using object-based geomorphometry for hydro-geomorphological … · 2016-08-31 · 3494 d. guida et...

Hydrol. Earth Syst. Sci., 20, 3493–3509, 2016www.hydrol-earth-syst-sci.net/20/3493/2016/doi:10.5194/hess-20-3493-2016© Author(s) 2016. CC Attribution 3.0 License.

Using object-based geomorphometry for hydro-geomorphologicalanalysis in a Mediterranean research catchmentDomenico Guida1, Albina Cuomo1, and Vincenzo Palmieri21Department of Civil Engineering, University of Salerno, Fisciano, 84084, Italy2ARCADIS, Agency for Soil Defense of the Campania Region, Naples, Italy

Correspondence to: Albina Cuomo ([email protected])

Received: 11 February 2016 – Published in Hydrol. Earth Syst. Sci. Discuss.: 9 March 2016Revised: 28 July 2016 – Accepted: 1 August 2016 – Published: 31 August 2016

Abstract. The aim of the paper is to apply an object-basedgeomorphometric procedure to define the runoff contribu-tion areas and support a hydro-geomorphological analysis ofa 3 km2 Mediterranean research catchment (southern Italy).Daily and sub-hourly discharge and electrical conductivitydata were collected and recorded during a 3-year monitor-ing activity. Hydro-chemograph analyses carried out on thesedata revealed a strong seasonal hydrological response in thecatchment that differed from the stormflow events that oc-cur in the wet periods and in dry periods. This analysis en-abled us to define the hydro-chemograph signatures relatedto increasing flood magnitude, which progressively involvesvarious runoff components (baseflow, subsurface flow andsurficial flow) and an increasing contributing area to dis-charge. Field surveys and water table/discharge measure-ments carried out during a selected storm event enabled us toidentify and map specific runoff source areas with homoge-neous geomorphological units previously defined as hydro-geomorphotypes (spring points, diffuse seepage along themain channel, seepage along the riparian corridors, diffuseoutflow from hillslope taluses and concentrate sapping fromcolluvial hollows). Following the procedures previously pro-posed and used by authors for object-based geomorphologi-cal mapping, a hydro-geomorphologically oriented segmen-tation and classification was performed with the eCogni-tion (Trimble, Inc.) package. The best agreement with theexpert-based geomorphological mapping was obtained withweighted plan curvature at different-sized windows. By com-bining the hydro-chemical analysis and object-based hydro-geomorphotype map, the variability of the contribution ar-eas was graphically modeled for the selected event, whichoccurred during the wet season, by using the log values of

flow accumulation that better fit the contribution areas. Theresults allow us to identify the runoff component on hydro-chemographs for each time step and calculate a specific dis-charge contribution from each hydro-geomorphotype. Thiskind of approach could be useful when applied to similar,rainfall-dominated, forested and no-karst catchments in theMediterranean eco-region.

1 Introduction

In order to gain a better understanding of hydrology, it isessential to study the complex interactions and linkages be-tween watershed components, such as drainage network, ri-parian corridors, headwaters, hillslopes and aquifers and re-lated processes operating at multiple scales (National Re-search Council, 1999). Hydrological science plays an impor-tant and fundamental role only when it provides an integratedknowledge and understanding of the forms and processes thatoperate in watersheds at multiple space–time scales in thelandscape (Marcus et al., 2004). A useful way of understand-ing the response of catchments to rainfall events is to ana-lyze stream discharge vs. rainfall per unit of time, plottedas a storm flow hydrograph and hyetograph, respectively. Inrecent decades, hydrologists have carried out numerous stud-ies on catchment and hillslope hydrology in order to definewhen, how and where runoff is produced and how it pro-gressively increases along the drainage network. Hydrolo-gists generally agree that following rainfall, new-event wa-ter components are added to the old, pre-event water com-ponents through various hydrological mechanisms that aregenerally referred to as baseflow components that are derived

Published by Copernicus Publications on behalf of the European Geosciences Union.

