Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sciences 4 Discussions Earth System ective interaction Hydrology and ff dynamics and hence land- ff , and A. Basile 3 , M. Martina 4928 4927 1,4 ecting rainfall-runo ff processes and to be a powerful tool for environmental ff , G. Langella 2 , A. Coppola 1 This work produced an analytical evaluation of the potential and limitations of soil This discussion paper is/has beenSystem under Sciences review for (HESS). the Please journal refer to Hydrology the and corresponding Earth final paper in HESS if available. Department of Soil, Plant, Environment and Animal Production Sciences, Department for Agricultural and Forestry Systems Management, Hydraulic Division, Department of Earth and Geo-EnvironmentalInstitute Sciences, for University Mediterranean of Agricultural Bologna, and Bologna, Forestry Italy Systems, National Research Council, that special care is requiredachieved. in Further, handling all the soil case database studiesthe data agree soil on if the hydrological their appropriate model full degree to potential ofbetween be complexity is pedology of applied. to and be We also hydrologyawareness emphasise of to that the address e hydrologic community landscapemap about hydrology the or requires type soil of (i) soil database,quantitative better information (ii) framework behind for the a evaluating soil development surveyed hydrologicaltive by features, information the and on (iii) pedological soil quantita- communityprediction spatial of errors. variability a and, better if possible, on the spatial distribution of data obtained through soillandscape surveys hydrology and viewpoint soil and is mapping. alsostudies: developed This through irrigation evaluation the is management following Italian madenerability, at case flood from the peak a district forecasting, scale, and assessment land evaluation of for groundwater maize vul- production. We show for ungauged basins. TheMapping rapid (DSM) development is of promising hydropedologytion along to with both capacity Digital enhance of Soil our rainfall-runo policy understanding research. and (spatial) Despite predic- cial these point developments and as broad tobe how conceptualizations, usefully the the employed by soil cru- the data hydrologistdetailed has from knowledge yet typically of to the available be addressed. quality soila and This mapping soil quantity question map. databases implies of can information embedded in and behind The role of soilrecognised properties as crucial and issues their greatlyscape spatial a hydrology. distribution This indata the becomes are landscape even lacking. more are important already This applies when to hydrological the monitoring critical issue of making hydrological predictions Abstract 1 University of Napoli2 Federico II, Portici, Napoli, Italy University of Basilicata,3 Potenza, Italy 4 Ercolano, Napoli, Italy Received: 31 March 2011 – Accepted:Correspondence 22 to: April 2011 F. Terribile – ([email protected]) Published: 17 MayPublished 2011 by Copernicus Publications on behalf of the European Geosciences Union. Hydrol. Earth Syst. Sci.www.hydrol-earth-syst-sci-discuss.net/8/4927/2011/ Discuss., 8, 4927–4977, 2011 doi:10.5194/hessd-8-4927-2011 © Author(s) 2011. CC Attribution 3.0 License. Potential and limitations of usingmapping soil information to understand landscape hydrology F. Terribile 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent scales ff processes (Lin et al., 2008) ff processes. Amongst others, ff 4930 4929 processes at microscopic (macropores and ag- erent branches of a broader scientific community ff ff , storage, filtering, physical and chemical support to ff ects of a bimodal pore-size distribution and its variability on a hillslope water bal- ff From a landscape hydrology perspective, along with such recent advances in hy- The lack of scientific literature on this crucial question is rather unfortunate and sur- In an attempt to conceptualize the relationships between hydrology and pedology, Some evident examples of this potential are given in the literature on the relation- In recent years many advances have been attained by soil scientists in ameliorating and Ryan, 1999; McBratney et al., 2003). provide a more suitable better frameworking for hydrological hydropedology and problems its at contribution the to landscape solv- scale. dropedology, in the lastscientists few in years mapping much soils has (Table 1).emerged been More as achieved specifically, a by Digital credible Soil the alternativetools Mapping community and to (DSM) of techniques has traditional coming soil soil from mapping.(e.g. di spatial It entails statistics, the GIS,order use remote to of and put new into proximal a sensing, quantitative computer framework the programming) spatio-temporal in study of soils (McKenkie prising and it isand possibly from related the to few thevery references evidence much quoted (by driven above) surveying by thatsoil the soil maps). at scientific hydrologists In present literature this rather hydropedology regard, than has our pedologists contribution, been which (who emphasises typically pedological issues, produce may tion (forms) and soilthe “soil processes architecture” (functions) encompassing in(aggregates, the horizon, hydropedology. soil profile, They structural catena,(HFU) also etc.) complexity as at emphasise the and di mapping defineizations tool the one combining Hydrologic basic soil Functional point, and Unit data yet processes. from to standard Despite be soil these addressed mappingusefully conceptual- by databases employed hydropedologists, (often by concerns the hydrologists. how onlyedge soil soil on To the answer data quality available) this and can question quantity be we of information need embedded detailed in knowl- and behind a soil map. the e ance. Along with these contributions hereapplications we with report respect four to case studies soil of mapping hydropedology issues (Fig. 1). Lin et al. (2008) have created a hierarchical framework for bridging soil type distribu- to soil morphology and soil hydrophobicity, respectively; Coppola et al. (2009) studied catchments); Bouma (1981) and Ritsema et al. (2005) related preferential flow paths close interaction between soilanalysis in science saturated and and hydrology, unsaturatedenhance embracing soil the conditions. understanding multiscale and This process prediction disciplineand of promises to rainfall-runo to be both a powerful tool for environmental policy researchships (Bouma, between 2006). soil (soilLin et architecture) al. and (2008) rainfall/runo analyzedmodelling the of contributions surface/subsurface of runo hydropedology togregates), the mesoscopic understanding (horizons and and pedons) and macroscopic scales (hillslopes and in ungauged basins. soil information in bothand the Rawls, 2004) estimate and of thedology hydrological spatial (Bouma, parameters inference 2006; of (e.g. Lin soil et PTF; information. al., Pachesky In 2006) has this emerged regard, as hydrope- a new discipline devoted to the the most fundamental aspectsreproducibility of the is modelling a strategyproaches are fundamental hidden. represent requirement a But of independent criticalof scientific obstacle a validity scientifically to and rigorous it.such methodology subjective framework, in to For ap- hydrologic classify this catchments, and soilter reason plays then between the we model infiltration crucial argue and role thevegetation, the of runo processes. partitioning etc necessity wa- (Dunne, In 1978).logical The monitoring importance data of are soils lacking, further such as increases when when hydrological hydro- predictions are required There has been an increase ofnell attention et to quantitative al., taxonomy 2007; instrategies hydrology Wagener (McDon- and et components al., of 2007).plicit simulation explanation Most at based authors all. on and This intuitive scientists leads basis, to select a or modelling fundamental without non-reproducibility any of results ex- since 1 Introduction 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the organ- o erent aims, scales and procedures ff 4932 4931 is any soil property, cl the climate, s ´ e et al., 2007) the DSM sensu stricto (DSMss) which is the parent material (the state of the soil at time zero), t the p ) , o, r, p, t, ... the topography, (cl r f Albeit mainly useful as a conceptual framework (correct values of many parameters Despite the progress made on the subject, the scientific literature is rather devoid We can distinguish (Carr DSM overcomes some serious limitations of conventional soil mapping such as = climate, land use, etc.) that have forged soil development, (iii) it is not possible to derive s (referred to here as CLORPT),isms, where absolute age of the soil, and where ... representscannot additional be non-specified obtained), factors. this equationogists: emphasises (i) some soil important formation points, hasproperties, also a for strong functions hydrol- mechanistic and basis, their (ii)disciplinary spatial if approach we distributions aim including then to those we understand environmental must soil factors adopt (geology, a geomorphology, robust multi- Dokuchaev and Jenny, respectivelytempted in to formalize 1882 soil and formation by 1941, the first following equation: recognized and then at- hydrology viewpoint, and willthis draw paper, on first some we relevant describe someand the potential, case process and studies of those from making ofreinforce Italy. associated soil and soil maps, In to databases. showing rethink We their the then limitations emphasise interaction the between need pedology to and landscape hydrology. 