.

De Pauw, E., Oberle, A. and Zoebisch, M. 2004.Land cover and land use in Syria - Anoverview. Jointly published by Asian Institute ofTechnology (AIT), ICARDA and the WorldAssociation of Soil and Water Conservation(WASWC). 47 pp + 1 map A3 + 1 CD. ISBN

974-92678-8-5ESRI. 2005. ArcGIS Desktop Help. Also URL:

http://support.esri.comFAO. 2001. FAOCLIM 2: World-Wide Agroclimatic

Database. CD ROM. FAO, Rome, Italy.Geological Map Syria, scale 1:500,000 and

1:200,000Hutchinson, M.F. 1995. Interpolating mean rainfall

using thin plate smoothing splines, InternationalJournal of Geographical Information Systems,9: 385-403.

Hutchinson, M.F. 2000.ANUSPLIN version 4.1.User Guide. Center for Resource andEnvironmental Studies, Australian NationalUniversity, Canberra.

Köppen W. and Geiger H. 1928. Handbuch derKlimatkunde. Berlin, Germany

Louis Berger International, Inc, Remote SensingInstitute South Dakota University, Agency forInternational Development and the Syrian ArabRepublic. 1982. Land Classification/Soil SurveyProject of the Syrian Arab Republic. Volume 2,Reconnaissance Soil Survey, 1:500,000 scale

Oweis, T., D. Prinz and A. Hachum. 2001. Waterharvesting: indigenous knowledge for the futureof the drier environments. ICARDA, Aleppo,Syria. 40 pp. ISBN 92-9127-116-0

Oweis, T. 2004. Rainwater harvesting for alleviatingwater scarcity in the drier environments of WestAsia and North Africa. Paper presented at theInternational Workshop on Water Harvestingand Sustainable Agriculture, September 7th,2004, Moscow, Russia.(www.fao.org/ag/agl/aglw/wh/docs/Oweis.doc)

Soil Survey Staff. 1975. Soil Taxonomy. A basicsystem of soil classification for making andinterpreting soil surveys. Soil ConservationService, US Department of Agriculture,Agriculture Handbook No. 436.

Technoexport. 1967. The Geology of Syria, Map 1:500.000 and Explanatory notes. Prepared forthe Ministry of Industry, S.A.R. and Ministry ofGeology, USSR.

UNESCO, 1979. Map of the world distribution ofarid regions. Map at scale 1:25,000,000 withexplanatory note. United Nations Educational,Scientific and Cultural Organization, Paris, 54pp. ISBN 92-3-101484-6

Van de Steeg, J. and E. De Pauw. 2002. Overviewof the soil resources of Syria. Research Report.Natural Resources Management Program,ICARDA, Aleppo, Syria, 65 pp.

20

4. CONCLUSIONS AND FOLLOW UP

21

ANNEX 1. SOIL ASSOCIATION COMPOSITION

A11a 3b 70 7b 30A11b 3a 45 13 35 18b 10 7b 10A11c 3a 50 19 30 18b 10 2b 10A11d 3b 50 19 20 18b 15 7b 10 42b 5A11e 3a 50 19 20 18b 10 7b 10 2b 10A11f 3a 70 7a 20 2a 10A11g 3a 50 7b 20 13 20 18b 5 2b 5A11h 3a 45 2a 20 18b 10 7b 10 12c 10 42b 5A11i 3a 60 12c 30 9a 10A11j 3a 50 12c 20 11 20 18c 10A11k 3a 50 18b 25 2a 10 7b 10 42b 5A11m 3a 60 32 30 9b 10A12a 2c 40 3a 25 18b 20 7b 15A12b 2c 50 18b 30 7b 20A12c 2c 40 18b 30 3a 20 42b 10A12d 2c 50 18b 20 4b 20 42b 10A13a 4a 50 25b 30 3a 10 7b 10A13b 4a 40 3a 30 13 10 9a 10 25b 10A21a 7b 40 18d 20 6a 20 5 10 15b 10A21b 7b 80 3a 20A21c 7b 65 4a 20 3b 10 15b 5A21d 7c 40 6b 30 18e 20 42b 10A22a 8 70 4a 30A22b 8 50 6b 30 18e 15 42b 5A31a 12b 50 9b 30 3a 10 11 10A31b 12b 50 3a 30 7a 10 9b 5 10 5A31c 12c 40 3a 20 19 20 9a 10 4b 10A31d 12c 40 11 30 3a 15 9a 15A31e 12c 50 3a 20 9a 15 18b 10 42b 5A31f 12c 50 3a 20 11 20 9a 10A31g 12a 50 3a 30 9a 10 10 10A31h 12a 40 3a 20 9a 20 4b 10 11 5 13 5A31i 12c 70 15a 30A31j 12c 40 15a 30 11 20 3a 10A31k 12a 50 11 30 3a 20A32a 11 60 18a 20 12c 20A32b 11 40 12c 20 19 20 3a 10 22 10A32c 11 40 12c 20 3a 20 13 10 42b 10A41a 13 60 3b 40A51a 15a 70 14 30E11a 16 70 15a 15 19 10 14 5E21a 17 80 21a 20E41a 26 100E51a 18b 70 42b 20 2c 10E51b 18b 50 19 30 3b 15 42b 5E51c 18b 60 42b 30 19 10E51d 18c 50 12c 30 42b 20E51e 18c 50 19 20 42b 10 12c 10 3a 5 11 5E51f 18c 40 11 30 12b 20 42b 10E61a 20c 40 26c 30 42b 20 30d 10E61b 20c 60 35b 20 26d 15 42b 5E61c 20b 80 26b 10 42b 10E61d 20a 50 26a 20 42b 10 25a 10 28 10E61e 20c 45 26c 20 25a 20 42b 10 28 5E61f 20a 70 42b 20 26a 10E61g 20a 40 42b 30 26a 20 30c 10

So

ilasso

cia

tio

n

So

il 1

% S

oil 1

So

il 2

% S

oil 2

So

il 3

% S

oil 3

So

il 4

% S

oil 4

So

il 5

% S

oil 5

So

il 6

% S

oil

6

So

il 7

% S

oil

7

ANNEX 1. continued

E61h 20a 40 42b 20 26a 20 30c 10 35a 5 34 5E61i 20a 40 42b 30 26a 10 30c 10 34 5 36 5E61j 20e 50 26a 30 1 10 30a 10 42b 0I11a 30a 70 31a 30I11b 30b 40 31c 30 21c 20 40a 10I11c 30d 40 42c 30 20c 10 26d 10 31b 10I11d 30a 40 26b 30 20b 10 28 10 42b 10I11e 30a 60 25c 30 26c 10I11f 30b 60 26d 30 28 10I12a 25b 40 3a 30 12b 30I12b 25b 40 30b 30 26b 10 20b 10 31c 10I12c 25b 40 3a 20 7c 20 26c 10 30d 10I12d 25c 40 31a 30 15a 10 39a 10 40a 10I12e 25b 50 30b 20 4b 20 26b 10I12f 25b 50 30b 15 31c 10 28 10 26a 5 42b 5 20a 5I13a 26b 50 20c 40 28 10I13b 26d 50 30d 20 31b 10 29 10 42b 5 20c 5I13c 26a 40 35b 30 20d 10 30c 10 42b 10I13d 26b 40 20b 20 28 20 30a 10 25b 10I13e 26a 50 30a 30 42b 10 28 10I13f 26b 40 28 30 25b 10 30d 10 31c 10I13g 26c 40 25a 30 3a 20 18a 5 13 5I13h 26b 40 25c 30 20b 10 28 10 30a 10I13i 26b 40 30a 20 21c 20 20b 10 42b 10I13j 26b 40 25b 30 20c 20 42b 10I14a 28 70 25c 30I14b 28 60 26d 20 20b 10 30a 10I15a 31c 40 30b 20 40a 20 37 10 41 10I15b 31b 50 40b 20 30d 15 26c 10 42a 5I15c 31b 50 30d 20 40b 10 42a 10 26c 10I15d 31c 50 40a 20 25b 20 30b 10M11a 37 40 30a 30 36 20 33 10M11b 37 35 30a 30 24 20 21b 10 40a 5M11c 37 35 30d 25 31a 20 42b 5 20e 5 23 5 26a 5M12a 35b 40 30a 30 26c 10 36 10 38 10M12b 35a 30 26b 20 27 20 30a 20 20d 10V11a 40b 70 39b 30V11b 40b 40 31c 30 30a 20 26a 10 42b -X11a 42a 100X11b 42a 100X11c 42a 100X11d 42a 100X12a 42b 100

Explanatory notes: Column 1: Soil association labelColumn 2: 1st soil type of the soil associationColumn 3: Estimated percentage of the 1st soil type in the soil associationColumn 4: 2nd soil type of the soil associationColumn 5: Estimated percentage of the 2nd soil type in the soil associationColumn 6: 3rd soil type of the soil associationColumn 7: Estimated percentage of the 3rd soil type in the soil associationColumn 8: 4th soil type of the soil associationColumn 9: Estimated percentage of the 4th soil type in the soil associationColumn 10: 5th soil type of the soil associationColumn 11: Estimated percentage of the 5th soil type in the soil associationColumn 12: 6th soil type of the soil associationColumn 13: Estimated percentage of the 6th soil type in the soil associationColumn 14: 7th soil type of the soil associationColumn 15: Estimated percentage of the 7th soil type in the soil association

So

ilasso

cia

tio

n

So

il 1

% S

oil

1

So

il 2

% S

oil

2

So

il 3

% S

oil

3

So

il 4

% S

oil

4

So

il 5

% S

oil 5

So

il 6

% S

oil

6

So

il 7

% S

oil

7


22

SO

IL_ID

1S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

escri

pti

on

taxo

no

my

cla

ss

2cla

ss

3cla

ss

4cla

ss

5g

ical

cla

ss

6

lab

el

1T

ypic

M

YN

NB

Well

dra

ined,

modera

tely

deep,

dark

bro

wn t

o d

ark

Haplo

xera

lfsre

ddis

h b

row

n,

claye

y te

xture

d s

oils

with

cla

yacc

um

ula

tion in d

epth

. T

he s

ubso

il is

str

ongly

st

ruct

ure

d.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m.