3494 D. Guida et al.: Using object-based geomorphometry for hydro-geomorphological analysis

from deep and shallow aquifers, thus expanding and reducingthe runoff-contributing areas (Betson, 1964). The most com-mon general concept that explains the above-mentioned hys-teretic behavior is the variable source area (VSA) concept.This concept was originally proposed by Hewlett (1961)and later adopted by other authors (Dunne and Black, 1970;Dunne and Leopold, 1978; Huang and Laften, 1996; Van-der Kwaak and Loague, 2001; Zollweg et al., 1995, Pionkeet al., 1996). Despite its early formulation, it has providedthe hydrological background for more recent research stud-ies (Lyon et al., 2004, Easton et al., 2007, 2008; Buchananet al., 2012; Moore et al., 1988; Barling et al., 1994; Kwaad,1991; Easton et al., 2010; White et al., 2011). Contemporar-ily, the “hydro-geomorphic paradigm” was proposed by Si-dle et al. (2000) in order to discriminate the VSA hydrologicsources and pathways, which refers to the connected hydro-geomorphic components of the catchments (hollow, hillslopeand riparian corridor). Within a more general program forflood hazard assessment procedures, the hydro-geomorphicparadigm was used to generalize at basin and regional scalein southern Italy by Cuomo (2012), by means of hydro-geomorphology (Okunishi, 1991, 1994; Babar, 2005; Sidleand Onda, 2004; Goerl et al., 2012). Cuomo (2012) intro-duced and applied a new hydro-geomorphological basic unit,the hydro-geomorphotype, by using the Salerno Geomorpho-logical Mapping System (Dramis et al., 2011; Guida et al.,2012, 2015) as a framework for object-based geomorpho-logical mapping. Based on the up-to-date and shared the-oretical geomorphometric background (Baatz and Schäpe,2000; Dragut and Blaschke, 2006; van Asselen and Seij-monsbergen, 2006; Anders et al., 2011; Dragut et al., 2013,2014; Eisank et al., 2014), this proposal is currently un-der experimental calibration as an effective, object-based ge-omorphometric procedure for spatial individuation, objec-tive delimitation and automatic recognition of the hydro-geomorphotypes from the perspective of an object-based dis-tributed hydrological modeling (Cuomo et al., 2012).

Linking geomorphometry with hydrology towards hydro-geomorphology gives consistency to the suggestion made byPeckham (2009) with the aim of simplifying the issue of thecomputational cost and time of a fully distributed model.

In the past, many authors made extensive use of chemicaland isotopic tracers in order to separate the runoff compo-nents recorded in the hydrographs and pinpoint distinctivesources and pathways by using the geochemical and isotopicsignature of water at parcel scale or for small catchments(Klaus and McDonnell, 2013). However, applying only thehydro-chemograph and isotopic separation methods to an ex-perimental parcel cannot provide sufficient information onthe spatial distribution of runoff sources and paths for basinsas a whole, due to their spatial heterogeneity structure andtime process variability.

Moreover, extensive use of the above-mentioned methodsis more expensive and time-consuming than the quantity andquality of the data collected and the knowledge gained. As

stated by Ladouche et al. (2001), with these methods aloneit is possible to identify the type, timing and volume of therunoff components, but it is impossible to define the spa-tial origin and related pathways during storm events accu-rately. In order to overcome these difficulties and by follow-ing the general approach used by Latron and Gallart (2007),we used an integrated, hydro-geomorphological approachfor studying a Mediterranean research catchment in south-ern Italy. This approach is based on detailed geomorpho-logical surveys, mapping and 3-year hydro-chemical mon-itoring. It integrates a new procedure for identifying andseparating hydro-chemical runoff components and a geo-morphometric application for the objective delimitation ofthe source areas, where each runoff component is gener-ated (Cuomo and Guida, 2013; Guida and Cuomo, 2014).Starting from these premises, the paper describes the studyarea as a Mediterranean research catchment and presents thehydro-chemical data set recorded during the monitoring ac-tivity carried out in the 2013–2014 calibration period. Inthe next section an original procedure is described for de-termining the timing, type and hydro-chemical signature ofthe runoff components involved during storm events. Withthe aim of spatially defining these runoff sources, an object-based hydro-geomorphological map was then set by hydro-logically oriented segmentation and classification. Finally,the results of combined hydro-chemical and object-basedhydro-geomorphometric analysis are discussed in order todetermine the variability of the contributing area during asignificant storm event (see the storm event hydro-chemicaldata set in the Supplement data).