2 Soil mapping: a hydrological viewpoint2.1 on making soil maps Introduction to the making of soil maps the utmost importance tociated examine soil whether data), and produced(traditional to in or what accordance by extent with DSM), soil di scale.This can maps work play (and aims a asso- limitations to role of in address soil hydrological the surveys applications above and at soil question, the mapping. focusing landscape Our on appraisal the is potential made from and a landscape the generic statement concerning the importance of hydropedology, we believe it is of have to rely strongly on such maps for decision making. In this context, going beyond of the predictionin error applying (soil hydrological type modellingadvances and at by soil the DSM, attributes); landscape unfortunatelymost scale. this it countries, regions, can is Despite municipalities rather be all andthe evident only usefully so the real (e.g. forth, important employed soil classical Jones data soil available et – maps and al., still usable 2005) constitute – forof that landscape critical in and watershed and hydrology. ditional analytical and/or evaluation obtained concerning by theThis DSM) use is for rather of landscape unfortunate, soil and given map watershed that information hydrological policy (tra- studies. makers and communities worldwide still using attribute domain inference systemskey (e.g. aspect water retention of capacity). DSM, Accuracy partlyglobal is given accuracy a the of nested a structure DSMdata of product (localization, some depends measurements, inference upon etc.), systems. theof covariates accuracy The and the of inference most the systems whole interesting used. set outputs of One of soil such DSM methods may be the spatial distribution conventional soil mapping, emphasisesgiven the location soil continuum, depend where alsoneighbouring soil on locations, properties their and at geographic then a positiondiscrete overcome and polygons limitations as on and a the coarseness meansgeographic soil of of and properties describing using the soil large at attribute variability domains. in the landscape in bothinvolved the in the creationlato of soil (DSMsl) information which in generates the space derived domain, soil from attributes the from DSM sensu the outputs of DSMss by the description of soilsoilscape variability. complexity by For means instance,gist using of the an a conventional implicit approach robust predictivecommunicated model describes mental in which model is a strongly developedand clear qualitative, by uncertainty, complex manner. and the and the rarely pedolo- soil The spatial approach variability is has not described. a Moreover high DSM, degree unlike of subjectivity 5 5 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ¨ elian and Cornu, cult or even impossible to de- ffi ) where the profile is located, are de- 2 4934 4933 er greatly and the spatial distribution of this diversity ff the soil profile andscribed. the site (about 10 m Acquisition of all availablefactors information (e.g. geological on map, the geomorphology,those spatial DEM, obtained after climate distribution remote data, of and etc.) soil-forming proximal sensing. including A synopsis of allfication. such This information synopsis, for assisted producingor by a digital), the consists preliminary in use landscape segmenting of(supposedly), classi- a photointerpretation in region (either into this analog many preliminary landscapeforming units factors step, considered (at internally least homogeneous thosethe available). strongly in deterministic In terms basis other of ofapproximation, words, soil- the the the region soil-forming procedure of factors employs to interestanalysis. segment, into areas with for a carrying first out soil samplingIn and these segmentedsurvey areas consists a in preliminary openingdrilling) up soil where, holes following survey standardized and procedures, is trenches a (and then vertical also section carried of in soil out. performing called hand This A detailed discussion of both conventional and DSM methods may be found in some Despite these well known problems, in the last century the scientific community of On the other hand, DSM scientists (McBratney et al., 2003) have translated Jenny’s In a landscape, the above equation is a powerful conceptual framework rather than 1. 3. 2. 2.2 The standard process of soilThe survey main and steps soil typically mapping employedinclude: in producing soil maps are illustrated in Fig. 1 and haustive auxiliary information withattribute models domains. of inference in the spatial, temporal and/or reference books (Dent and Young, 1981;description McKenzie et of al., this 2008). complex We procedure report for here the a purposes brief of environmental hydrologists. combining point-based data (from field survey and laboratory analysis) and mostly ex- is not necessarily required. DSM thus typically consists in creating soil information soil and environmental factors with ations view for to using determining these the asadaptation soil spatial of spatial distribution CLORPT prediction func- of notempirical soil for representation a types of mechanistic and relationshipsfactors. explanation soil In between of attributes. SCORPAN this soil soil is and It formation obtainedaddition by of other but is extending geographical for spatially an position. the five an The referenced soilobtained prediction forming of from factors soil known with properties observations the of neighbouring ais the given point. based site is Much on thus DSM the workusing use worldwide surveyed of soil already samples), existing and soil conventional databases and (as analogical laboratory-measured soil data cartography itself maps. These weremanagement. then employed as indispensable tools forqualitative CLORPT planning formulation into proper the morePAN land quantitative model. and It inference-based consists SCOR- of an empirical quantitative description of relationships between els of soil formation at2008; both Minasny point-based and et landscape al., scaleformation, 2008; (Samou formalized Salvador-Blanes in et the al.,termine equation, 2007), and some are then of still model,soil-forming the very CLORPT such factors di factors of as during soil the the (long) age life of of pedogenesis a developingpedologists and/or soil. the used status the ofto CLORPT other analyze conceptualization and and report standardized survey the methods spatial distribution of soils through the production of soil ment hydrology), (iv) soils candepends di on the spatial distribution of the factors that induceda soil practical formation. tool forimportant producing conceptualization, a reviews spatial and local analysis studies of on soils. the use Indeed, of despite mechanistic some mod- recent soil information from solely geological data (as is often done in environmental catch- 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 1993; ff erent types of in- ff erent horizontal layers, called pedo- ff ect the sampling scheme, this stage is char- ff 4936 4935 Final test of the soilthe soil map, map. typically performed by the organization commissioning Field control to check soilwith mapping explanatory units notes and where drafting forincluding of field each the description STU final and is soil lab also analysis. map given (SMU) a representative profile Final review anddatabase. publication and release (upon request) of .shp file and soil rather complex task is typicallya performed limited by aggregating number all ofdominant soil (and soil information co-dominant) into mapping soil type units (STU). (SMU), each being represented by the On the basis of the resultssoil obtained mapping units after (SMU) the map preliminary issoil soil produced information survey, a after (also a preliminary named synopsis preliminary of soilsoil both correlation). types landscape In and (Soil this Typological map, Units oneeach or also preliminary more typically SMU. named as STU) areSystematic associated soil to survey (in accordance withcommissioning standards the required survey) by the and organization soilmap. analysis on the basis of the preliminaryFirst SMU draft of theaim is final to SMU organize and maparea produce within and a a soil synthesis coherent of framework legend. (typically all named the In as soil this final knowledge in soil drafting the correlation). process, study This the The second step consists in gathering low cost and spatially continuous auxiliary co- Assuming a profile consisting of only three horizons, the potential output will include 4. 5. 6. 2. 3. 1. into a quantitative empirical relationshiping with on specific the soil target properties/classes. soil attribute Depend- at hand, more or less SCORPAN factors are elaborated information, this may leadand to money a constraints, new which inevitably survey a acterized in by order observing to integrate inthe the the soilscape data. geographical continuum. domain Soils Due are only toformation, now a time coded such limited on as a number soil points of profileanalysis. basis field points by di descriptions, of soil types, and quantitative laboratory variates related to the factors of the SCORPAN model. The soil-forming factors are put 2.3 The DSM process of soilThe surveying general and procedure soil of mapping digitalThe soil soil mapping modeller may first be needsered schematized basic in in knowledge a both about few the qualitative key soils: steps. and soil quantitative information forms. is gath- According to the type of pre-existing What is evident here is thethe complexity of large the quantity process of of surveyingthe data and obtained mapping to soil of be soils, knowledge. processedentire With and process these of the considerations surveying, importance making itabout and of is 20 publishing a 000 hardly a ha good surprising soil may map synopsis that take (1:50 2–3 of the 000) years. for an area of features which are of great relevance to hydrology. about 257 field data (includingand in descriptions the of theoretical the case where profilebased that and data all types the for of soil each soil sampling soil featurestative) occur) station field observation. and and 36 Using laboratory laboratory- the datainternational (qualitative, obtained, semi-quantitative, soils systems quanti- are then of classifiedWorld soil into Reference categories classification Base using (WRB, such 2006). as Soil Taxonomy (USDA, 2010) or genic horizons, and alsofeatures and in properties determining, that can forFAO, be 2006). directly each derived of in Finally, the the soilsformed field are identified (Soil in Survey sampled the horizons, Sta laboratory. for specific a chemical An soil example and of survey physical the analysis is kind to given of be in soil per- Table features 2 that where are described we in have highlighted in bold and italics those The description typically consist in recognising di 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff ). The availability of https://applicazioni.regione. http://websoilsurvey.nrcs.usda. http://www.nrcs.usda.gov 4938 4937 ). sgss ) or also at regional level (e.g., From this scenario it seems evident that soil mapping, being heavily financed by Despite this complex scenario, already reported in Dobos et al. (2006) and Jones The 1:250 000 scale aims at an inventory of regional soil resources. Most