The s

oil

mo

istu

re r

eg

ime

7is

xeric.

2a

Lith

ic

SC

AN

DW

ell

dra

ine

d,

sha

llow

, st

ron

g b

row

n t

o r

ed

dis

h y

ello

w,

Calc

iort

hid

scl

aye

y te

xture

d s

oils

. T

he s

ubso

il is

weak

to

modera

tely

str

uct

ure

d.

The s

urf

ace

conta

ins

abundant

stones.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

ha

ve a

ho

rizo

n e

nrich

ed

in

ca

lciu

m

carb

onate

an

d a

re d

eve

lop

ed

on

lim

est

on

e,

ma

rl

or

basa

lt. T

he s

oil

mois

ture

regim

e is

aridic

.2b

Lith

ic

SC

V-F

ND

Well

dra

ine

d,

sha

llow

, st

ron

g b

row

n t

o r

ed

dis

h y

ello

w,

Calc

iort

hid

scl

aye

y te

xture

d s

oils

. T

he s

ubso

il is

weakl

y st

ruct

ure

d.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

urf

ace

conta

ins

few

to v

ery

few

sto

nes.

T

he s

oils

ha

ve a

ho

rizo

n e

nrich

ed

in

ca

lciu

m

carb

onate

an

d a

re d

eve

lop

ed

on

lim

est

on

e,

ma

rl

or

basa

lt. T

he s

oil

mois

ture

regim

e is

aridic

.2

cLith

ic

SY

VN

DW

ell

dra

ined,

shallo

w,

stro

ng b

row

n,

claye

y te

xture

dC

alc

iort

hid

sso

ils.

The s

ubso

il is

modera

tely

str

uct

ure

d.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

urf

ace

conta

ins

very

few

sto

nes.

The s

oils

have

a h

orizo

n e

nrich

ed in

calc

ium

ca

rbo

na

te a

nd

are

de

velo

pe

d o

n lim

est

on

eor

basa

lt. T

he s

oil

mois

ture

regim

e is

aridic

.3a

Typ

ic

DC

FN

BW

ell

dra

ine

d,

de

ep

, st

ron

g b

row

n t

o r

ed

dis

h y

ello

w,

Calc

iort

hid

scl

aye

y te

xture

d s

oils

. T

he s

ubso

il is

weakl

y st

ruct

ure

dand c

onta

ins

be

twe

en

15

an

d 3

5%

gra

vel.

The s

urf

ace

conta

ins

few

sto

nes.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

X-r

ay

am

orp

hous,

an

d h

ave

a h

orizo

n e

nrich

ed

in

ca

lciu

m

carb

onate

. T

hey

are

deve

loped o

n p

yrocl

ast

ic m

ate

rial.

The s

oil

mois

ture

regim

e is

aridic

.1A

ll so

il ty

pes

conta

in f

ree C

aC

O3.

For

this

reaso

n t

he a

ttribute

is

not

repeate

d in t

he d

esc

riptio

ns.

2F

or

exp

lanatio

n o

f depth

cla

ss s

ymbols

, se

e T

able

3 in s

ect

ion 2

.2.3

.3.

3F

or

exp

lanatio

n o

f te

xture

cla

ss s

ymbols

, se

e T

able

3 in s

ect

ion 2

.2.3

.3.

4S

ee e

xpla

nato

ry n

ote

s at

the e

nd o

f th

is t

able

rela

ted t

o s

tonin

ess

cla

sses

5F

or

exp

lanatio

n o

f sa

linity

cla

ss s

ymbols

, se

e T

able

3 in s

ect

ion 2

.2.3

.3.

6F

or

exp

lanatio

n o

f hyd

rolo

gic

al cl

ass

sym

bols

, se

e s

ect

ion 3

.1.2

.3.

7S

ee e

xpla

nato

ry n

ote

s at

the e

nd o

f th

is t

able

rela

ted t

o s

oil

mois

ture

regim

es.

23

ANNEX 2. BRIEF DESCRIPTION OF SOIL TYPES IN SYRIA


24

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

esc

rip

tio

n

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

3b

Typ

ic

DY

VN

BW

ell

dra

ined,

deep,

yello

w t

o v

ery

pale

bro

wn,

claye

y C

alc

iort

hid

sso

ils.

The s

ubso

il is

weakl

y st

ruct

ure

d a

nd c

onta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The s

urf

ace

conta

ins

very

few

sto

nes.

The A

WC

is

betw

een 1

50 a

nd

200 m

m/m

. T

he s

oils

are

deve

loped o

n lacu

strine

or

fluvi

al deposi

ts.

The s

oils

have

a h

orizo

n e

nrich

ed

in c

alc

ium

carb

onate

. T

he s

oil

mois

ture

regim

e is

aridic

.4a

Xero

llic

DL

F-C

NB

Well

dra

ined,

deep,

dark

bro

wn t

o b

row

n,

loam

y so

ils.

Calc

iort

hid

sT

he s

ubso

il is

weakl

y st

ruct

ure

d a

nd c

onta

ins

more

th

an 3

5%

gra

vel.

The s

urf

ace

conta

ins

few

to c

om

mon

stones.

The A

WC

is b

etw

een 1

50 a

nd 2

00 m

m/m

. T

he s

oils

have

vert

ic p

ropert

ies

and a

horizo

n e

nrich

ed

in c

alc

ium

carb

onate

The s

oil

mois

ture

regim

e is

aridic

.4b

Xero

llic

DL

M-A

NB

Well

dra

ined,

deep,

dark

bro

wn t

o b

row

n,

loam

y so

ils.

Calc

iort

hid

sT

he s

ubso

il is

weakl

y st

ruct

ure

d a

nd c

onta

ins

more

th

an 3

5%

gra

vel.

The s

urf

ace

conta

ins

many

to

abundant

stones.

The s

oils

have

vert

ic p

ropert

ies

and h

ave

a h

orizo

n e

nrich

ed in

calc

ium

carb

onate

. T

he A

WC

is b

etw

een 1

50 a

nd 2

00 m

m/m

. T

he s

oils

are

deve

loped o

n li

mest

one,

marl a

nd g

ypsu

m.

The s

oil

mois

ture

regim

e is

aridic

.5

Aquic

D

YN

GD

Imperf

ect

ly d

rain

ed,

deep,

dark

bro

wn t

o v

ery

pale

C

am

bort

hid

sbro

wn,

claye

y so

ils.

The s

ubso

il is

modera

tely

st

ruct

ure

d.

The s

urf

ace

conta

ins

very

few

sto

nes.

T

he A

WC

is b

etw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

deve

loped o

n li

mest

one o

r co

lluvi

al d

eposi

ts.

The s

oil

mois

ture

regim

e is

aridic

.6a

Lith

ic

SY

NN

DW

ell

dra

ined,

shallo

w,

dark

bro

wn t

o y

ello

wis

h b

row

n,

Cam

bort

hid

scl

aye

y so

ils.

The s

ubso

il is

modera

tely

str

uct

ure

d.

The s

urf

ace

is f

ree o

f st

ones.

The A

WC

is b

etw

een

150 a

nd 2

00 m

m/m

. T

he s

oils

have

vert

ic p

ropert

ies

and a

re d

eve

loped o

n b

asa

lts.

The s

oil

mois

ture

re

gim

e is

aridic

.6b

Lith

ic

SY

DN

DW

ell

dra

ined,

shallo

w,

bro

wn t

o s

trong b

row

n,

claye

y C

am

bort

hid

sso

ils.

The s

ubso

il is

str

uct

ure

less

and c

onta

ins

more

th

an 3

5%

gra

vels

. T

he s

urf

ace

pre

dom

inantly

conta

ins

stones

and r

ock

s. T

he A

WC

is le

ss t

han 2

0 m

m/m

. T

he s

oils

have

vert

ic p

ropert

ies,

and a

re d

eve

loped

on li

mest

one,

marl,

gyp

sum

, co

lluvi

al o

r m

arine

deposi

ts.

The s

oil

mois

ture

regim

e is

aridic

.

25

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5cla

ss

6g

ical

cla

ss

7

lab

el

7a

Typ

ic

DC

NN

BW

ell

dra

ined,

deep,

reddis

h y

ello

w t

o s

trong b

row

n,

Cam

bort

hid

scl

aye

y te

xture

d s

oils

. T

he B

subso

il is

modera

tely

st

ruct

ure

d.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

have

vert

ic p

ropert

ies,

and a

re d

eve

loped

on lim

est

one a

nd/o

r basa

lts.

The s

oil

mois

ture

re

gim

e is

arid

ic.