2 Hydro-geomorphology and monitoring activity of thestudy area

The study area is a forested and hilly catchment locatedin the Bussento River drainage basin, the 3 km2 Ciciriellocatchment in the Cilento and Vallo di Diano National Park–UNESCO Global Geopark, southern Italy (Fig. 1).

At the base the terrigenous bedrock is composed of alower Tertiary, marly-clayey formation passing in unconfor-mity upward to middle Miocene, westward-dipping sand-stone strata and pelitic intervals. A lenticular 10 m thickmarly layer (“Fogliarina Marl”, as a geosite in the Geopark)outcrops along the right-hand side of the valley. Regosols,regolite and gravelly slope deposits up to 5 m thick cover theabove-mentioned bedrock. The mainstream bed, rectilinearand dipping strata subsequent to main faults is incised in allu-vial gravelly and smooth deposits and partly in bedrock; thesecondary streambed is exclusively in bedrock, subsequentto minor fault systems. From a hydro-geomorphological per-spective, the groundwater circulation is controlled by thelitho-structural arrangement of the above-mentioned bedrockformations, where the marly-clayey formation constitutesthe local aquitard below the sandstone aquifer. The west-ward dipping of the permeability boundary causes a general

Hydrol. Earth Syst. Sci., 20, 3493–3509, 2016 www.hydrol-earth-syst-sci.net/20/3493/2016/

D. Guida et al.: Using object-based geomorphometry for hydro-geomorphological analysis 3495

Figure 1. Hydrogeological map of the Ciciriello Experimental Catchment and location of the monitoring stations (modified from Cuomoand Guida, 2016). Legend: bedrock lithology: Ma, marly clay and argillite Tertiary formation, and base aquiclude; Ss, sandstone Mioceneformation, and fractured general aquifer; Mf, marl, interlayered and perched aquifer.

westward groundwater flow, convergent toward the lowerapex of the wedge-like hydro-structures (“hydro-wedge” inCascini at al., 2008 and Cuomo and Guida, 2016), where themain permanent springs are located. In the headwaters, col-luvial hollows are situated at the bottom of the zero-orderbasins, and are considered to be the main headwater hydro-geomorphotypes by Cuomo (2012), where dominant satu-ration excess runoff occurs mainly during the wet season.The streamflow of both permanent springs from the bedrockaquifers and seasonal springs from colluvial headwater in-crease down valley.

From December 2012, water depth (D), discharge (Q) andspecific electrical conductivity (we used either sEC or ECin the following) were measured daily at the main station,hourly during the floods and weekly at the sub-stations dur-ing the inter-storm periods (Fig. 1). The Q measurementswere obtained with the Swoffer 3000 current meter (Swof-

fer Inc., USA), and the EC parameter was measured withmulti-parametric probe HI9828 (Hanna Instruments Inc., Ro-mania). The monitoring year 2013–2014 (Fig. 2) provided acomplete hydro-chemical data set, which enabled us to carryout the analysis at seasonal and event timescales (Cuomo andGuida, 2014).

3 Hydro-geomorphological procedure for thecontributing areas’ individuation

The contributing area is a dynamic hydrological concept be-cause it may vary seasonally. The extension of the contribut-ing area is strongly influenced by various static factors suchas topography and soils, and dynamic factors such as an-tecedent moisture conditions, rainfall characteristics (Dunneand Black, 1970) and vegetation cover.

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Figure 2. Plot of the hydro-chemograph data set recorded at the main monitoring station (BS16_01) and the 10 min rainfall plot at the Sanzarain gauge (from Cuomo and Guida, 2016). Legend: numbers indicate the selected events; horizontal lines are representative of the referenceparameter ranges; black dashed-double dot lines indicate EC maxima in the dry period; the black dashed-dot line represents EC minimumduring the dry period; the black dashed line indicates EC maxima in the wet period; the black dotted line represents an EC minimum inthe wet period; the gray dashed line indicates the Q minima in the wet period; the gray dotted line indicates the average Q maximum inthe wet period; finally, the gray dashed-dot curve indicates the theoretical annual baseflow curve of the catchment during the period underconsideration.

