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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
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society organizations including the private sector. The alliance mobi-
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(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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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
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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
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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)
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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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
ON-FARM MICRO-CATCHMENT SYSTEMS
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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.
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
ON-FARM MICRO-CATCHMENT SYSTEMS
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
ON-FARM MICRO-CATCHMENT SYSTEMS
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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)
ON-FARM MICRO-CATCHMENT SYSTEMS
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.
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
ON-FARM MICRO-CATCHMENT SYSTEMS
15
Tab
le 5
. 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 p
its-
Sm
all
pit
s-
Sm
all
ru
no
ff
Sm
all
ru
no
ff
Ru
no
ffR
un
off
Co
nto
ur
R
an
ge s
hru
bs
Fie
ld c
rop
sb
asin
s-
b
asin
s-
str
ips
-str
ips
-b
en
ch
Ran
ge s
hru
bs
Tre
e c
rop
sR
an
ge s
hru
bs
Fie
ld c
rop
ste
rra
ce
s
Muhafa
za
%%
%%
%%
%%
%%
%%
%%
%%
%%
%%
%(P
rovi
nce
)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
As
Suw
eid
a0
01
00
10
11
10
10
01
00
10
00
Dara
00
00
00
01
10
11
00
00
00
00
0A
l Q
unayt
irah
00
00
00
00
00
00
00
00
00
00
0D
am
asc
us
21
32
13
72
97
29
21
42
13
10
1T
art
ous
00
00
00
00
00
00
00
00
00
00
0H
om
s3
25
32
51
38
21
13
82
16
39
32
50
01
Ham
a1
13
11
32
81
02
81
03
24
11
30
00
Al G
hab
00
00
00
00
00
00
00
00
00
00
0Latt
aki
a0
00
00
00
00
00
00
00
00
00
00
Idle
b0
00
00
00
00
00
00
00
00
00
00
Dayr
Az
Zor
10
21
02
13
51
81
44
18
41
51
02
00
0R
aqqa
11
21
12
78
15
78
15
31
41
12
00
0A
leppo
01
10
11
22
42
24
10
10
01
00
0H
ass
ake
h0
01
00
12
24
22
41
01
00
10
00
%S
: perc
enta
ge o
f th
e P
rovi
nce
with
suita
bili
ty s
core
0.6
-0.8
( o
r 60-8
0%
on p
erc
enta
ge b
asi
s)%
VS
: perc
enta
ge o
f th
e P
rovi
nce
with
suita
bili
ty s
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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)
Figure 14. Absolute average difference in coverage (4 constraints)
ANNEX 3
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
38
Figure 16. Difference between simulated and H1-predicted coverage for different numbers of constraints
Figure 15. Absolute average difference in coverage (6 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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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 -
Range shrubs Field crops Tree crops Range shrubs Field crops Tree crops
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 -
Range shrubs Field crops Tree crops Range shrubs Field crops Tree crops
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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
USE OF GIS TO LOCATE BIOPHYSICAL POTENTIAL FOR WATER HARVESTING
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