7b

Typ

ic

DC

N-V

NB

Well

dra

ine

d,

de

ep

, ye

llow

ish

bro

wn

to

da

rk b

row

n,

Cam

bort

hid

scl

aye

y te

xture

d s

oils

. T

he s

ubso

il is

modera

tely

st

ruct

ure

d.

The s

urf

ace

conta

ins

no t

o v

ery

few

st

ones.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

have

vert

ic p

ropert

ies,

and a

re d

eve

loped

on c

ollu

via

l d

ep

osi

ts.

Th

e s

oil

mo

istu

re r

eg

ime

is

arid

ic.

7c

Typ

ic

MC

A-D

NC

Well

dra

ine

d,

mo

de

rate

ly d

ee

p,

yello

wis

h b

row

n t

o

Cam

bort

hid

sye

llow

ish r

ed,

claye

y te

xture

d s

oils

. T

he s

ubso

il is

m

odera

tely

str

uct

ure

d a

nd c

onta

ins

more

than

35%

gra

vel.

Sto

nes

and r

ock

s dom

inate

the s

urf

ace

. T

he A

WC

is

betw

een 6

0 a

nd 1

00 m

m/m

. T

he s

oil

mois

ture

regim

e is

aridic

.8

Xero

llic

DY

NN

BW

ell

dra

ine

d,

de

ep

, ye

llow

ish

bro

wn

, cl

aye

y te

xtu

red

C

am

bort

hid

sso

ils.

The s

ubso

il is

weakl

y st

ruct

ure

d.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

deve

lop

ed

on

lim

est

on

e.

Th

e s

oil

mo

istu

re r

eg

ime

is

arid

ic.

9a

Calc

ic

M-D

CN

GB

Well

dra

ined,

(modera

tely

) deep,

stro

ng b

row

n,

claye

y G

ypsi

ort

hid

ste

xture

d s

oils

. T

he s

ubso

il is

weakl

y st

ruct

ure

d a

nd

conta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The A

WC

is

betw

een 6

0 a

nd 1

00 m

m/m

. T

he s

oils

have

a h

orizo

n

enrich

ed

in

bo

th g

ypsu

m a

nd

ca

lciu

m c

arb

on

ate

T

he s

oil

mois

ture

regim

e is

aridic

.9b

Calc

ic

M-D

LN

GB

Well

dra

ine

d,

(mo

de

rate

ly)

de

ep

, d

ark

ye

llow

ish

G

ypsi

ort

hid

sbro

wn,

loam

y te

xture

d s

oils

. T

he s

ubso

il is

weakl

y to

m

ode

rate

ly s

tru

ctu

red

an

d c

on

tain

s b

etw

ee

n 1

5 a

nd

35%

gra

vel.

The A

WC

is

betw

een 1

00 a

nd

150 m

m/m

. T

he s

oils

have

a h

orizo

n e

nrich

ed in b

oth

gyp

sum

and c

alc

ium

carb

onate

The s

oil

mois

ture

re

gim

e is

arid

ic.

ANNEX 2


26

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

10

Cam

bic

D

YN

GB

Well

dra

ine

d,

de

ep

, ye

llow

ish

bro

wn

, cl

aye

y te

xtu

red

G

ypsi

ort

hid

sso

ils.

The s

ubso

il is

weakl

y st

ruct

ure

d.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

have

a h

orizo

n

enrich

ed

in

gyp

sum

an

d a

re d

eve

lop

ed

on

lim

est

on

e

or

basa

lt. T

he s

oil

mois

ture

regim

e is

aridic

.11

Petr

ogyp

sic

SL

NG

DS

hallo

w s

oils

, w

ith v

ary

ing d

egre

e o

f st

onin

ess

. G

ypsi

ort

hid

sT

he s

oils

ha

ve a

str

on

gly

ce

me

nte

d h

orizo

n e

nrich

ed

in

gyp

sum

an

d a

re d

eve

lop

ed

on

lim

est

on

e a

nd

/or

gyp

sum

, o

r co

lluvi

al d

ep

osi

ts.

12a

Typ

ic

DC

V-F

GC

Well

dra

ine

d,

de

ep

, ye

llow

ish

bro

wn

to

re

dd

ish

bro

wn

, G

ypsi

ort

hid

scl

aye

y te

xture

d s

oils

. T

he s

ubso

il is

weakl

y st

ruct

ure

d.

The

su

rfa

ce c

on

tain

s ve

ry f

ew

to

fe

w s

ton

es.

T

he A

WC

is

betw

een 1

00 a

nd 1

50 m

m.

The s

oils

are

sa

line

, a

nd

ha

ve a

ho

rizo

n e

nrich

ed

in

gyp

sum

. T

he

y a

re d

eve

lop

ed

on

la

cust

rin

e o

r flu

via

l d

ep

osi

ts.

The s

oil

mois

ture

regim

e is

aridic

.12b

Typ

ic

DL

NG

CW

ell

dra

ine

d,

de

ep

, d

ark

ye

llow

ish

bro

wn

to

ye

llow

ish

G

ypsi

ort

hid

sbro

wn,

loam

y te

xture

d s

oils

. T

he s

ubso

il is

st

ruct

ure

less

an

d c

on

tain

s b

etw

ee

n 1

5 a

nd

35

%

gra

vel.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

sa

line

, a

nd

ha

ve a

ho

rizo

n e

nrich

ed

in

gyp

sum

. T

he

y a

re d

eve

lop

ed

on

la

cust

rin

e a

nd

flu

vial deposi

ts.

The s

oil

mois

ture

regim

e is

aridic

.12c

Typ

ic

DL

N-V

GC

Well

dra

ine

d,

de

ep

, b

row

nis

h y

ello

w t

o y

ello

wis

h

Gyp

siort

hid

sbro

wn,

loam

y te

xture

d s

oils

. T

he s

ubso

il is

st

ruct

ure

less

. T

he s

urf

ace

conta

ins

none t

o v

ery

few

st

ones.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m.

The

so

ils a

re s

alin

e,

an

d h

ave

a h

orizo

n e

nrich

ed

in

gyp

sum

. T

hey

are

deve

loped o

n lacu

strine o

r flu

vial

deposi

ts.

The s

oil

mois

ture

regim

e is

aridic

.13

Typ

ic

DY

NN

DV

ery

sh

allo

w,

we

ll d

rain

ed

so

ils.

Th

e s

oils

ha

ve a

P

ale

ort

hid

sst

ron

gly

ce

me

nte

d la

yer

in d

ep

th a

nd

are

de

velo

pe

d

on lim

est

on

e a

nd

/or

collu

via

l d

ep

osi

ts.

14

Aquolli

c D

CN

SD

Mode

rate

ly w

ell

dra

ine

d,

de

ep

, d

ark

bro

wn

to

gra

yish

S

alo

rthid

sbro

wn,

claye

y te

xture

d s

oils

. T

he s

ubso

il is

st

ruct

ure

less

. T

he A

WC

is

betw

een 1

00 a

nd

150

mm

/m.

Th

e s

oils

are

sa

line

an

d a

re d

eve

lop

ed

on

limest

one a

nd b

asa

lt. T

he s

oil

mois

ture

regim

e is

aridic

.

27

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

15a

Typ

ic

DC

NS

CM

od

era

tely

we

ll d

rain

ed

, d

ee

p,

da

rk b

row

n t

o b

row

n,

Salo

rthid

scl

aye

y te

xture

d s

oils

. T

he s

ubso

il is

str

uct

ure

less

. T

he A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

sa

line

an

d a

re d

eve

lop

ed

on

flu

via

l d

ep

osi

ts.

The s

oil

mois

ture

regim

e is

aridic

.15b

Typ

ic

DL

VS

BM

ode

rate

we

ll d

rain

ed

, d

ee

p,

ligh

t ye

llow

ish

bro

wn

to

S

alo

rthid

spale

bro

wn,

loam

y te

xture

d s

oils

. T

he s

ubso

il is

m

odera

te t

o s

trong s

truct

ure

d.

The s

urf

ace

conta

ins

very

few

sto

nes.

The A

WC

is

betw

een 1

00 a

nd

150 m

m/m

. T

he s

oils

are

salin

e a

nd a

re d

eve

loped

on lim

est

one.

The s

oil

mois

ture

regim

e is

aridic

.16

Typ

ic

DC

NN

BM

ode

rate

ly w

ell

dra

ine

d,

de

ep

, g

rayi

sh b

row

n,

cla

yey

Torr

ifluve

nts

soils

. T

he s

ubso

il is

str

uct

ure

less

. T

he A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on lim

est

one.

The s

oil

mois

ture

regim

e is

aridic

.17

Typ

ic

DY

NN

BM

ode

rate

ly w

ell

dra

ine

d,

de

ep

, d

ark

gra

yish

bro

wn

to

X

ero

fluve

nts

gra

yish

bro

wn,

claye

y so

ils.

The s

ubso

il is

st

ruct

ure

less

. T

he A

WC

is

betw

een 1

00 a

nd

150 m

m/m

. T

he s

oils

have

gle

yic

pro

pert

ies.

The s

oils

are

de

velo

pe

d o

n la

cust

rin

e d

ep

osi

ts.