Figure 3. Flow chart procedure for identifying contributing areas.



Figure 4. (a) The V-notch weir at the BS16_01_01dx and (b) stations.

In the following sections, an integrated procedure is pro-posed that uses simple geomorphometric tools to take intoaccount various hydrological and geomorphological factorsthat cause time–space runoff variability in the catchment casestudy.

The flowchart in Fig. 3 shows the three integrated ap-proaches used in the application.

The first approach on the left-hand side highlights theexpert-based activities by geomorphological surveys and di-rect monitoring carried out at basin scale before and duringthe application event and the derivation of traditional, hand-drawn, expert-based geomorphological maps. The field-oriented flow accumulation scenarios were obtained fromdata collected at the control points (Fig. 1) for each eventtime step (five time steps) and each hydro-geomorphotypeand by using the flow accumulation map derived from thesecond step described below. The expert-based activitiesare illustrated in Sect. 3.1. The second approach (see theflowchart in the center) shows the geomorphometric routineactivities carried out during the application, as illustrated inSect. 3.2. Starting from the topographic data source, a hydro-logically corrected DEM was obtained and the log of the flowaccumulation map was derived, which was reclassified inthe first approach in order to obtain the best agreement withthe field evidence highlighted during the storm event at eachhydro-geomorphotype. The field-oriented flow accumulationmaps were obtained as a proxy for the contributing area sce-narios. As better explained in Sect. 3.2, after five elabora-tion steps, the geomorphometric analysis provided us withthe object-based hydro-geomorphological map of the catch-ment, quantitatively defining the spatial extension of the ba-sic hydro-geomorphotypes. The hydro-geomorphotype mapwas calibrated with the hydro-chemical analysis illustrated inSect. 3.3 and was then overlaid with the five contributing areascenarios, thus obtaining the final hydro-geomorphologicalscenarios maps.

3.1 Direct survey on the catchment during a stormevent

Before and during the storm event in the period from 29 to 31January 2015, one of the authors and field collaborators car-ried out direct field surveys by measuring EC and, whereverpossible, the Q parameters on the control points in Fig. 1,and repeated them at each time step of the storm event. Thepre-event conditions were detected at 17:15 on 29 January2015 by carrying out systematic surveys and taking mea-surements from the main stream and secondary channel sta-tions (Fig. 4a), where only groundwater feeds the dischargealong the riparian corridors. After the beginning of rainfall,measurements were taken from 07:20 to 09:10 on 30 Jan-uary 2015 at the zero-order basin springs and hollow stations(Fig. 4b), where the soil became increasingly saturated andcontemporarily new water was added from the riparian cor-ridor downstream.

During the storm event, repeated measurements weretaken at the same control points from 11:30 to 13:00 detect-ing direct runoff (Fig. 5a) and soil pipe contribution (Fig. 5b).

Figure 6a shows the hydro-chemograph of the storm eventrecorded at the main station and cumulative rainfall mea-sured at the nearest rain gauge station. On the plot, the phasesof hydrological response in the catchment were determinedby means of the progressive runoff generation activation,identified with the above-mentioned field measurements. InFig. 6b, the hysteretic Q-EC cycle (Cuomo and Guida, 2016)of the event demonstrates homogeneity in hydro-chemicalresponse in the rising and recession limbs. At 20:00 on 29January 2015, the field measurements at piezometers and Q-sEC values (approximately 60 L s−1 and 240 µS−1) recordedat the main station were typical of pre-event conditions oc-curring during the wet period, as found by Cuomo andGuida (2016). After it started raining, in addition to the di-rect rainfall in the main streamflow, the contribution fromgroundwater ridging along the riparian corridor and flood-plain began to feed the total discharge. The contributionarea expands with continual rainfall and excess saturationrunoff is progressively added to the discharge from the col-



Figure 5. Measurements at 12:00 in the dirt road point controls (a) and the soil pipe (b) with respective EC values.

Figure 6. (a) Hydro-chemograph plot of the 29–31 January 2015 storm event and related hydro-geomorphological phases, during which therunoff components are progressively added, according to Table 3; (b) Q-EC hysteretic cycle of the storm event.

luvial hollows, reaching approximately Q= 1000 L s−1 andsEC= 100–120 µS−1. In addition to these values, firstly themacropore contribution is added. Finally excess infiltrationrunoff from the saturated areas becomes dominant, whichprogressively increases the discharge, reaching asymptoticalsEC= 80 µS−1 values.