European Analysis of the soil map as a product must really start fromThe its availability information of content, soil maps varies from country to country and their quality is often The output can be used in a DSMsl inference system that makes predictions in the Thirdly, a spatial soil inference system is used to predict continuous soil attributes public agriculture departments, aiming atfinanciers better of agriculture such management, are maps. therarely At main included this in such scale maps. in many countries, hill andagricultural departments mountain and areas organizations, is are stronglyagricultural focused questions. on providing This answers means, to for instance,by that soil the massive mapping databases generated projectsunless specific have processing no is quantitative performed (e.g. data estimate directly of PTF). usable for soil hydrology ning and landlandscape management hydrology. (mainly Availability of in thesethere agriculture semidetailed is and scale a soil forestry) maps markedmost but varies discrepancy of greatly: also the in mapped possibly map areasogy in coverage refer viewpoint to from (plains plains. several are This European situation, just regions to one be and component regretted of from a a hydrol- catchment), is due to the fact that rather limited (Table 1) but it embeds high potential inet future al. prospects. (2005), manysuch European as the regions case are concerningproduced the gradually at scale moving the of to soil inventory maps. (1:250 adopt 000) At standardization and present, semidetailed the (1:50 latter 000) arecountries scales. generally either already have such1:50 000 maps or semidetailed are scale in aims the to process of produce obtaining soil them. information The directly usable in plan- the last 20–30institutions years. (which typically In commissioned thethey soil only EU maps) provide are despite processed unwilling generalized thisUSA products to soil potential, (Rossiter, reveal datasets 2004). soil unfortunately are By data; many easily contrast,soil accessible in national maps ( the obtained through DSM procedures and with a hydrological content is still 2006) that over 500 000 detailed soil profiles have been described in EU countries in related to the presence of soil survey agencies. It has been estimated (Dobos et al., ples of soilin databases the and USA). vectorsurvey Soil data maps programs themes can at maygov/app/HomePage.htm be national be easily scale found accessed on (e.g.emilia-romagna.it/cartografia through the in services internet the such (e.g. USA as web soil which is obviously highlywe dependent only on report the some scale. examples of For methods the and sake standards of as this given specific in paper, Table 3. 3 Soil mapping: a hydrological viewThe of result the final of result soil soil database surveying containing and allanalysis, soil along the mapping with information is GIS obtainedpolygons. vector the data from production Hitherto containing field the of the work geometry same a and of information georeferenced the laboratory was soil produced mapping in unit analogical format. Exam- from soil observations andforms according auxiliary to data. the typecomputing of The power, input and empirical data, so the function on. scale can of target, have the di modeller’ssoil know-how, attribute domain (pedotransfer rules,attributes mechanistic (e.g. models) water to retention infer capacity). soil hydrological soil covariates. Digital terrain analysis,accounts using for a the Digital Elevation calculation Modelson of (DEM) and the as Gallant, so-called input, 2000), terrain suchconvexity, (or etc.) as land or those surface) to referring parameters the to (Wil- potential the solar geomorphology radiation. (e.g. concavity, on a mathematical and/or statistical basis to yield a wide range of spatially exhaustive 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent and ff , water retention at specific ect aggregation and hence ff ff ers a rather large number and type ff erent type such as clay, silt, organic ff 0.2 microns), this is because only fine clay < 4940 4939 ect by the particle size. For instance, the usually called clay ff erent spatial scales and governing soil porosity and then in turn ff erent hydrological behaviour. Then, from our viewpoint, it is better erent climatic condition (e.g., paleoclimate). ff ff ecting the occurrence of these soil features and then we would expect ff Another soil feature worth mentioning because having a closer interaction with soil In Table 2 we have already reported soil features contained in the soil mapping In this paper we do not aim to go further into this subject. However, soil map content coatings are typically fineis clay able coatings to ( pass throughsmall the pores filtering (not action simply of soils.significance in Then pores of this where coatings fine bypass must clay flowcoatings can be occurs) can move related and in move to then very both this thenot vertically hydrological complexity. unique. and horizontally; The Moreover then next (iii)and their issue the its hydrological (ii) field detectability meaning is quantification (even is ofcontrasts that with between these magnifying the features lenses) coatings themselves is it andbe not depends the formed always soil by under matrix; easy the di finally, degree (iv) coatings of can colour that their occurrence and quantificationhydrology could processes. help us Unfortunately inconsider understanding the the underlying issue following: soil is (i)matter far coatings etc.; then more can the be complex, physicsit of governing in is each di fact very of indeed we these much type must of a materials is di relationship with them. Thiscesses) is occurring because at di pH issoil only hydraulic one properties of such the asfunction. many the soil soil water features retention (pro- and the hydraulic conductivity hydrology is the occurrenceflocculated, of transported coatings and in deposited soils.cial (flocculated) in factor Those lower a are horizons. soil Water material is typically a de- cru- databases. In theory, anyin given hydrology; soil for characteristic instanceinfluencing may soil ion have pH, exchange a governing on direct/indirectsoil the clay interest porosity composition minerals, and of can hydrological the behaviour. greatlythe soil However, soil a soil solution system pH, and and even thus if potentially strongly the influencing hydraulic properties, has no direct quantitative to focus on actual databaselegend/report. rather than on soil classification as given in standard soil World Reference Base: WRB,work 2006) of that are agriculture/forestry still (e.g.logical very FAO, features much USDA) (e.g. developing rather field in estimatepressure than the heads, of in frame- etc.), infiltration hydrology. if and present,of runo Then in the hydro- the best many offinal parameters the soil enabling cases classification have the been mayhaviour. soil employed be as Then classification. far one the from worse givemay Then, outcoming have the a result in indication rather is di some of that the cases, soils soil the classified hydrological with be- the same name soil map legends and reportswhich provide maybe a important synopsis for of hydrologyaggregated soil are along information not where with anymore parameters available othertion being also information embedded applies in and to the soilupdated process classification. international of Pedologists soil typically soil classification classify mapping. soils, schemes indeed (e.g. This using Soil situa- Taxonomy: USDA, 2010; Since soil mapping (and relatedof soil soil/land databases) information o andspatial produces distribution a of qualitative soils, andscape it issues, quantitative is can description largely evident benefit of that fromenvironmental the environmental this hydrology hydrology, type and applied of soil information. to mappingit The land- may interaction is thus between seem interaction rather only obviousperform in but a theory. in detailed reality In examination order ofhydrological perspective) to the rather understand information than this the contained usual issue in examination soil it of databases soil is map (from very legends. a important In to fact (e.g. data from specific soilsearching mapping through units) the can many be dedicated easily websites browsed (such for as specific those areas given by above). 4 Soil mapping and environmental hydrology: interaction yet to be formed 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erence is important if ff erent pH and redox potentials. ff 4942 4941 erent pH values, then the same mottles may ff erent solubility at di ff cult to successfully address many new soil-(agro- ffi ectively used in environmental hydrology (e.g., param- ff erent hydrological conditions. In addition, mottles can occur as the ff Indeed, despite several positive judgments the Land Evaluation (LE) procedure has The publication of soil maps and soil reports (both in analogical and/or digital format) In this scenario it is also very important to emphasize that standard soil mapping Another case worth mentioning concerns mottles. Their occurrence (frequency, size, Given the complexity of the issue and the need to go into the hydrological usability of For example, the analysis of water balance, using bucket-based models, might in- In order to shed some hydrological light on this matter, weAnother crucial reclassified issue all we the considered soil is the quality of the estimate/measurement of lated physical and chemical processeshand, taking place many in theoretically the problems soil landscape. in On the the hydrological other application of physically-based use of soil water balance models.some However, classic tackling but landscape crucial hydrology questions issuesbe poses concerning employed. the It type is of soil knowncal water that (e.g. balance there land models is evaluation) to no versus mechanistic generally acceptable simulation choice models. between empiri- also been widelypirical criticized basis by which)environmental the makes challenges scientific it which community require di the for dynamic its characterization qualitative of the and interre- em- 5 Some examples of interaction Landscape hydrology typically requires an understandingponent of of soil a hydrology as larger one environmental com- system. This can be generally performed through the ago) only rarely allow accessstill to have the it). original soil information in their possession (ifdoes