The s

oil

mois

ture

regim

e is

aridic

.18a

Lith

ic

SC

AN

DW

ell

dra

ine

d,

sha

llow

, d

ark

ye

llow

ish

bro

wn

, cl

aye

y T

orr

iort

hents

soils

. T

he s

ubso

il is

weakl

y st

ruct

ure

d a

nd c

onta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The A

WC

is

betw

een

20 a

nd 6

0 m

m/m

. T

he s

oil

mois

ture

regim

e is

aridic

.18b

Lith

ic

SC

AN

DW

ell

dra

ine

d,

sha

llow

, ye

llow

ish

bro

wn

to

str

on

g

Torr

iort

hents

bro

wn,

claye

y so

ils.

The s

ubso

il is

weakl

y st

ruct

ure

d,

and c

on

tain

s b

etw

ee

n 1

5 a

nd

35

% g

rave

l. T

he s

urf

ace

has

abundant

stones

and r

ock

s.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

lop

ed

on

lim

est

on

e,

gyp

sum

, a

nd

ba

salt.

T

he s

oil

mois

ture

regim

e is

aridic

.18c

Lith

ic

SC

N-F

ND

Well

dra

ine

d,

sha

llow

, d

ark

ye

llow

ish

bro

wn

, cl

aye

y T

orr

iort

hents

soils

. T

he s

ubso

il is

weakl

y st

ruct

ure

d,

and c

onta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The s

urf

ace

conta

ins

no

to f

ew

sto

nes.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

loped o

n g

ypsi

c ro

cks.

ANNEX 2


28

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5cla

ss

6g

ical

cla

ss

7

lab

el

18d

Lith

ic

VL

AN

DW

ell

dra

ined,

very

shallo

w,

dark

gra

yish

bro

wn t

o

Torr

iort

hents

gra

y, loam

y so

ils.

The s

ubso

il co

nta

ins

betw

een 1

5

and 3

5%

gra

vel.

The A

WC

is

betw

een 2

0 a

nd

60 m

m/m

. T

he s

oils

are

deve

loped o

n lacu

strine

deposi

ts.

The s

oil

mois

ture

regim

e is

aridic

.1

8e

Lith

ic

SY

AN

DW

ell

dra

ine

d,

sha

llow

, ye

llow

ish

bro

wn

, cl

aye

y so

ils.

Torr

iort

hents

The

su

bso

il co

nta

ins

be

twe

en

15

an

d 3

5%

gra

vel.

The s

urf

ace

rock

iness

is

abundant. T

he A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

loped

on b

asa

lt.19

Typ

ic

DC

AN

CW

ell

dra

ine

d,

de

ep

, ye

llow

ish

bro

wn

to

str

on

g b

row

n,

Torr

iort

hents

claye

y so

ils.

The s

ubso

il co

nta

ins

more

than 3

5%

gra

vel.

The s

urf

ace

conta

ins

abundant

stones.

T

he A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

lop

ed

on

la

cust

rin

e d

ep

osi

ts.

Th

e s

oil

mo

istu

re

regim

e is

arid

ic.

20a

Lith

ic

VC

AN

DW

ell

dra

ined,

very

shallo

w,

light

gra

y to

dark

gra

yish

X

ero

rthents

bro

wn,

claye

y so

ils.

The s

ubso

il co

nta

ins

betw

een 1

5

and 3

5%

gra

vel.

The A

WC

is

betw

een 2

0 a

nd

60 m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

ones

and f

luvi

al deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

20b

Lith

ic

VC

AN

DW

ell

dra

ine

d,

very

sh

allo

w,

red

dis

h b

row

n,

cla

yey

Xero

rthents

soils

. T

he s

ubso

il is

weakl

y st

ruct

ure

d a

nd c

onta

ins

betw

een 5

and 3

5%

gra

vel.

The s

urf

ace

conta

ins

com

mon r

ock

s. T

he A

WC

is

betw

een 2

0 a

nd

60 m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

ones

and m

arls.

The s

oil

mois

ture

regim

e is

xeric.

20c

Lith

ic

SC

AN

DW

ell

dra

ined,

shallo

w,

dark

bro

wn t

o b

row

n,

claye

y .

Xero

rthents

soils

. T

he d

epth

is

very

shallo

w.

The s

ubso

il co

nta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The s

urf

ace

conta

ins

abundant

stones.

The A

WC

is

betw

een 2

0 a

nd

60 m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

one

and m

arls.

The s

oil

mois

ture

regim

e is

xeric.

20d

Lith

ic

SL

CN

DW

ell

dra

ined,

shallo

w,

very

dark

gra

yish

bro

wn t

o

Xero

rthents

gra

yish

bro

wn,

loam

y so

ils.

The s

ubso

il is

weakl

y st

ruct

ure

d a

nd c

onta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The s

urf

ace

conta

ins

com

mon s

tones

and r

ock

s. T

he

depth

is

shallo

w,

the s

oil

conta

ins

com

mon t

o m

any

rock

s. T

he A

WC

is

low

er

than 2

0 m

m/m

. T

he s

oils

are

deve

lop

ed

on

ba

salt

an

d/o

r lim

est

on

e.

The s

oil

mois

ture

regim

e is

xeric.

29

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

20e

Lith

ic

VS

AN

DW

ell

dra

ine

d,

very

sh

allo

w,

yello

wis

h b

row

n t

o d

ark

X

ero

rthents

gra

yish

bro

wn,

sandy

soils

. T

he A

WC

is

low

er

than

20 m

m/m

. T

he s

oils

are

deve

loped o

n b

asa

lt.

The s

oil

mois

ture

regim

e is

xeric.

21a

Typ

ic

DS

NN

AE

xcess

ively

dra

ined,

deep,

sandy

soils

. T

he s

ubso

il X

ero

rthents

conta

ins

more

than 3

5%

gra

vel.

The A

WC

is

low

er

than 2

0 m

m/m

. T

he s

oil

mois

ture

regim

e is

xeric.

21b

Typ

ic

DC

AN

AE

xce

ssiv

ely

dra

ine

d,

de

ep

, re

dd

ish

bro

wn

to

ye

llow

ish

X

ero

rthents

red,

claye

y te

xture

d s

oils

. T

he s

ubso

il is

modera

tely

st

ruct

ure

d a

nd c

onta

ins

more

than 3

5%

gra

vel.

The s

urf

ace

conta

ins

abundant

stones.

The A

WC

is

low

er

than 2

0 m

m/m

. T

he s

oil

mois

ture

regim

e is

xeric.

21c

Typ

ic

M-D

CA

NA

Exc

ess

ive

ly d

rain

ed

, (m

od

era

te)

de

ep

, re

dd

ish

bro

wn

X

ero

rthents

to y

ello

wis

h r

ed,

claye

y te

xture

d s

oils

. T

he s

ubso

il is

m

odera

tely

str

uct

ure

d a

nd c

onta

ins

more

than 3

5%

gra

vel.

The s

urf

ace

conta

ins

abundant

stones

and

com

mon r

ock

s. T

he A

WC

is

low

er

than 2

0 m

m/m

. T

he s

oils

are

de

velo

pe

d o

n lim

est

on

e.

The s

oil

mois

ture

regim

e is

xeric.

22

Typ

ic

DS

CN

AE

xcess

ively

dra

ined,

deep,

stro

ng b

row

n,

sandy

soils

. T

orr

ipsa

mm

ents

The A

WC

is

betw

een 6

0 a

nd 1

00m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

one o

r basa

lt. T

he s

oil

mois

ture

re

gim

e is

arid

ic.

23

Typ

ic

DS

-XN

NA

Exc

ess

ively

dra

ined,

deep,

bro

wn,

sandy

soils

. T

he

Xero

psa

mm

ents

AW

C is

betw

een 6

0 a

nd 1

00 m

m/m

. T

he s

oils

are

deve

lop

ed

on

co

lluvi

al d

ep

osi

ts.

The s

oil

mois

ture

regim

e is

xeric.

24

Aq

uic

D

CN

ND

Imperf

ect

ly d

rain

ed,

deep,

gra

yish

bro

wn,

claye

y so

ils.

Xe

roch

rep

tsT

he s

ubso

il is

str

uct

ure

less

or

weakl

y deve

loped.

The

AW

C is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

deve

lop

ed

on

lim

est

on

e,

or

collu

via

l, flu

via

l o

r la

cust

rine d

eposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

25a

Calc

ixero

llic

M-D

YN

NB

Well

dra

ine

d,

(mo

de

rate

ly)

de

ep

, d

ark

re

dd

ish

bro

wn

, X

ero

chre

pts

claye

y so

ils.

The s

ubso

il is

str

ongly

str

uct

ure

d a

nd

conta

ins

15 t

o 3

5%

gra

vel.

The A

WC

is

betw

een

100 a

nd 1

50 m

m/m

. T

he s

oils

are

deve

loped o

n

limest

one.

The s

oil

mois

ture

regim

e is

xeric.

25b

Calc

ixero

llic

DC

FN

BW

ell

dra

ined,

deep,

reddis

h b

row

n t

o s

trong b

row

n,

Xero

chre

pts

claye

y so

ils.

The s

ubso

il is

modera

tely

str

uct

ure

d a

nd

conta

ins

15-3

5%

gra

vel.

The s

urf

ace

conta

ins

few

st

ones.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oil

mois

ture

regim

e is

xeric.

ANNEX 2


30

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

25c

Calc

ixero

llic

DY

NN

BW

ell

dra

ine

d,

de

ep

, d

ark

re

dd

ish

bro

wn

to

ye

llow

ish

X

ero

chre

pts

bro

wn,

claye

y so

ils.

The s

ubso

il is

weakl

y st

ruct

ure

d

and c

onta

ins

15-3

5%

gra

vel.