In order to obtain the contributing area scenarios, the flowaccumulation map by means of the SAGA module imple-mented in QGIS was generated. More precisely, the log val-ues of the flow accumulation map were reclassified accord-ing to the actual conditions observed in streamflow and eachhydro-geomorphotype during five different scenarios that oc-curred during the training storm event. The final contributingarea scenario map shows the best agreement between the re-classified log values of the flow accumulation map and thefield evidence.

3.2 Object-based hydro-geomorphological mapping

In order to quantitatively define the runoff source areas, anobject-based hydro-geomorphological map of the catchmentwas created using an original, automatic spatial analysis pro-cedure. Starting from the Campania Region Technical Map at1 : 5000 scale (CTR), a vector map providing elevation val-ues, a digital elevation model (DEM) with a 5 m cell sizewas obtained by means of the Topo-To-Raster tool (TO-POGRID) in ArcGIS. This algorithm provides an interpola-tion method specifically designed for creating hydrologicallycorrected DEMs. Moreover, further spurious sinks have beenremoved by means of the Fill tool. In the scientific litera-ture some methods are known for a more suitable grid res-olution (Hengl, 2006) based on the properties of the inputdata (i.e., complexity of the land surface), but the grid spac-ing used appeared to be suitable for hydro-geomorphological



applications since it follows the general rule that it shouldbe adequately sufficient at the local hillslope scale, mark-ing the transition in process dominance from hill slope tochannel (Peckham, 2009). This DEM was used for creatingan “object-based” hydro-geomorphological map that was ob-tained with a step-by-step rule set. During the first step, a ge-omorphometric analysis was performed by calculating planand profile curvatures at increasing cell window sizes: 5, 7,9, 11, 13, 15, 17, 19 and 21 cells. The multiscale analysis ofcurvatures was performed with Landserf free GIS software,thus obtaining a raster layer for each geomorphometric cal-culation.

During the second step the best agreement with expert-based geomorphological mapping was achieved with eCog-nition Developer software by means of an original mul-tiresolution segmentation algorithm, using appropriate land-surface parameters.

The multiresolution segmentation algorithm merges spa-tially contiguous pixels or cells into “image objects” (seg-ments) based on local homogeneity criteria of the input pa-rameters. These segments, bounded by discontinuities in theinput variables, are then used as building blocks in the clas-sification, according to attributes such as average values ofinput variables, shape indexes, and topological relations ofsegments (Dragut et al., 2013).

More precisely, the morphometric parameters obtainedduring the previous step (plan and profile curvatures at var-ious cell windows) are used with a proportional increasedweight to the increasing cell window size for each raster layer(Table 1); sine and cosine of aspect were also used as inputparameters. We did not consider the slope gradient since itis quite constant except for the valley bottom and hilltop anddid not provide us with additional information for the seg-mentation procedure.

Other settings used for this algorithm are scale 7, shape0.0002, and compactness 0.0002.

During this procedure, the segments obtained were com-pared to the expert-based geomorphological mapping byusing the target-training procedure proposed in Guida etal. (2015) (Fig. 7a).

The image objects obtained from the segmentation areshown in Fig. 7b.

In the third step, the objects obtained during the previousstep were classified. The classification procedure was car-ried out according to the criteria proposed by Hennrich etal. (1999), whose conceptual background was the “landscapecatena” (Conacher and Dalrymple, 1977), which combinessurface form and pedo–hydro–geomorphological processesat hillslope scale.

The classification was based on the sum of the planimet-ric curvatures that were re-classified according to the thresh-old values listed in Table 2. The interval values listed in Ta-ble 2 were achieved by a supervised classification. By onlyusing the plane curvature sum computed with different win-dow sizes, we were able to obtain an object-based hydro-

Table 1. Weights assigned to each layer implemented in the eCog-nition developer software for the multiresolution segmentation al-gorithm.