they not include all thethe produced main soil soil information types butthis (reference rather summary profiles) an and extensive is summary theinternal a of related consistency, fairly landscape it standardized features. doessoils process Although not between where and provide especially much a within tool care the soil to is mapping investigate taken units. the to spatial ensure variability of does not provide trueability quantitative and information related spatial aswhere uncertainty. to this In ensure information the has detailed scientific beenformation soil literature produced cannot spatial there ex be post are retrieved vari- mainly but a(e.g. (i) in few due administrative most cases to localities regions) loss this who of spatial data commissioned in- and the (ii) soil because customers survey (in many cases long many Italian palaeosols in the Po Valley. lated on the basismeasurement. of particle size classes by means oflocation) a is PTF a and very not importanttainly through index true, for but direct assessing some caution waterthese must saturation be two patterns. taken elements This if have these is rather mottlesFor cer- di refer instance, to iron if or two manganese: indicate soils rather show di veryresult di of ancient water saturation processes, such as those occurring for instance in environmental hydrological applications. duce one to assumecations. that a Unfortunately soil this(Available db Water is Capacity, provides not the high always reference quality the water data case storage for because, in hydrological for appli- the example, rhizosphere) the is AWC calcu- semiquantitative or quantitative, as illustratedthis in information Table 2. is then This to di eterization be of e hydrological models). data presented in soil databases, weitations summarized in in using Table the 4 17 the soil mainto features potential hydraulic typically and properties found lim- in and soil hydrological databasesboth processes. and the Analysis directly enormous related of potential the but table also clearly caution shows in using some of these soil parameters in features occurring in aand negligible typical hydraulic soil relevance. database in accordance withsoil their characters. direct, In soil indirect tion. databases almost In all reality, parameters the have a methods quantitative by formaliza- which this information is obtained may be qualitative, 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | )) and satu- ¨ oschl, 2000). h ( θ erent methods is based ff erent modelling approaches, and ff erence. ff ¨ osten et al., 1998). For modelling purposes, 4944 4943 on 50 of the 100 supplementary sites. In the remaining s ) and the relative variance (1-RV), while the ANOVA test k r ). The comparison between the di cient ( ffi framework to simulation modelling The outcome of this analysis (Fig. 3) demonstrates that there are four groups of In general, Land Evaluation (LE) has been the most commonly used procedure The latter concept was fully explored in the first of four case studies centered on simulation modelling was used on real benchmark soils (Method 4), and to a greater provided an independent estimate ofthe predictive forage ability maize was biomass calculated using forson several comparison statistical correlation purposes; indexes, including coe the Pear- was carried out to evaluate the maize biomass di models behaving similarly. Classical2 and hybrid and land 3 evaluation in methodsresults confirm (methods Fig. that 1, 3) the FAO-like provide approachesthe perform poor detailed best results at scale regional over of scale all our rather than the case LE study. performance The indexes. first leap These forward in results occurred when data on groundwater level (lower boundary conditions)site were (Bonfante obtained et from a al., monitoring 2010)from the and meteorological from monitoring the network soil ofhttp://www.arpalombardia.it Regione report. Lombardia (ARPA Daily Lombardia climatic – on data both were obtained the predictive ability and cost. Locally tested remote-sensing measurements data input a pre-existing 1:50 000cated soil after map a with relative stochastic database, spatial ausing simulation further annealing the 100 procedure sites (Aarts particle lo- andOC, size Korst, 1989) etc.), distribution and and measurements therated of hydraulic main conductivity bulk ( chemical density, parameterssupplementary water retention (e.g. sites curve the pH, ( water EC, Function retention (PTF) curves by werewere Vereecken derived et computed using using the al. HYPRES PedoTransfer (1989) PTF and (W the saturated hydraulic conductivity worldwide to address local/regional/nationalmaize land biomass use production was planning. conductedin in Here the an a Lodi area plain of LE (Po about valley) forsimpler 2000 using forage ha standard nine in alternative LE northern methods approach (Fig. Italy tomodelling 2). a (SWAP; They van more ranged from Dam extensive a use et of al., physically 1997 based and simulation CropSyst; Stockle et al., 2003), using as In real life situations, wherecal funding conceptual and frameworks data of quality hydropedology are maygists the be who basic of need little constraints, to help the know to logi- thethen landscape cost/benefit whether hydrolo- ratio complex of the models di (e.g.able mechanistic and simulation appropriate models) for are aan specific really landscape evaluation sustain- hydrological of task. this In issuesoil the hydrological is characterization current attempted using example models by at combininget increasing level pedological al., of 2009). information complexity (Manna and the ungauged basin. Thedeveloped by latter applying and novel data. (partially) the aquifer5.1 vulnerability case study Case were study 1 – comparative land evaluation approaches: from the FAO the need to obtain “the right results for the rightthe reasons” interaction (Grayson and between Bl pedologyto and a hydrology. land Although evaluation the exerciseexcellent methodological for case example maize study to production is show in theogy devoted overall a even potential typical in of agroecosystem, hydrology. applying hydropedol- it Thein is other hydrological an three problems. case Specifically,ment studies they at mainly deal the focus with district on irrigation scale, soil aquifer planning functionality vulnerability and assessment, manage- and flood forecasting in an are still unsolved, despite theybetween where these posed two several years extreme agomodel approaches, (Beven, complexity 1989). it provides Moving is arequires generally more an accepted accurate increase that in description the anIndeed, of number increase there the and exists in quality phenomenon the of but need parameters also to and find hence the higher costs. optimal model complexity in accordance with model (e.g., physics of heterogeneity, equations and parameters scale integration, etc.) 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ciency ffi ha). This 4 both the overall investment plan ff 4946 4945 ecting the performance of land evaluation and also ff generation are important. Moreover, even if this case study ff ¨ oschl, 2000) may be concretely translated. processes, etc.) where soil functions such as storing, filtering, transformation ff The current example highlights the importance of the pedological information along A case study in Sardinia (Arangino et al., 1986) can help appreciate the procedure This approach is a kind of land evaluation largely employed in the world to address Our findings, being largely based on detailed characterization of soil hydropedolog- Summing up, comparison between the FAO framework and mechanistic simulation The best prediction results were obtained by abandoning the support of the soil- dated to the specific conditions of a study site, can be used to improve the e From the optimal irrigationaccount management the standpoint, inherentsoil soil a properties spatial further mainly variability step influencinghydraulic within should the conductivity. each soil take water soil into balance, unit, such especially as water of retentionwith those and the spatial variabilityto of optimal irrigation soil management. hydraulicare properties known In to in this be lending respect aatmosphere valuable physically a tool water based major to balance. numerical simulate contribution soil models In water flow, particular yielding the these soil-vegetation- algorithms, once calibrated and vali- order to produce aall soil the suitability areas map included in for the irrigation. project and It also is to used identifylocal/regional/national new as areas land a suitable template for use irrigation. tocommunity planning. review has largely Despite criticized its the procedure widespread for use its the qualitative and scientific empirical basis. suitability classes for irrigationsess (USBR, whether 1981). a soil The has purposeof an the inherent of hydraulic capacity this system to for approach pay irrigation is o and to to provide as- appropriate addedin value question: to farmers. an irrigationmap capability information, class was both assigned qualitativequantitative to (i.e., (i.e., each clay drainage soil content, class, unit EC,parameters using risk were SAR, the combined of water soil into table soil an depth, erosion, empirical profile multiparameter etc.) depth, scheme etc). (USBR, and 1981) These in One typical issue inand landscape management hydrology of applied irrigationis to at traditionally agriculture achieved the by is district using both scale soil the (typically information to lower planning derive than in 10 a simplistic way the land 5.2 Case study 2 – mapping of irrigated areas ical behaviour, show greatsation potential is also the in main otherenvironmentally land applications related evaluation where topics engine. such (e.g. This groundwater characteri- runo vulnerability, is nitrate the pollution, case rainfall- forand instance interface of for addressing runo is not exclusively related tomethodological a example as landscape to hydrology how issue, the(Grayson statement it and “the is Bl right in results our for the opinion right a reasons” suitable plexity always means an increase inpredictive ability its evolves predictive discontinuously ability. with Indeed, respect inis to this indeed model case complexity. the Data study, leading the quality parameterplays a a major role in determining final costs. cessing (Fig. 1). Needlessdard to LE say, approaches this and approachwhich poses was is major 