The s

urf

ace

is

free o

f st

ones.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he

so

ils a

re d

eve

lop

ed

on

ma

rin

e d

ep

osi

ts.

The s

oil

mois

ture

regim

e is

xeric.

26a

Lith

ic

SY

AN

DW

ell

dra

ine

d,

sha

llow

, ye

llow

ish

re

d t

o r

ed

dis

h b

row

n,

Xe

roch

rep

tscl

aye

y so

ils.

The s

ubso

il is

weakl

y to

str

ongly

st

ruct

ure

d a

nd c

onta

ins

15 t

o 3

5%

gra

vel.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

have

gle

yic

pro

pe

rtie

s. T

he

so

ils a

re d

eve

lop

ed

on

la

cust

rin

e

deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

26b

Lith

ic

SC

AN

DW

ell

dra

ine

d,

sha

llow

, re

dd

ish

bro

wn

to

da

rk b

row

n,

Xero

chre

pts

claye

y so

ils.

The s

ubso

il is

modera

tely

str

uct

ure

d a

nd

conta

ins

15 t

o 3

5%

gra

vel.

The s

urf

ace

conta

ins

abundant

stones

and c

om

mon t

o a

bundant

rock

s.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

lop

ed

on

lim

est

on

e a

nd

flu

via

l d

ep

osi

ts.

The s

oil

mois

ture

regim

e is

xeric.

26c

Lith

ic

SC

CN

DW

ell

dra

ine

d,

sha

llow

, re

dd

ish

bro

wn

to

da

rk b

row

n,

Xero

chre

pts

claye

y so

ils.

The s

ubso

il is

weakl

y to

modera

tely

st

ruct

ure

d a

nd c

onta

ins

15 t

o 3

5%

gra

vel.

The s

urf

ace

co

nta

ins

com

mon s

tones

and c

om

mon t

o a

bundant

rock

s. T

he A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

de

velo

pe

d o

n lim

est

on

e a

nd

co

lluvi

al

deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

26d

Lith

ic

SC

F-M

ND

Well

dra

ined,

shallo

w,

very

dark

gra

yish

bro

wn t

o

Xero

chre

pts

bro

wn,

claye

y so

ils.

The s

ubso

il is

weakl

y to

moder

ate

ly s

truct

ure

d a

nd c

onta

ins

15 t

o 3

5%

gra

vel.

The s

urf

ace

conta

ins

few

to m

any

stones.

The A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

loped

on lim

est

one.

The s

oil

mois

ture

regim

e is

xeric.

27

Lith

ic-V

ert

ic

SC

AN

DW

ell

dra

ined,

shallo

w,

dark

bro

wn t

o b

row

n,

claye

y X

ero

chre

pts

soils

. T

he s

ubso

il is

str

ongly

str

uct

ure

d a

nd c

onta

ins

15 t

o 3

5%

gra

vel.

The s

urf

ace

conta

ins

abundant

ston

es

an

d c

om

mo

n r

ock

s a

nd

sh

ow

s co

nsi

de

rab

le

shrink-

swell

pro

pert

ies.

The A

WC

is

betw

een 2

0 a

nd

60 m

m/m

. T

he s

oils

are

deve

loped o

n b

asa

lt and

fluvi

al deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

Taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

28

Petr

oca

lcic

S

CN

ND

Well

dra

ined,

shallo

w,

dark

bro

wn t

o b

row

n,

claye

y X

ero

chre

pts

soils

. T

he s

ubso

il is

str

uct

ure

less

and s

trongly

in

dura

ted w

ith c

alc

ium

carb

onate

. T

he A

WC

is

60

to 1

00 m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

one

and b

asa

lt. T

he s

oil

mois

ture

regim

e is

xeric.

29

Ruptic

Lith

ic

SC

CN

DW

ell

dra

ine

d,

sha

llow

, re

dd

ish

bro

wn

to

ye

llow

ish

re

d,

Xero

chre

pts

claye

y so

ils.

The s

ubso

il is

modera

tely

str

uct

ure

d a

nd

conta

ins

15 t

o 3

5%

gra

vel.

The s

urf

ace

conta

ins

com

mon s

tones.

The A

WC

is

60 t

o 1

00 m

m/m

. T

he

so

ils a

re d

eve

lop

ed

on

lim

est

on

e a

nd

ma

rls.

T

he s

oil

mois

ture

regim

e is

xeric.

30a

Typ

ic

DC

NN

BW

ell

or

mo

de

rate

ly w

ell

dra

ine

d,

de

ep

, d

ark

gra

yish

X

ero

chre

pts

bro

wn t

o r

eddis

h b

row

n,

claye

y so

ils.

The s

ubso

il is

m

odera

tely

to s

trongly

str

uct

ure

d.

The A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on b

asa

lts,

limest

one a

nd m

arls.

T

he s

oil

mois

ture

regim

e is

xeric.

30b

Typ

ic

DC

N-C

NB

Well

or

mo

de

rate

ly w

ell

dra

ine

d,

de

ep

, ye

llow

ish

re

d

Xero

chre

pts

to d

ark

red,

claye

y so

ils.

The s

ubso

il is

str

uct

ure

less

to

modera

tely

str

uct

ure

d.

The s

urf

ace

conta

ins

no t

o

com

mon r

ock

s. T

he A

WC

is

betw

een 1

00 a

nd

150 m

m/m

. T

hese

soils

have

vert

ic p

ropert

ies

and a

re d

eve

loped o

n b

asa

lts.

The s

oil

mois

ture

re

gim

e is

xeric.

30c

Typ

ic

DY

NN

BW

ell

dra

ined,

deep,

dark

bro

wn,

claye

y so

ils.

The

Xero

chre

pts

subso

il is

weakl

y st

ruct

ure

d.

The A

WC

is

betw

een 1

00

and 1

50 m

m/m

. T

he s

oils

are

deve

loped o

n f

luvi

al

deposi

ts,

or

on lim

est

one o

r co

lluvi

al deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

30d

Typ

ic

DY

F-A

NB

Well

dra

ine

d,

de

ep

, re

dd

ish

bro

wn

to

ye

llow

ish

re

d,

Xero

chre

pts

claye

y so

ils.

The s

ubso

il is

str

ongly

str

uct

ure

d.

The

surf

ace

conta

ins

few

to a

bundant

stones.

The A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on b

asa

lts.

The s

oil

mois

ture

regim

e is

xeric.

31a

Vert

ic

DY

NN

CW

ell

dra

ined,

deep o

r m

odera

tely

deep,

dark

bro

wn t

o

Xero

chre

pts

yello

wis

h r

ed,

claye

y so

ils.

The s

urf

ace

show

s co

nsi

de

rab

le s

hrin

k-sw

ell

pro

pe

rtie

s. T

he

su

bso

il is

m

odera

tely

to s

trongly

str

uct

ure

d.

The A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on lim

est

one a

nd/o

r basa

lts,

or

marls.

T

he s

oil

mois

ture

regim

e is

xeric.

31

ANNEX 2


32

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

Taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

31b

Vert

ic

DY

AN

CW

ell

dra

ine

d,

de

ep

or

mo

de

rate

ly d

ee

p,

yello

wis

h r

ed

, X

ero

chre

pts

claye

y so

ils.

The s

urf

ace

show

s co

nsi

dera

ble

sh

rink-

swell

pro

pert

ies.

The s

ubso

il is

str

ongly

st

ruct

ure

d.

The s

urf

ace

conta

ins

abundant

stones

and

som

e r

ock

s. T

he A

WC

is b

etw

een 1

00 a

nd 1

50 m

m/m

.T

he s

oils

are

deve

loped o

n lim

est

one,

marls

or

collu

vial deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

31c

Vert

ic

DY

CN

CW

ell

or

mo

de

rate

ly w

ell

dra

ine

d,

de

ep

, ye

llow

ish

X

ero

chre

pts

bro

wn t

o d

ark

bro

wn,

claye

y so

ils.

The s

urf

ace

show

s co

nsi

de

rab

le s

hrin

k-sw

ell

pro

pe

rtie

s. T

he

su

bso

il is

st

ruct

ure

less

to s

trongly

deve

loped.

The s

urf

ace

co

nta

ins

com

mon s

tones.

The A

WC

is

betw

een

100 a

nd 1

50 m

m/m

. T

he s

oils

are

deve

loped o

n

limest

one a

nd/o

r m

arls,

or

on b

asa

lts.

The s

oil

mois

ture

regim

e is

xeric.

32

Typ

ic

SL

DN

BS

om

ew

ha

t e

xce

ssiv

ely

dra

ine

d,

sha

llow

, d

ark

gra

yish

V

itrandepts

bro

wn

, lo

am

y so

ils.

Th

e s

ub

soil

is w

ea

kly

de

velo

pe

d

and c

onta

ins

more

than 3

5%

gra

vel.

The s

urf

ace

co

nta

ins

dom

inant

stones.

The A

WC

is

betw

een 2

0

and 6

0 m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

one

and/o

r pyr

ocl

ast

ic d

eposi

ts,

or

on f

luvi

al deposi

ts.

33

Aq

uic

D

CN

ND

Imperf

ect

ly d

rain

ed,

deep,

dark

bro

wn t

o g

rayi

sh

Haplo

xero

llsbro

wn,

claye

y so

ils.

The s

ubso

il is

str

uct

ure

less

to

modera

tely

str

uct

ure

d.