Layer (cell window) Weight

Plan curv (5) 1Plan curv (7) 2Plan curv (9) 3Plan curv (11) 4Plan curv (13) 5Plancurv (15) 6Plan curv (17) 7Plan curv (19) 8Plan curv (21) 9Prof curv (5) 1Prof curv (7) 2Prof curv (9) 3Prof curv (11) 4Prof curv (13) 5Prof curv (15) 6Prof curv (17) 7Prof curv (19) 8Prof curv (21) 9Aspect Cos 10Aspect Sin 10

geomorphological map (Fig. 7c), which was in good agree-ment with the expert-based geomorphological map.

Finally, a spatial statistical analysis was performed on theobject-based hydro-geomorphotype map (Fig. 7c) and thefive contributing area scenarios maps in order to evaluatetheir spatial relationships for the training storm event that oc-curred in January 2015 (Fig. 6). The application at the stormevent timescale is described in the next section.

3.3 Dynamic hydro-chemograph separation

In order to understand the runoff generation that occurs dur-ing storm events for each period (wet/dry), we used theQ-EC relationship data analysis proposed by Cuomo andGuida (2013) and Guida and Cuomo (2014) due to thegood agreement between the hydro-chemograph separationand the hydrograph filtering comparative procedure intro-duced by Longobardi et al. (2014, 2016). Moreover, Cuomoand Guida (2016) subsequently proposed a modified massbalance procedure based on a “step-like”, recursive, two-component hydrograph separation for the Ciciriello catch-ment. The authors assigned a correspondent mechanism ofrunoff generation to each component and the Q-EC thresholdvalues for each mechanism in that contributing area started toenlarge and expand.



Figure 7. (a) Expert-based hydro-geomorphological map; (b) multiresolution segmentation map; (c) object-based hydro-geomorphologicalmap obtained by classifying the multiresolution segmentation map using the plan curvature sum only.

Figure 8. Delimitation of the five inner fields that define the limits of seasonal response of the catchment (modified from Cuomo and Guida,2016) and, in blue, the hysteretic cycle of the study event, from its beginning (blue circle) to its end (blue square). Legend: UH1 and W1,upper hyperbolic curve 1 and wet area 1, respectively (typical of the Q-EC mixed value of groundwater and groundwater ridging); UH2and W2, upper hyperbolic curve 2 and wet area 2, respectively (typical of the Q-EC mixed value of groundwater, groundwater ridging andsubsurface flow); UL3 and W3, upper linear curve and wet area 3, respectively, typical of the Q-EC mixed value of groundwater, groundwaterridging subsurface flow and direct runoff; LHg, lower hyperbolic curve typical of the Q-EC response when direct runoff is suddenly addedto the groundwater following the heavy showers that occurred during the dry period; D, the dry area where the Q-EC typical of a dry fall forwhich only the groundwater flow feeds the streamflow; T , transition area, where the Q-EC typical values of a dry–wet or wet–dry fall, whenthe groundwater flows, groundwater ridging and soil pipe feed the streamflow.

In this study, these values were used for each phase ofthe field survey in order to verify the correspondence be-tween the end-member hydro-chemograph signature pro-posed by Cuomo and Guida (2013, 2016) and Guida andCuomo (2014), and the starting runoff contributing area.

Cuomo and Guida (2016) adopted the daily data set illus-trated in Sect. 2 (Fig. 2) using the end-members that the au-thors measured at specific stormflow components by carry-ing out direct surveys and taking piezometric measurements.They obtained three upper boundary curves and one lowerboundary curve (Fig. 8), each of them representing a spe-



Table 2. Geomorphometric classification, geomorphological correspondence, hydro-geomorphotype definition and hydro-geomorphologicalbehavior for each hydro-geomorphotype.