47 questions based times on on morefiles, its costly a is sustainability, than new while indeed stan- field method the campaign19-fold 6, one higher and with than hydrological all method analysis good 1).be on performance chosen, Thus, reference producing indexes strictly pro- consistent at speaking, results. the this lowest method cost is (but themodelling still cheapest disproved to the assumption that an increase in model mechanics and com- size the importance ofprocessing or working averaging on observations of real several soils soils. and onmapping real units, measured data dramaticallyperforming rather increasing geostatistical than the analysis number (Method of 9) samplings as a and mean analyses of and digital soil mapping pro- extent if their hydraulic properties were measured (Method 6). These results empha- 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) ), C S . The new erentiating ff , defined as 800 d 800 ) functions) and d θ 800 cm in the soil ( − k ect in di ff ) and h ( ranges from a minimum of 4 θ 800 800 cm is the soil pressure head d − river plain serviced by the Sinistra Sele Ir- 2 4948 4947 ) and sometimes also the hydraulic conductivity ( I erent soils in determining groundwater vulnerability in the Sarno river plain ff Identification of the representative soil profile within eachCharacterization soil of mapping the hydraulic unit. propertiesparticle-size (namely, distribution for each soil horizon of theCalibration representative of soil profiles. a1981; specific Basile soil and unit D’Urso,erties 1997), quasi and through physically-based the coupling particle PTF the size (Arya measured distributions. hydraulic andApplication prop- of Paris, the calibrated PTFs to several points in the whole area. Classification of soils on theof basis hydrologically homogeneous of new the units “functional (HFUs). properties” and demarcation Definition of specific “functionalapplication properties” of (output the of model in the all simulation the model) soils. and An example of the first group of models is DRASTIC (Aller et al., 1987),Here we which is report an example, applying a process-based method to emphasize the The main points of the procedure were as follows: Unfortunately, the application of these models at the landscape scale is strongly 1. 2. 3. 4. 6. 5. the role of the soil on applying DRASTIC-like models. based simply on ratings andthe weights. impact Of of the seven thefactors required vadose are inputs, calculated zone the using ( soil the media soil ( texture. role of di (southern Italy), one ofby the sandy most to polluted sandy rivers loam in soils which Europe. have This a plain practically negligible is e characterized ability to filter pollutantslikelihood out for groundwater of to water. be impactedpose Therefore by contaminants the a at need health concentrations that is concern would vulnerability increasing (the assessment to is new generally assess Groundwater performed the three Directive, by groups using 2006/118/EC). (Tesoriero one Groundwater et model al., frommethods, 1998): the (ii) following process-based (i) methods the and simple (iii) and statistical widespread methods. overlay and index to a maximum ofto 8 take account days. of the Theclassification hydrological soil allows similarities production classification highlighted of map by the evaluating shown HFU map in shown Fig. in5.3 4a Fig. 4b. was modified Case study 3 – theIn role natural of soil ecosystems in soil assessingthropogenic filters groundwater ecosystems vulnerability water this becomes that a falls very as important rain function and because goes of into the soil’s rivers. In an- the number of days requiredlayer to between reach 10 an cm average and pressure 30at cm head which depth. of stress The value starts of tional in property” the represents chosen theirrigation crop optimal supply interval (alfalfa) on between and demand. two therefore The irrigations the functional in property calculated the “func- event of was simulated and the chosen output was the “functional property” rigation Consortium (southern Italy),available an at irrigated a area scale where of a 1:10 soil 000 map (Fig. was 4a). already The drainage process following irrigation proposed a methodearlier to application classify of the soils Hydropedological Functionalas according Unit (HFU) a to concept, later soil-landscape their defined 2008). hydrological unit behaviour with in similar an pedologic and hydrologic functions (Lin et al., The method was developed on a 11 km 2010). limited by the availabilitysoil of spatially hydraulic variable behaviour. information Bya for coupling correct soil pedological description map, information, of and as the a already small available in number of hydrological analyses, D’Urso and Basile (1997) of irrigation, thus contributing to the rational use of water resources (Bonfante et al., 5 5 25 15 20 10 15 25 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | shows 2 is the ratio ciency index In / ffi erent water and Df ff erential equations ff erences are consid- ff 100 where × )] In / Df erent gradients and aspects, and the ( ff − ects of the soil and subsoil stratifications. [1 ff = e 4950 4949 I ) functions (van Genuchten, 1980) and dispersivity θ ( k ) and h ( retention curves of horizons with the same sandy loam texture. θ Ap The pattern of spatial variability is quite similar considering the maps of groundwater However, application of this type of process-based method is not suitable in un- Recognition of the variability in space domain of any individual property like water re- Considering the proposed limitations concerning the use of an overlay and index A combined standpoint, both pedological (1:50 000 soil map; Terribile et al., 2003) 1/2 Feox). The map of groundwater vulnerability based on an LE approach was pro- omorphologic units: the crests,valleys. the slopes The at di main question is to what extent a parsimonious identification of soils well expressed andic properties, and formedandic by materials. re-deposition of already pedogenizated 5.4 Case study 4 – floodThis forecasting case in the study Sangone evaluates basin gauged the basin. contribution In the of Sangoneand soil basin discharge the data time only to data series available floodconsiderable at refer pedological forecasting to the the and in closure meteorological hydrological section. an complexity. un- This It basin is of formed about by 150 km three main ge- susceptibility (Fig. 6) produced byas the benchmark). two methods The (usingPie1 the worst and process-based Pie2 soil method units), protectionsoils, and characterized capacity by by poorly refers expressed poorly toprotection andic evolved properties. the capacity mountain The Pie3 is foothill soil Vas1, mapping unit and unit an (but colluvial with intra-mountain the also highest valley where soils are very deep with scheme of land evaluation wasconductivity, applied. depth of The groundwater, chosen texture, factors saturated+ were: soil water saturated content, hydraulic andicityduced (Alox (Fig. 6a). Itphysically based was model compared (SWAP) and with then the(a calculating the map measure soil (Fig. of protection soil 6b) e between protection produced the capacity): first leaving by flux applying at a the bottom and the incoming flux at the top of a soil profile. be identified andThe selected, occurrence which were ofthese important low soils in order distinctive explaining physical clayfor and the the minerals chemical water solute (allophone-like) and characteristics solute transport. in which fluxester (i.e. are retention the high very permeability and soil associated important high with matrix CEC). high From values lends of this wa- (mechanistic) knowledge, a modified empirical hydrological, enabled other soil parameters already available in the soil database to able. On the other hand, the understanding of the process, both pedological and out. The parameters of were incorporated into a 1D modelwater (SWAP; van and Dam transport et al., of 1997),and solute simulating the applying the flow respectively convection-dispersion of the module.flow Richards and di Functional solute transport properties through representingdespite a 80 the the cm water depth similar section soilsolute were discharge calculated. texture, (Basile As reference et expected, soil al., 1999). pedons show verygauged di basins where very little information, especially on hydraulic properties, is avail- information and groundwater vulnerability isthe highly hydraulic properties complex due and the to combined the e non-linearity of method (DRASTIC) and thenmation the in potential a of more mechanistic embedding model, the a available process-based hydraulic modelling infor- approach was carried ample, Fig. 5 shows Although they show theerable, same reflecting DRASTIC both score, thethan the soil texture hydraulic structural di on complexity and thepore the arrangement system. influence of of elementary other particles factors in producingtention, the although actual significant in soil discriminatingmation soils, on does the not real itself risk provide of groundwater reliable pollution. infor- The relation between hydropedological and hydrological, was established todological select configuration 18 of soil theence types soil profile on mapping was the opened units. basis andfor of For in laboratory the each each determination hydrope- horizon soil of undisturbed mappinghydraulic both soil conductivity) unit samples the and a were hydraulic the collected refer- properties solute (i.e., transport water parameters retention (i.e. and dispersivity). For ex- 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent 3). ff = erence Vegetation processes with par- ff O, pH in KCl, pH in 2 ff erent forms: the distributed and the lumped ff 4952 4951 ). erent levels of soil knowledge were compared, namely: (i) soil characteristics ) can help in interpreting the hydrological complexity hidden in flood forecasting ff A preliminary study of pre-existing available information (soil use map, 1:250 000 along with standard thematicenvironmental covariates layers data (e.g. which resulted, geology,useful from for land a describing use, the pedological specific viewpoint, etc.), soila very distribution using fuzzy of the c-means some study clustering area.a to Between study them, classify of the vegetation DEMIndex) (de by using Bruin analysing Landsat and and the Modis Stein,Wang NDVI images et 1998), (Normalized al., (Rouse and Di 2004). et al., 1973; Tucker etA al., soil 1986; survey mainly limitedprofiles to inside describe each and identified to P-SMU sample (about supposedly 50 representative soil profiles and 50 minipits). The Soil chemical analysis onNaF, EC, about OC, 220 CEC, EB, soil and samples then (pH Fe, Al inA and H synopsis Si extracted of in19 all oxalate SMU. at acquired pH These data unitsnamely and were mountain, definition piedmont combined of and into a plain. schematic the It soil three must map be main having emphasise landscape that systems this soil map soil map, 1:50 000 soillogical map map, available 10 for m the DEM). only lower plainA area, synopsis 1:50 000 and geo- definition of preliminary soil mapping units (P-SMU) obtained, choice of representative soilsdure; is but indeed at crucial the (andoptions to present questionable) the in day use this we of proce- do a traditional not pedological think understanding (clorpt). that there are better “sustainable” The second approach consists of the following main steps: In the first approach a global data set of soil types was used: the FAO/UNESCO Soil Results obtained applying a blind (i.e. without any calibration) simulation relative to The study was performed through a comparison of the results obtained by using 3. 