The A

WC

is

betw

een 1

00 a

nd

150 m

m/m

. T

he s

oils

are

deve

loped o

n lim

est

one

and/o

r gyp

sum

. T

he s

oil

mois

ture

regim

e is

xeric.

34

Entic

M

CA

ND

Well

dra

ine

d,

mo

de

rate

de

ep

, d

ark

re

dd

ish

bro

wn

to

H

aplo

xero

llsye

llow

ish

bro

wn

, cl

aye

y so

ils.

Th

e s

ub

soil

is

modera

tely

str

uct

ure

d a

nd c

onta

ins

more

than 3

5%

gra

vel.

The s

urf

ace

conta

ins

com

mon r

ock

s and

abundant

stones.

The A

WC

is

betw

een 2

0 a

nd

60 m

m/m

. T

he s

oils

are

deve

loped o

n g

ypsu

m,

limest

one,

marls

or

fluvi

al deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

35a

Lith

ic

SC

-LA

ND

Well

dra

ined,

shallo

w,

dark

(re

ddis

h)

bro

wn,

claye

y to

H

aplo

xero

llslo

am

y so

ils.

The s

ubso

il is

usu

ally

weakl

y st

ruct

ure

d.

The s

urf

ace

conta

ins

few

sto

nes.

The A

WC

is

betw

een 6

0 a

nd 1

00 m

m/m

. T

he s

oils

are

deve

loped

on b

asa

lts a

nd lim

est

one.

The s

oil

mois

ture

re

gim

e is

xeric.

33

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

Taxo

no

my

cla

ss

3cla

ss

4cla

ss

5c

las

s6

gic

al

cla

ss

7

lab

el

35b

Lith

ic

SC

-LA

ND

Well

dra

ined,

shallo

w,

dark

bro

wn,

claye

y to

loam

y H

aplo

xero

llsso

ils.

The s

urf

ace

conta

ins

abundant

stones

and f

ew

to

many

rock

s. T

he A

WC

is b

etw

een 6

0 a

nd 1

00 m

m/m

.T

he

so

ils h

ave

ve

rtic

pro

pe

rtie

s a

nd

are

de

velo

pe

d o

n

limest

one a

nd m

arls.

The s

oil

mois

ture

regim

e is

xeric.

36

Pach

ic

M-D

CA

ND

Well

dra

ine

d,

(mo

de

rate

ly)

de

ep

, g

rayi

sh b

row

n t

o

Haplo

xero

llsdark

bro

wn,

claye

y so

ils.

The s

ubso

il is

str

uct

ure

less

and c

onta

ins

more

than 3

5%

gra

vel.

The s

urf

ace

co

nta

ins

abundant

stones

and m

odera

te r

ock

s and

has

a t

hic

k to

pso

il enrich

ed in o

rganic

matter.

T

he A

WC

is

betw

een 2

0 a

nd 6

0 m

m/m

. T

he s

oils

are

deve

lop

ed

on

lim

est

on

e a

nd

/or

gyp

sum

. T

he

so

il m

ois

ture

regim

e is

xeric.

37

Typ

ic

DY

CN

BW

ell

dra

ined,

deep,

dark

reddis

h b

row

n t

o d

ark

H

aplo

xero

llsgra

yish

bro

wn,

claye

y so

ils.

The s

ubso

il is

str

ongly

st

ruct

ure

d a

nd c

onta

ins

betw

een 1

5 a

nd 3

5%

sto

nes.

T

he s

urf

ace

conta

ins

com

mon s

tones.

The A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on g

ypsu

m o

r flu

vial deposi

ts.

The s

oil

mois

ture

re

gim

e is

xeric.

38

Vert

ic

DY

AN

CV

ert

ic H

ap

loxe

rolls

(H

ap

lic K

ast

an

oze

ms)

are

ve

ry

Haplo

xero

llslim

ited d

efin

ed.

The s

urf

ace

conta

ins

many

rock

s and

abun

da

nt

sto

ne

s a

nd

sh

ow

s co

nsi

de

rab

le s

hrin

k-sw

ell

pro

pe

rtie

s. T

he

so

ils a

re d

eve

lop

ed

on

lim

est

on

e

and/o

r gyp

sum

, or

on f

luvi

al or

collu

vial deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

39a

Entic

D

YN

NC

Mode

rate

ly w

ell

dra

ine

d,

de

ep

, d

ark

ye

llow

ish

bro

wn

C

hro

mo

xere

rts

to v

ery

dark

gra

yish

bro

wn,

claye

y so

ils w

ith s

trong

shrink-

swe

ll p

rop

ert

ies.

Th

e s

ub

soil

is m

od

era

tely

st

ruct

ure

d.

Th

e s

urf

ace

is

fre

e o

f st

on

es.

Th

e A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on b

asa

lts.

The s

oil

mois

ture

regim

e is

xeric.

39b

Entic

D

YN

NC

Well

dra

ined,

deep,

dark

bro

wn,

claye

y so

ils w

ith

Chro

moxe

rert

sst

rong s

hrink-

swell

pro

pert

ies.

The s

ubso

il is

st

ruct

ure

less

. T

he s

urf

ace

is

in g

enera

l fr

ee o

f st

ones,

but

conta

ins

com

mon r

ock

s. T

he A

WC

is

betw

een 1

00

and 1

50 m

m/m

. T

he s

oils

have

vert

ic p

ropert

ies.

T

he s

oils

are

de

velo

pe

d o

n lim

est

on

e,

ma

rls,

co

lluvi

al

and f

luvi

al deposi

ts.

The s

oil

mois

ture

regim

e is

xeric.

ANNEX 2


34

AN

NE

X 2

. C

on

tin

ued

SO

IL_ID

2S

oil

Dep

thT

extu

reS

ton

iness

Sali

nit

y

Hyd

rolo

-D

es

cri

pti

on

Taxo

no

my

cla

ss

3cla

ss

4cla

ss

5cla

ss

6g

ical

cla

ss

7

lab

el

40a

Typ

icD

YN

-MN

CM

od

era

tely

we

ll d

rain

ed

, d

ee

p,

da

rk y

ello

wis

h b

row

n

Ch

rom

oxe

rert

sto

very

dark

gra

yish

bro

wn,

claye

y so

ils w

ith s

trong

shrink-

swe

ll p

rop

ert

ies.

Th

e s

ub

soil

is u

sua

lly

modera

tely

to s

trongly

str

uct

ure

d.

The s

ubso

il co

nta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The s

urf

ace

usu

ally

conta

ins

no t

o m

any

stones.

The A

WC

is

betw

ee

n 1

00

an

d 1

50

mm

/m.

Th

e s

oils

are

de

velo

pe

d

on b

asa

lts a

nd lim

est

one.

The s

oil

mois

ture

re

gim

e is

xeric.

40b

Typ

ic

DY

NN

CM

ode

rate

ly w

ell

dra

ine

d,

de

ep

, re

dd

ish

bro

wn

to

da

rk

Chro

moxe

rert

sre

ddis

h b

row

n,

cla

yey

soils

with

str

on

g s

hrin

k-sw

ell

pro

pert

ies.

The s

ubso

il is

str

uct

ure

less

and c

onta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The s

urf

ace

is

free o

f st

ones.

The A

WC

is

betw

een 1

00 a

nd 1

50 m

m/m

. T

he s

oils

are

deve

loped o

n b

asa

lts.

The s

oil

mois

ture

regim

e is

xeric.

41

Typ

ic

DY

NN

CM

odera

tely

well

dra

ined,

deep,

very

dark

gra

y to

very

P

ello

xere

rts

dark

bro

wn,

claye

y so

ils w

ith s

trong s

hrink-

swell

pro

pert

ies.

The s

ubso

il is

str

ong s

truct

ure

d,

and

conta

ins

betw

een 1

5 a

nd 3

5%

gra

vel.

The A

WC

is

betw

een 1

50 a

nd 2

00 m

m/m

. T

he s

oil

mois

ture

regim

e is

xeric.

42a

Basa

lt flo

ws

VN

AN

DS

elf-

exp

lan

ato

ry42b

Rock

outc

rops

VN

AN

DS

elf-

exp

lan

ato

ry42c

Rubble

lands

VN

AN

BS

elf-

exp

lan

ato

ryS

tonin

ess

cla

sses:

N:

None (

0%

); V

: V

ery

few

(0-2

%);

F:

Few

(2

-5%

); C

: C

om

mon (

5-1

5%

); M

: M

any

(15-4

0%

); A

: A

bundant

(40-8

0%

); D

: D

om

inant

(>=

80%

)N

ote

on s

oil

mois

ture

regim

es:

In t

he S

oil

Taxo

nom

y cl

ass

ifica

tion s

yste

m t

he s

oil

mois

ture

regim

e is

an inhere

nt

desc

ripto

r of

the s

oil

types.

The f

ollo

win

g s

oil

mois

ture

regim

es

are

reco

gniz

ed in S

yria

:•

Aridic

and t

orr

ic;

•X

eric

With

out

goin

g into

the p

reci

sion o

ffere

d b

y S

oil

Taxo

nom

y, t

he f

ollo

win

g c

onditi

ons

apply

:A

ridic

/torr

ic m

ois

ture

regim

e:

the s

oil

pro

file (

about

1 m

) is

com

ple

tely

dry

in m

ost

years

for

more

than 6

cum

ula

tive m

onth

s, a

nd p

art

ially

or

com

ple

tely

mois

t fo

r le

ss t

han 3

con-

secu

tive m

onth

s. T

his

mois

ture

regim

e c

orr

esp

onds

with

mois

ture

conditi

ons

in a

rid z

ones

or

sem

i-arid z

ones

with

poor

wate

r in

filtr

atio

n,

e.g

. cr

ust

y su

rface

s or

very

shallo

wso

ils.

There

is

little

or

no leach

ing in t

hese

mois

ture

regim

es,

and,

in t

he p

rese

nce

of

a s

ourc

e,

solu

ble

salts

can a

ccum

ula

te.

Xeric

mois

ture

regim

e:

the s

oil

pro

file is

com

ple

tely

dry

for

at

least

45 d

ays

in t

he 4

month

s fo

llow

ing s

um

mer

sols

tice,

and p

art

ially

or

com

ple

tely

mois

t fo

r at

least

45 d

ays

fol-

low

ing w

inte

r so

lstic

e.

This

mois

ture

regim

e c

orr

esp

onds

with

mois

ture

conditi

ons

in M

edite

rranean c

limate

s, w

ith m

ois

t and c

oo

l w

inte

rs a

nd w

arm

and d

ry s

um

mers

. T

he m

ois

-tu

re,

com

ing d

uring w

inte

r w

hen p

ote

ntia

l eva

potr

ansp

iratio

n is

at

a m

inim

um

, is

part

icula

rly

effect

ive f

or

leach

ing.

Introduction

The quantification of constraints is a particularproblem for datasets in which the mapping unitsare not homogeneous. Even when the composi-tion of these datasets is known, no information isavailable about the spatial distribution of the com-ponents at sub-pixel level. The best one can dois to assume that such constraint can occur any-where in a pixel and that each sub-pixel has anequal probability to be covered by a constraint.This is shown in Figure 10 where a pixel is repre-sented as a square composed of 100 blocks.

When several constraint layers are combined inGIS, each of them with several components, ofwhich the position, spatial pattern and contiguityinside each pixel is unknown, it becomes muchmore difficult to apply a reduction factor that willaccount for these various constraints. The two hypotheses seem particularly plausible,that the total proportion of a pixel affected byone constraint or another is either the highest ofthe proportions of the individual constraints(H1), or the sum, with the condition that it doesnot exceed 1 (H2).

H1 = Max (pi) i=1, 2,…n

H2 = Min

Conventional wisdom would incline towards thebelief that probably H1 would be more likely tobe true when the various constraints have highprobabilities of occurrence. In such cases thechances of overlapping spatial patterns wouldincrease, especially as the number of con-straints increases. In the same view H2 mightbe more likely to be true when the various con-straints have low probabilities of occurrence. Insuch cases the chances of overlap would below and the total area within a pixel could betaken as the sum of the areas (proportions)within a pixel of the individual constraints.

Procedure

These two hypotheses were tested by a Monte-Carlo simulation, in which a pixel was subdivid-ed in 100 blocks and the overlap was simulatedof a variable number of blocks, of which thenumber and position were generated by ran-domization. The following conditions applied:• in each simulation run the number of blocks

to be generated could vary at randombetween 1 and 100

• the position of the blocks could vary at ran-dom subject to spatial contiguity

• the number of constraints varied betweentwo and six

The procedure is illustrated in Figure 11 for asingle run that generates at random 6 blocks. The growth of these building blocks is at ran-dom, on condition that the pixel boundaries arenot exceeded and that growth occurs in a cellthat is a neighbor to the growing shape.

A total of 250,000 simulation runs was execut-ed. The number of constraints simulated variedbetween 2 and 6, totalling 50,000 runs for eachnumber of constraints. For each number of con-straints, the number of blocks was organized in10 classes. In each class the number of blockscould be randomized within the class bound-aries only, e.g. for the class 1-10 the number ofblocks could have values between 1 and 10only, for the class 41-50 the number of blocksvaried between 41 and 50.

Figure 10. Initial probabilities of constraints at sub-pixel level

ANNEX 3. MONTE-CARLO SIMULATION OF SUB-PIXEL LEVEL CONSTRAINTSOVERLAP

35


36

Figure 11. Simulating the generation of random polygons within pixels (6 blocks); in green the growing shape, inyellow the growing neighborhood

Figure 12. Example using 3 constraints for comparing simulated coverages with those predicted by H1 and H2

For each simulation run the total coverage wascalculated from the individual coverages foreach constraint, and compared with the cover-ages predicted by either H1 or H2 (Fig. 12).

The average coverage in each class was thencompared with the coverage predicted by H1and H2 and the differences between them cal-culated and plotted for different numbers of con-straints (Figs. 13-15).

Results

Figs. 13-15 show that the H2 method of sum-mation leads to smaller differences with the cov-erages generated by simulation, than the H1method. If the results for H2 under differentnumbers of constraints are compared (Fig. 16), itappears that high prediction errors can beexpected particularly where the total coveragefor the different constraints is in the range 50-80%. Below and above this range the errors inprediction are low.

37

Figure 13. Absolute average difference in coverage (2 constraints)


ANNEX 3


38

Figure 16. Difference between simulated and H1-predicted coverage for different numbers of constraints


39

This annex provides a summary of areas suitableor highly suitable for different water harvestingtechniques aggregated on district basis. Figure

17 shows the location of the different districts.The results, aggregated by district, are summa-rized in Tables 7 and 8.

ANNEX 4. SUMMARY OF DISTRICT AREAS SUITABLE FOR DIFFERENT WATERHARVESTING TECHNIQUES

Fig

ure

17.

Dis

tric

ts o

f S

yria


40

Table 7. Percentages of Syrian districts with high suitability scores for water harvesting techniques (group 1)

WH system Contour Contour Contour Semi-circular Semi-circular Semi-circular ridges- ridges- ridges- bunds- bunds - bunds -


Montika % % % % % % % % % % % % % % % % % %S VS VS+S S VS VS+S S VS VS+S S VS VS+S S VS VS+S S VS VS+S

Abu_Kamal 2 3 5 2 1 3 21 3 24 2 3 5 4 1 4 3 0 3Afrin 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Ain_Al_Arab 4 3 7 1 0 2 0 0 0 3 2 5 1 0 1 0 0 0Al_Bab 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0Al_Fiq 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Ghab 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Ghutta 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0Al_Hafa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Jisr_Ash_Shu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Maghrim 3 8 11 5 2 7 2 8 10 3 7 11 6 2 8 2 2 4Al_Malika 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Ma'ra 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Mayadin 2 2 3 1 1 2 14 2 16 2 2 3 2 1 2 1 0 2Al_Qardaha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Quasir 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Qunaytirah 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Al_Qutayfah 13 19 32 4 15 19 3 29 32 8 14 22 5 13 17 5 11 16Al_Sqalbieh 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0An_Nabk 9 1 10 2 0 3 3 2 5 7 1 8 3 1 3 2 0 2Ar_Rastan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Ariha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0As_Safira 5 5 11 1 3 3 5 7 12 4 3 7 1 2 3 1 2 3As_Salamiyeh 3 7 9 4 3 6 2 12 14 3 6 9 4 3 7 2 2 4As_Sanamayn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0As_Suwayda 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0At_Tall 2 5 8 1 3 5 3 3 6 2 4 6 1 3 4 2 2 4Az_Zabdani 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A'zaz 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Banyas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Center 7 4 10 0 1 1 1 1 2 3 2 5 1 0 1 0 0 1City_Damascus 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0Dara 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0Darayya 4 1 5 0 1 1 1 5 6 2 1 3 0 1 1 0 1 1Darayya/Center 11 14 25 0 0 0 0 2 2 8 12 20 0 0 0 0 0 0Dayr_Az_Zor 4 7 11 5 1 6 9 5 14 4 7 10 6 1 7 1 1 2Dreikisch 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Duma 4 1 5 1 0 2 8 0 9 4 1 5 2 0 2 2 0 2Hama 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0Harim 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

41

Table 8. Percentages of Syrian districts with high suitability scores for water harvesting techniques (group 1)

WH system Contour Contour Contour Semi-circular Semi-circular Semi-circular ridges- ridges- ridges- bunds- bunds - bunds -


Montika % % % % % % % % % % % % % % % % % %S VS VS+S S VS VS+S S VS VS+S S VS VS+S S VS VS+S S VS VS+S

Hassakeh 3 1 4 1 0 1 4 3 7 3 1 4 1 1 1 1 0 1Homs 5 8 13 3 5 7 7 14 21 5 7 12 4 5 9 4 3 7Idleb 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Izra' 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0Jablah 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Jarablus 2 1 3 2 1 3 2 2 4 2 1 3 2 1 3 2 1 3Jebel_Saman 2 1 3 0 0 1 0 2 2 2 1 3 0 0 1 0 0 1Lattakia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Masyaf 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Menbij 0 1 2 0 1 1 3 3 7 0 1 1 0 1 1 0 1 1Mesiaf 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Muhradah 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Qatana 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0Quamishli 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Raqqa 3 5 8 3 2 5 10 11 21 3 5 8 4 2 6 2 2 3Ras_Al_Ain 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0Safita 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Salkhad 4 1 5 0 0 0 0 0 0 4 1 5 0 0 0 0 0 0Shahba 3 3 6 0 1 2 0 3 3 3 3 5 1 1 2 1 1 2Sheikh Badr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Tabqat 2 5 7 0 0 0 0 1 1 2 4 6 0 0 1 0 0 1Tadmor 7 10 17 6 3 9 14 8 22 6 9 15 7 3 10 4 2 5Tall_Kalakh 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Tartous 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Tell_Abiad 0 1 1 0 0 0 1 2 2 0 1 1 0 0 0 0 0 0Yabrud 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ANNEX 4