4. 5. 1. 2. functions forms resentative of the minimumhydrological level model. of Moreover, the knowledgeet dataset available al., was for 2003; also the Liu used et implementation in al., several of 2008) applications a where (Doll other (local) sources of data were not available. resolution and of qualitative information on the soil characteristics. However, it is rep- is estimated using the soil texture. This dataset clearly has the limitation of a coarse Map of the World.the FAO legend This was data replaced2006). by set the was The World developed Reference global Base under datasetbased for the includes on Soil 106 Zobler’s SOTER Resources assessment (WRB, soil Programme of unitserties and the of such FAO/UNESCO Soil as which Map soil four of depthtype are the and from in World. saturated the the hydraulic Soil FAO-UNESCO study conductivity prop- soilretention are area, classification. relationship derived for and The each unsaturated soil-water soil Genuchten’s hydraulic properties, formula conductivity, i.e., (van are water- Genuchten, represented 1980). by van Mean saturated hydraulic conductivity two di retrieved after a global soil mapsoil resource, survey (ii) campaign. soil characteristics The derived soils after wereapproaches. a accordingly detailed parameterized relative to the di TOPKAPI model was developed in(Todini two and di Ciarapica, 2001;al., Ciarapica 2011). and Several applications Todini, 2002; ofMartina the Liu et distributed et model al., al., (Bartholmes 2006;have and 2005; Diomede shown Todini, 2005; Martina et its al., et abilityternal 2008a,b; to variables correctly Liu such reproduce et not as al.,model only soil 2008; has also the moisture Vischel been discharge (Vischel et applied but on(Liu et al., several also and al., 2008a) catchments Todini, other with 2002; 2008b). good in- Martina hydrological et The capabilities al., lumped 2011). TOPKAPI ( TOPKAPI, a physically based distributed modelsimonious of parameterization. infiltration-runo The parameter valuestained of from the a TOPKAPI model digital can elevation be model, ob- soil maps, vegetation and land use maps. The ( 5 5 15 10 25 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), forms functions volume is then ff ective and also in ff mechanism. In the first pa- ) and processes ( ff cient, which is more hydrologi- ffi ect the simulation performance can forms e (NS) and the Root Mean Square ff ff study. ˇ Simunek et al., 1998). Undisturbed soil mechanism is captured. ff 4954 4953 functions cients aiming to assess the overall simulation performance. Both ffi tration at predefinedsamples pressure heads were ( analyzed(Reynolds in et the al., laboratory 2002)(Arya, by 2002) for for applying the the the saturated unsaturatedsoil hydraulic water fallow hydraulic conductivity retention conductivity, head and curve, Wind’s and the method dry the wet method WP4-T branch branch method of of (Bittelli the the and soilto Flury, the water 2009) constitutive retention for functional the curve. relationships Data of were the parameterized TOPKAPI model. according is named “schematic” becausecosts) has than much those less required soil by standard observations soil (and mapsA then (Table reaggregation 3). lower of thoseical SMU behaviour, expected using to the have,original hydropedological potentially, reasoning 19 a SMU given similar were in hydrolog- thenas Table 4. “soil-landscape” aggregated units, to Then because form strongly the eight based mapping on theDetermination units landscape named of features. here soilsome hydraulic stony properties soils atcurves and the were thin eight determined horizons, soil-landscape through water units. an retention inverse method In and following hydraulic a conductivity process of infil- In Table 5 are given the computed Nash-Sutcli The recession limb of the hydrograph is strongly connected with the baseflow. In the Points (1) to (6) refer to the specific contribution of pedology toIn discriminate the the Fig. 7a and b the results of the simulations in the two approaches are shown. The peak of the discharge is meaningful of the runo 6. 7. contribute to exploring potential andshown limitations that special of care soil is survey required and in handling soil data mapping. obtained after It soil was survey database 6 Conclusions The importance ofwhich knowing determines the a sort soilimportant of distribution in physical the ( signature absence of ofthe hydrological the crucial monitoring catchment, issue data. becomes of Thiseral even applies, making more sense for hydrological example, of to predictions making inthe the specific ungauged interaction sense basins. between of hydrology producing In and a the catchment soil classification gen- e system, this paper aimed to soil-landscape map is applied. Particularlycally the based, NS show coe a largeby increase the since soil-landscape both unit peaks map and rather baseflow than are the better FAO captured UNESCO soil map. pecially those beside the river,the is slope not of correctly the parameterized. hydrographplayed In is by the the very second soils similar approach in totion, the that have deposit observed. a (downstream) high An which, transmissivitylow important according (relative flow role to is soil here the also thickness is parameteriza- captured. and permeability) such that the Error (RMSE) coe the NS and the RMSE show a clear improvement of the model performance when the bution is more accurate, enablinga a shallow distinction soil, to and beestimated, the which made valley, means between with that the deep, also hillslope, the permeable with runo soils. Thefirst peak approach flow the is baseflow is correctly underestimated since the transmissivity of the soils, es- volume infiltrated into the soil is very low. In the second parameter set, the soil distri- rameter set, the soiloverestimated because is the shallow soil and capacity is with exceeded low in much permeability. of the The catchment runo and the of the area and point (7) refers to the As can be easily seen, simulationduces from none the first of approach the usingand important the recession FAO features soil limb. map of repro- Simulation thesoil from hydrograph data the such collected second in as the approach peakThe field instead, importance flow, and which of the the the exploits classification, rising soil the is propertiesbe able which recognized. to most reproduce a such features. 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ¨ oschl, 2000). In this ectively applied also at land- ff 4956 4955 ectiveness of a hydrology devoted and relatively ff ective in reproducing inter-unit variability (case study 1) and ff erent underlying physics governing soil particles and soil pores; this can ff Along with addressing the issue of “representative soils” indeed it is required to ad- Then here we showed four case studies based on the “representative soil profile From this case study we obviously confirmed that simple approaches are less expen- On the other hand it must be emphasised that both the identification of soil mapping In this respect, the rapid development of hydropedology, a new discipline merging of hydraulic properties than those estimated by PTFs. analysis taking into account intra-unitrespect, spatial a simple variability widely of used hydraulicdraulic approach properties. characteristics is (parameters). To the this This application lead oftexture to PTFs and assumes to soil that spatialize pore the soilretention, architecture spatial hy- variability (e.g., hydraulic of conductivity) pore size issidering distribution, similar. the pore di This connectivity, assumption water canproduce be misleading results rather when weak directly con- ductivity using properties. texture To data this for respect,was deriving in better hydraulic case con- described, study in 1 terms we showed of that statistics the and heterogeneity spatial pattern, by real measurements inexpensive soil survey (with respectto to identify the standard main soil SMU survey anda to approaches) soil select only hydraulic few characterization. aiming representative soil profiles where to produce dress the complex problem of soil heterogeneities, typically performed through a spatial (ii) a spatial analysiserties. taking into account intra-unit spatial variability of hydraulichydrological prop- characterization” applied in the contestapproach of shown physically-based modelling. to The belumped e discharge data withoutto any emphasise calibration that (casemeasurements, case study demonstrated study 4). the 4, e It characterised is by of a great limited interest dataset of hydrological assist these critical choices made by the pedologist. sive and produce resultsthat a commensurate major with leap their ining the investment soil quality map but, of (i) results most a can representative importantly, soil indeed profile be hydrological obtained characterization by and, adding hopefully, to an exist- typical upper catchment landscape where both topography and soil outcrops better included in each SMU) is more complicate in an alluvial plain settings rather than in least for this case study,scape the Richards’ scale. equation This can good beof e result case may study be 1: explainedluvial (i) by rather plain the homogeneous (but specific geomorphologic still environmental situationmade having setting consisting the two in very 1D an distinct water al- preferential terracing flow flows systems); assumption (e.g. feasible; this cracks, (ii) settingsoil silt may no layers have (horizons) distinct coatings); limiting pedological (iii) water evidences no flow of (e.g., distinct iron pedological pan, evidences fragipan,units etc.). of (SMU) and the selection of “representative soils” (for each soil typological units “the right results for therespect, right a reasons” generalization statement of (Grayson theelling and methodological application Bl pathway in from simple acan to land be complex evaluation mod- extended procedure toity for landscape of maize hydrology physically-based production model issues. (case at study landscape Despite scale 1) remarks (Beven, on 1989), the it was applicabil- proven that, at ogy viewpoint, using several case studiesgoal from of Italy these (Fig. 1). casevulnerability Regardless studies of assessment, (i.e., the specific peak irrigationtion) management flood a at forecasting, district common land scale,well aspect evaluation groundwater known concerned for that, the maize albeitracy complexity not produc- of linearly, of the increasing described the model phenomenonthus model complexity requires generates and an to higher increase hence be costs. in thethe applied. basic accu- This data lack is parameters of It and even data more is is important a in crucial ungauged basins factor where towards the proper modeling choice, according to situation is the traditional goalconcerns. of soil maps that are very much focusedpedology, on hydrology agricultural and geomorphology,map can information. contribute to We evaluated extending its the potential use and of limitations soil from a landscape hydrol- if their hydrological full potential has to be achieved; one of the main reasons for this 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , cracking, permeability, flood, ff – can produce integrated results ective interaction between pedology ff functions 4958 4957 and forms erenti scale spaziali, Convegno inaugurale ISAFOM, Ercolano, 2002. ff erent soil hydrological processes. ff erent applications with a very similar data requirement. An example of these In a context ofmon a base wider of environmental mainly management physicaldi planning, soil information, the as use a of fundamental a tool com- able to unify Greater awareness on the part oftion, hydrologists both about directly how and much indirectly and relateda what to soil informa- landscape map hydrology in issues, the lies attached behind soil database. Awareness on the part of pedologist of the need to move actual assessment of The inclusion of quantitativesemivariogram) information and on spatial soil distribution spatial oftion variability prediction of (e.g., errors soil variance, within maps. abution In to new hydropedology. this concep- framework digital soil mapping can give a major contri- some hydrological features/properties (i.e.,structure, mottles, runo etc.) from qualitative toschemes quantitative to or at better least incorporate semi-quantitative hydrological parameters in soil classification. ff – – – – involved in the landslide of Sarno 1998, Geoderma, 117,Sci. 331–346, Soc. 2003. Am. J., 73, 1453–1460, 2009. pedologico e idrologico dei suoliApplicata, della 1, piana 251–261, del 1999. F. Sarno (Campania), Quaderni di Geologia Terribile, F.: L’uso di modelliagro-ambientali a idrologici di di simulazione numerica nella soluzione di problemi retention curves in clay-loamy soils272, from 1997. particle-size determination, Soil Technol., 10, 261– Environmental Research Laboratory, EPA/600/287/035, Ada, OK, 1987. R.A.S.-E.A.F., Cagliari, 1986. SSSA Book Ser. 5., Madison, WI, 916–926, 2002. from particle-size distribution and1981. bulk density data, Soil Sci. Soc. Am. J., 45, 1023–1030, hydraulic conductivity, Vadose Zone J., 6, 423–431, 2007. uating ground water pollution potential using hydrogeologic setting, US EPA Robert S. Kerr To sum up, it is important to emphasise that e The issue has been further explored in the case study 2 where a quasi physically- In all given case studies, soil map database have been integrated with some extra Basile, A., Mele, G., and Terribile, F.:Bittelli, Soil M. and hydraulic Flury, behaviour M.: of Errors a in water selected retention benchmark curves determined soil with pressure plates, Soil Basile, A., Acutis, M., Buttafuoco, G., De Lorenzi, F., De Mascellis, R., D’Urso, G., Mele, G., and Basile, A., De Mascellis, R., and Terribile, F.: Il suolo e la protezione degli acquiferi: studio Basile, A. and D’Urso, G.: Experimental corrections of simplified methods for predicting water Arya, L. M. and Paris, J. F.: A physicoempirical model to predict the soil moisture characteristic Arangino, F., Aru, A., Baldacchini, P., and Vacca, S.: IArya, suoli L. delle M.: aree irrigabili Wind della and Sardegna, hot-air methods. 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The consequent highcan increase be in investment fully for such justified ansame analysis only information in – a inin terms more di of inclusive hydropedological frameworkcomplementarities where may be the theprotection contribution of capability soil map mapping (caseovercome to several study limitations the in 3), production current of which vector-based a approaches. might soil be crucial, given its ability to based PTF was calibrated foruncertainty each soil was mapping taken unit; on inmeasurement this board specific and by case the the the intra-unit inter-unit representativethe heterogeneity soil unit-specific profile estimated calibration function. hydraulic by properties the coupling of PTF and 5 5 25 20 15 10 15 10 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff ¨ oschl, G., , 1997. ´ oth, G.: Digital Soil Mapping as a support to ective hydraulic parameters for structured soil, J. 4960 4959 ff doi:10.5194/hess-1-915-1997 ¨ oschl, G.: Spatial modelling of catchment dynamics, in: Spatial Patterns in ´ e, F., Hengl, T., Reuter, H. 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J., Sivapalan, M., Vach 5 5 30 25 30 20 15 25 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 2 ) (km x (1: 72 50 000 589 + 4964 4963 hydromorphy index bulk density, saturated hydraulic conductivity saturated soil hydraulic conductivity, hydrologically active soil layer depth, soil texture, rainfall level, occurence of poorlypermeable layers 100 000 Selection of works involved in the (digital) soil mapping and hydrologically relevant New York, 2000. mated from topography and soil series information, Landscape Ecol., 11, 3–14, 1996. similarity, Geography Compass, 1, 901–931, 2007. ple information from a mapped reference area, Eur. J. Soil Sci., 48, 19–30, 1997. Author (year)McKenzie and Austin(1993)Zheng et clay al. Attributes content, (1996) CEC,Cialella EC, et ph, al. (1997)Voltz et al. (1997) 224Obertur et al. soil (1999) availableChaplot water et capacity soil al. drainage (2000) classes – wiliting soil bulk point texture density classes and COLE redoximorphic features, No – 100 000 observations Cartographic scale 182 Study 384, Area 208 – 426 500 12 100 000 000 – 100 000 – 192, 39 24 0.2 17, 36 Legacherie and Voltz(2000) Campling et wilting al. point (2002)Kravchenko et al. (2002)Jost et al. soil EC, (2005) drainage soil classesAgyare drainge et al. (2007)Shrestha soil et 295 soil texture, al. water ph, (2008) storage OC, CEC, 374 107 soil 600, water-holding 400 capacity, 165Joel et 195 al. (2009) – 100 000 – soil texture, – groundwater 20 – 6, 0.64 0.2 3550 50 000 0.005 to 7260 parameters. Table 1. Zheng, D., Hunt, E. R., and Running, S. W.: Comparison of available soil water capacity esti- Wilson, J. P.and Gallant, J. C.: Terrain analysis, Principles and applications, John Wiley & Sons, Wagener, T., Sivapalan, M., Troch, P., and Woods, R.: Catchment classification and hydrologic Voltz, M., Lagacherie, P., and Louchart, X.: Predicting soil properties over a region using sam- 5 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 , 2 3 , 2 Characters with a direct 2 3 , p 2 ET Rainfall Temperature nearby) images (ha) 3 3 3 , 2 3 . obs Remotely Minimum Cost , 2 N (in 100 ha) sensed polygon euro/ha 3 3 3 3 O) 2 N 3 4966 4965 Granulomety 2 2 mm) > 2 3 , 2 6 8 Exchageable bases 2 4 1 2 6 Organic carbon 4 2 2 2 1 2 quantitative evaluation. Not underlined: qualitative and semiquantitative 3 Characters with an indirect hydrological interest. Roots Coarse fragments ( Carbonates 2Mottles Electrical conductivity Coatings Biological activity 2 Total 10 Texture Consistency 5 Structure Slickensides 2 Carbonates Pores Cracks Boundaries 1 1 2 3 2 1 1 2 2 1 3 1 1 3 1 1 3 Example of soil survey and soil mapping standards. 1 Example of soil features to be described in a typical pedological description. Numbers 3 3 2 2 3 2 ff CountryCountryInter-region Region ProvinceDistrict SchematicWatershed authorities InventoryMountain 1:1 000 000communities 1:250 000Municipal district Semi-detailed 1:50 000Municipal districtFarms 0.16 Detailed Very 1–3 detailed 1:100 000 1:100 000 1:5000 1:25 4000 000 250 1:20 000 n.d. 10/50 15 100 0.2–0.7 1:8000 7–10 2 0.1 n.d. 1:33 000 400–500 Typical user Type of scale Mapping scale Curvature Deposition Morphology Erosion 2 Lithology Vegetation Rooting depth Land use Depth of rock Parent material 3 Elevation Slope Groundwater Flood Internal drainage Permeability Estimated AWC Exposure Stoniness 2 Runo Cracks 3 Soil surveyors Concentrations 6 CEC Vegetation cover Rockiness Type of observation Matrix colour 4 pH (Kcl) Field analysis at thesoil survey station Field analysis(32 of features) the soil profileLocation Laboratory analysis of the (73 features per horizon) Process for determining water and (12 features per horizon) Type of horizon (monthly data from weather stations soil profile pH (H temperature regime- soil taxonomy Table 3. evaluation. interest in hydrology. Table 2. indicate the sub-characters to bedrological described interest. for each feature. Normal: characters with low hy- Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | sat K erent ionic ff cult to ffi cult feature to determine. ffi erent from the present day (e.g. ff parameter which is very di evaluation are highly subjective and ects assessment ff ff ff water and pollutant dynamics parameters pollutants in soil behaviour flows (bypass flow) data is rarely known. Hence assessment is (even if water is absentat the time