42

Tab

le 9

. P

erc

en

tag

es o

f S

yri

an

pro

vin

ces w

ith

hig

h s

uit

ab

ilit

y s

co

res f

or

dif

fere

nt

wate

r h

arv

esti

ng

tech

niq

ues

WH

syste

mS

mall

Sm

all

Sm

all r

un

off

S

mall r

un

off

R

un

off

Ru

no

ffC

on

tou

r

pit

s-

p

its-

basin

s-

b

asin

s-

str

ips

-s

trip

s-

be

nc

h

R

an

ge s

hru

bs

Fie

ld c

rop

sR

an

ge s

hru

bs

Tre

e c

rop

sR

an

ge s

hru

bs

Fie

ld c

rop

ste

rra

ce

s

Montik

a

%%

%%

%%

%%

%%

%%

%%

%%

%%

%%

%S

VS

VS

+S

SV

SV

S+

SS

VS

VS

+S

SV

SV

S+

SS

VS

VS

+S

SV

SV

S+

SS

VS

VS

+S

Abu_K

am

al

30

33

03

21

62

72

51

26

41

43

03

00

Afr

in0

00

00

00

00

00

00

00

00

00

00

Ain

_A

l_A

rab

00

00

00

00

00

00

10

10

00

00

1A

l_B

ab

00

00

00

01

10

11

00

00

00

00

0A

l_F

iq0

00

00

00

00

00

00

00

00

00

00

Al_

Ghab

00

00

00

00

00

00

00

00

00

00

0A

l_G

hutt

a0

00

00

00

22

02

20

00

00

00

00

Al_

Hafa

00

00

00

00

00

00

00

00

00

00

0A

l_Ji

sr_A

sh_

Shu

00

00

00

00

00

00

00

00

00

00

0A

l_M

aghrim

22

42

24

27

92

79

62

82

24

00

0A

l_M

alik

a0

00

00

00

00

00

00

00

00

00

00

Al_

Ma'ra

00

00

00

00

00

00

00

00

00

00

0A

l_M

aya

din

10

21

02

16

31

91

63

18

21

21

02

00

0A

l_Q

ard

aha

00

00

00

00

00

00

00

00

00

00

0A

l_Q

uasi

r0

00

00

00

00

00

00

00

00

00

00

Al_

Qunayt

irah

00

00

00

00

00

00

00

00

00

00

0A

l_Q

uta

yfa

h5

11

16

51

11

64

19

23

41

92

35

13

17

511

16

43

7A

l_S

qalb

ieh

00

00

00

00

00

00

00

00

00

00

0A

n_N

abk

20

22

02

31

43

14

31

32

02

20

2A

r_R

ast

an

00

00

00

00

00

00

00

00

00

00

0A

riha

00

00

00

00

00

00

00

00

00

00

0A

s_S

afir

a1

23

12

36

61

26

61

21

23

12

31

12

As_

Sala

miy

eh

22

42

24

31

21

53

12

15

43

72

24

00

0A

s_S

anam

ayn

00

00

00

00

00

00

00

00

00

00

0A

s_S

uw

ayd

a0

00

00

00

11

01

10

00

00

00

00

At_

Tall

22

42

24

21

32

13

13

42

24

10

1A

z_Z

abdani

00

00

00

00

00

00

00

00

00

00

0A

'zaz

00

00

00

00

00

00

00

00

00

00

0B

anya

s0

00

00

00

00

00

00

00

00

00

00

Cente

r0

01

00

11

11

11

11

01

00

13

14

City

_D

am

asc

us

00

00

00

01

10

11

00

00

00

00

0D

ara

00

00

00

12

21

22

00

00

00

00

0D

ara

yya

01

10

11

16

71

67

01

10

11

10

1D

ara

yya/C

en

ter

00

00

00

02

30

23

00

00

00

10

2D

ayr

_A

z_Z

or

11

21

12

10

61

61

05

15

61

71

12

00

0D

reik

isch

00

00

00

00

00

00

00

00

00

00

0D

um

a2

02

20

29

01

09

01

02

02

20

20

00

43

Tab

le 9

. C

on

tin

ued

WH

syste

mS

mall

Sm

all

Sm

all r

un

off

S

mall r

un

off

R

un

off

Ru

no

ffC

on

tou

r

pit

s-

p

its-

basin

s-

b

asin

s-

str

ips

-str

ips

-b

en

ch

Ran

ge s

hru

bs

Fie

ld c

rop

sR

an

ge s

hru

bs

Tre

e c

rop

sR

an

ge s

hru

bs

Fie

ld c

rop

ste

rra

ce

s

Montik

a

%%

%%

%%

%%

%%

%%

%%

%%

%%

%%

%S

VS

VS

+S

SV

SV

S+

SS

VS

VS

+S

SV

SV

S+

SS

VS

VS

+S

SV

SV

S+

SS

VS

VS

+S

Ham

a0

00

00

00

11

01

10

00

00

00

00

Harim

00

00

00

00

00

00

00

00

00

00

0H

ass

ake

h1

01

10

14

47

43

71

11

10

10

00

Hom

s4

37

43

78

12

20

81

22

04

59

43

70

01

Idle

b0

00

00

00

00

00

00

00

00

00

00

Izra

'0

00

00

01

01

10

10

00

00

00

00

Jabla

h0

00

00

00

00

00

00

00

00

00

00

Jara

blu

s2

13

21

32

13

21

33

03

30

30

00

Jebel_

Sam

an

00

10

01

02

20

22

00

10

01

00

0Latt

aki

a0

00

00

00

00

00

00

00

00

00

00

Masy

af

00

00

00

00

00

00

00

00

00

00

0M

enbij

01

10

11

43

74

37

10

11

01

00

0M

esi

af

00

00

00

00

00

00

00

00

00

00

0M

uhra

dah

00

00

00

00

00

00

00

00

00

00

0Q

ata

na

00

00

00

00

00

00

00

00

00

00

0Q

uam

ishli

00

00

00

00

00

00

00

00

00

00

0R

aqqa

22

32

23

11

12

23

11

12

22

42

61

23

00

0R

as_

Al_

Ain

00

00

00

10

11

01

00

00

00

00

0S

afit

a0

00

00

00

00

00

00

00

00

00

00

Salk

had

00

00

00

00

00

00

00

00

00

00

0S

hahba

11

21

12

03

33

03

11

21

12

00

0S

heik

h B

adr

00

00

00

00

00

00

00

00

00

00

0T

abqat

00

10

01

01

10

11

00

10

01

00

0T

adm

or

42

54

25

15

72

31

67

23

73

10

42

51

01

Tall_

Kala

kh0

00

00

00

00

00

00

00

00

00

00

Tart

ous

00

00

00

00

00

00

00

00

00

00

0T

ell_

Abia

d0

00

00

01

23

21

30

00

00

00

00

Yabru

d0

00

00

00

00

00

00

00

00

00

00

ANNEX 4


ANNEX 5. MAPS OF SUITABILITY FOR VARIOUS MICRO-CATCHMENT AND MACRO-CATCHMENT SYSTEMS

Map 1

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Conto

ur

ridges,

range s

hru

bs'

44

45

Map 2

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Conto

ur

ridges,

fie

ld c

rops'

ANNEX 4


46

Map 3

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Conto

ur

ridges,

tre

e c

rops'

47

Map 4

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sem

i-ci

rcula

r bunds,

range s

hru

bs'

ANNEX 4


48

Map 5

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sem

i-ci

rcula

r bunds,

fie

ld c

rops'

49

Map 6

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sem

i-ci

rcula

r bunds,

tre

e c

rops'

ANNEX 4


50

Map 7

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sm

all

pits

, ra

nge s

hru

bs'

51

Map 8

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sm

all

pits

, fie

ld c

rops'

ANNEX 4


52

Map 9

. S

uita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sm

all

runoff b

asi

ns,

range s

hru

bs'

53

Map 1

0.

Suita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Sm

all

runoff b

asi

ns,

tre

e c

rops'

ANNEX 4


54

Map 1

1.

Suita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Runoff s

trip

s, r

ange s

hru

bs'

55

Map 1

2.

Suita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Runoff

str

ips,

fie

ld c

rop

s'

ANNEX 4


56

Map 1

3.

Suita

bili

ty f

or

the m

icro

-catc

hm

ent

syst

em

'Conto

ur

bench

terr

ace

s'

57

Map 1

4.

Macr

o-c

atc

hm

ent

syst

em

s: s

uita

bili

ty f

or

macr

o-c

atc

hm

en

t u

se

ANNEX 4


58

Map 1

5.

Macr

o-c

atc

hm

ent

syst

em

s: s

uita

bili

ty f

or

agricu

ltura

l use

59

Map 1

6.

Macr

o-c

atc

hm

ent

syst

em

s: s

uita

bili

ty f

or

macr

o-c

atc

hm

ent

or

agricu

ltura

l use

with

in a

dis

tance

fro

m e

ach

oth

er

ANNEX 4

Documents

Established in 1977, the International Center for Agricultural … · 2016. 10. 6. · ICARDA-18/500 /October 2008 About ICARDA and the CGIAR Established in 1977, the International