of soildescription) may have been produced in a climate di palaeoclimate) sat K 4968 4967 2 mm balance in soil. (from very low to very high) Basic environmental It is a subjective assessment because > ff is established using a table. Slope angle and (or its estimate) must be known.permeability and also hydromorphic horizons process estimate made by “expert best estimate” cracksreclamation consertia, etc.) hydraulic factors governing flood risk data water and pollutant soil horizon depth. Assessment istabular made data by and using empirical formulas. process qualitative tables) of particles size (matrical analysis) and degreesof of the destinctness aggregates in soils governing the assessment horizons. dynamics of water and breakage, type of breakage, degreecementation, of adhesiveness and plasticity. soil hydrologic soil structure, can also erent colours SQT It describes the colour (Munsell Tables) It is information that The size of the mottles depends on the soil ff estimate QL The class of runo ff 0.1 mm) magnifying glass (10 X) which strongly a field)smaller than 2 mm (infield) the schemes (e.g. USDA), of textural class and/or %macroporosity (pores frequency (visual> estimate using estimate comparative many calculating estimate water ofa sand, soil experience silt and horizon and also clay calibration on the (comparative tables) of macropores, using acohesion and adhesion connected to structure, It is appraised by a strongly quality-based hydrological aggregates and by depth means of of cracks their (metric resistance measurements) to in hand soil bypass flow processes that, by influencing specific soils linear under function investigation of the soil water content quality-based assessment the thickness of each horizonaggregates produced bywaterlogging limits and location of the mottles. of water stagnation forms of iron and manganese. The mottles information variability soil surfacein the investigated soilprofileRuno measurements) of frequency, width and observations depth in of the soil profile along with indirect processes of preferential data linear function of the water content in soil information (interviews with farmers, land (usually related to Fe and/orMn), on the surface of the frequency (visual estimate using comparative tables), size (metric measurement), contrast enables the assessment chemical and physical conditions (e.g. of pH) the (relative) degree and on the behaviour of the di Types of data of direct hydrological interest contained in the databases of soil maps. Types of data of direct hydrological interest contained in soil map databases. Abbrev: ff . of sub- . of sub- N InternaldrainageEstimated Estimate of eater rate removal inAWC the soil profile Estimate of QL Available WaterBoundaries (2) QL Capacity Lower for Assessment limit vegetation on of the horizons. basis It of is slope,Mottles texture, (10) Assessment based on texture, organic QT matter, Patches skeleton, of presence di of horizons with low It is It a is Metric feature a measurements governing feature (cm) governing Stronly bulk quality-based density, Many rock assessment of fragments, on the salinity, a parameters roots required and for this infiltration and runo infiltration and runo Essential basic This parameter can have marked spatial Cracks (3) Occurence of cracks at theGroundwater SQT Occurence of groundwater Estimate (using comparative tables QL and metricFloods It is Assessment evidence madeRuno of both on the basis of direct Flood risk Basic environmental It is a strongly anisotropic parameter, non- Strongly quality-based measurement QL Estimate of morphometric, morphodynamic and Basic environmental Rather subjective evaluation Type of soilfeature( Description of the feature Method Methodological description Main potential Main limitations features) N Consistence (5) Soil features related to QL Description of consistence and plasticity of soil It is another feature It is a feature determined by a strongly features) to be described Texture (4) Estimate of soil particlesStructure (96) SQT Analysis of soil aggregates. Assessment, using QL stadardized tactile testsPores (4) Description of type (comparison with diagrams), Estimate of Essential soil parameter to ItCracks Evaluation is (2) of a % feature data strongly requires much It is a feature Occurence of of a cracks strongly within qualitative SQT SQT Description of size, frequency and Frequency shape estimate (comparative tables), width Sign of potential It is a feature, Parameter strongly anisotropic with non- It is a rather di Type of soilfeature( Description of the feature Coarsefragments (10) Method larger than 2 mm (in Methodological the description Estimate of soil particles SQT Describes size (metric measurement), (using shape reference diagrams), lithology and It is essential Main potential information for Main Visual limitations estimate by comparative tables. Table 4b. QT: quantitative; QL: qualitative; SQT: semiquantitative. Abbrev: QT: quantitative; QL: qualitative; SQT: semiquantitative. Table 4a. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent physical ff ected by the required rather erent hydrological ff ff ects soil content may have very di ff structure, porosity andhence many physical properties processes 4970 4969 cient 0.21 0.89 soils according to the Soilclassification Taxonomy scheme. (USDA) Examples include XericUstic, udic, aquic moisture regime.This analysis consists in abalance but simplified assessment. very on water useful the It basis is ofevapotranspiration monthly data rainfall and and of silEvaluation AWC. is made using bucket-based(e.g. models Billeaus, Newhall) for coarse a quality(control the specific of section) very physical soil inputs. reality much depth This of a soils is the case strongly associated to It is a description for of AWC, the rainfall rather and “static” evapotranspiration. water balance. Since sil particles can beor aggregated inorganic by cementing organic agents, realanalysis granulometric (after dissolving all cements)granulometric governing or analysis very many apparent (no robust parameter pretreatments) canperformed. be physical processes permeability, depending on meso and macropores, properties, but which also can for change water according retention to clay mineralogy (kaolinite versus smectite ratio) ffi ) 14.68 6.58 1 e coe − ff s 3 Nash-Sutcli RMSE (m Parameter FAO soil map Soil-landscape unit map particle size classes (at leastsand, silt, clay) coarse sand, find sand, silt and clay (e.g. Pipette many hydrological method of have hydrometer very method). di evaluations. It is a behaviour. This is especially th case for Data included in the standard soil database (from soil mapping) with a direct hydro- Statistical indexes of the simulated discharge applying FAO and soil-landscape map. . of sub- N Water regime Simplified water balance QT This analysis is generally performed to classify It is a rather synthetic The assessment of the “water regime” is Type of soilfeature( Description of the feature Granulometry Laboratory analysis of the Method Methodological description QT Analysis of frequency of coarse fragments,Organiccarbon Basic information for Laboratory analysis of Main organic Soils potential C with content similar granulometry can still QT Main limitations % of organic C (e.g. typically performed after dichromate oxidation It method) is a soil feature that Soils with the same organic carbon strongly a features) to be described Table 5. Table 4c. logical interest. Abbrev: QT: quantitative; QL: quality; SQT: semiquantitative Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | + COMPLEXITY +

COSTS

(spatialization)

Aggregated

Not aggregated aggregated Not MECHANISTIC OUTPUT …

Binary Data Data Mining Data EMPIRICAL MODELLING Hydraulic properties MEASURED MEASURED MEASURED MEASURED +PTF CAL PTF NOT PTF CAL NOT PTF CAL NOT PTF CAL NOT PTF CAL and Terrain Parameters (TP) Parameters Terrain and Soil Variable Spatial Map Spatial Variable Soil Digital Terrain Analysis Terrain Digital Digital Elevation Model Elevation (DEM) Digital Auxiliary and Exhaustive Inputs Exhaustive Auxiliary and [case study #1] study [case X X X (CLORPT/SCORPAN)

INPUT soil obs. soil georeferenced soil db soil georeferenced mean of n erent approaches applied and their main char- ff 4972 4971 X X X X X X soil borne representative remote proximal air- soil information) 1 2 3 4 5 6 7 8 9 Environmental Datasets Datasets Environmental METHOD (Synopsis of (Synopsis landscape and Probe Sensing Probe Test of soil map Test (analogical or digital) (analogical (vector data, report, db) report, data, (vector Photointerpretation ? Systematic Soil Survey Systematic Final Final Soil Mapping Units Preliminary Soil Mapping Units (by the organization financingthe organization (by the soil map) Preliminary Soil Survey

Preliminary Landscape Classification

[case studies #1,2,3] studies [case [case study #4] study [case

(final correlation) (final (preliminary correlation) (preliminary PRINProject PRINProject

Spatialization

Soil Map 1:25000 Map Soil Soil Map 1:25000 Map Soil 1:25000 Map Soil revision

APPROACH

(CROPSYST) DETERMINISTIC DETERMINISTIC FAO Case study 1: schematic view of the di Data flow of the main steps involved in both conventional and digital soil mapping. HYBRID (ALMAGRA) (ALMAGRA +SWAP) (ALMAGRA acteristics. Fig. 2. Fig. 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent tested LE methods in relation to the ff 4974 4973 Hydrological Functional Units classified on the basis (b) . 800 d Soil map and (a) Case study 1: performance indexes of the di Case study 2: Fig. 4. of the “functional property” Fig. 3. level of complexity. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | a simple (a) (a) (b) 4976 4975 a process-based model. (b) Groundwater vulnerability zoning considering soil protection capacity with Case study 3: soil retention curves of surface horizons with the same risk score in the LE scheme and Fig. 6. Fig. 5. DRASTIC-like models. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (a) (b) Hydrograph of the Sangone (b) 4977 Hydrograph of the Sangone catchment from January 2002 to September 2006. catchment at the main floodgrey event line in September is 2006. thesoil-landscape Dashed data simulation line set. is using the the observed FAO discharge, dataset, and black line is the simulation using the Fig. 7. (a) Dashed line is theblack observed line discharge, is grey the line simulation is using the the simulation using soil-landscape the data FAO set. dataset, and