Sum of plan curvatureclass (SPC)

Geomorphometricparameters and to-pographic position

Landform, com-ponent or element(Dramis et al.,2011)

Hydro-geomorphotype(HGT in Cuomo,2012)

Hydro-geomorphologicalbehavior

SPC <−13.4 Convex, divergentflow-like, upslope

Upland, summit,peak, crest

Ridge Groundwater recharge onbare bedrock and dominantexcess infiltration runoff af-ter storm

−13.4 >=SPC <−3.76 Light convex-divergent flow-like,up to mid-slope

Shoulder, sideslope

Nose Shallow soil, groundwaterrecharge area, prevalentlyexcess infiltration runoff

−3.76 >=SPC < 2.3 Light convex-planar, parallelflow-like, midslope

Scarps, backslope, foot slope,wash slope, talus,

Hillslope Debris, deep soil, shallowaquifer, excesssaturation excess andsub-surficial runoff

2.3 >=SPC < 11.6 Planar to light con-cave, convergentflow-like, upslope

Glen, swallet, scar Hollow Deep soil, shallow aquifer,prevalently excess satura-tion, delayed runoff pro-duction

SPC=> 11.6 Concave, con-vergent mid- todown-slope

V-shaped stream,gully, bank, streambed

Riparian corridor Shallow soil, groundwaterdischarge, prevalently sub-surface, delayed return flowand groundwater ridging

Table 3. Hydro-chemical parameter range, distinctive for the wet (W ), dry (D) and transition (T ) period events. Legend: GW is for ground-water, SSF is for subsurface flow, and DR is the direct runoff (modified from Guida and Cuomo, 2016).

Field Processes and contributing areas ECquick range ECslow range Qthreshold(mS cm−1) (mS cm−1) (L s−1)

W1 GW from bedrock deep and perched aquifer 250–300 30–50GW+GWridging added from riparian corridor 200–220 400

W2 GW+GWridging along the riparian corridor 200–220GW+GWridging+SSF added from colluvial hollow 120–180 1000

W3 GW+GWridging+SSF 120–180 1000GW+GWridging+SSF+DR added from soil pipe 70–180 � 1000

D GW 320–350 3–5GW+GWridging 100–180 400

T GW+GWridging 100–180 400GW+GWridging+DR added from soil pipes 100–120

cific mechanism, source area and timing of runoff produc-tion. The lower hyperbolic curve (LHg) delimits all the Q-EC values recorded during the dry period. The upper hy-perbolic (UH) curves delimit the Q-EC values that are typ-ical of groundwater and groundwater ridging for the UH1curves. The second upper hyperbolic curves (UH2) startwhen the UH1 reaches its horizontal asymptote and the sub-surface mechanism starts, following which the upper lin-ear curve (UL) starts when the direct runoff and soil pipe

mixes with the previous components. The estimated inter-section points between the three upper consecutive curvesare the Q-EC threshold values for which another mecha-nism starts and hydro-dynamically interacts with the previ-ous mechanism. In this way, the waters join together beforereaching the streamflow. Subsequently, the authors carriedout the same procedure on the 13 storm events shown inFig. 2. Events nos. 1, 2, 3, 4, 10, 11, 12, and 13 were as-signed to the wet recharging period, while event nos. 5, 6, 7,



Figure 9. (a) Pre-event hydro-chemograph conditions, just before the storm event, with Q= 60 L s−1, filled blue square, and EC= 240 µS−1,filled green diamond, (b) scenarios corresponding to groundwater and decreasing groundwater ridging contribution to streamflow runningexclusively along the riparian corridor and main streamflow.

8, and 9 were assigned to the dry discharging period. More-over, the Q-EC relationship highlights three different typesof hydrologic behavior occurring in the three hydrologic pe-riods: wet (W ), dry (D) and transition (T ). In this way, theboundary curves between the dry–wet and wet–transitionevents were obtained in order to define further inner fields.Figure 8 shows a typical “threshold hydro-geomorphologicalsystem”, where each source runoff remains independent dur-ing low magnitude events but interacts physically and func-tionally with other sources at higher event magnitudes, thusinducing superposed hydrological mechanisms and complexhydro-chemical water mixing by dilution, dispersion and dif-fusion. By identifying these five areas with respect to the hy-drologic behavior of the catchment, it was possible to carryout the analyses for delimiting the contributing area in thenext section using the thresholds listed in Table 3.

By including the hysteretic cycle of the 29–31 Jan-uary 2015 study event in the plot of Fig. 8, the hydro-geomorphological response (see Supplement data) can beclassified as typical for a wet period that occurred after ashort transition period during which the aquifer began tofill and groundwater ridging decreased progressively. As ex-pected, during the event all the runoff components were pro-gressively activated when the Q-EC threshold values foreach started. Consequently, the contributing areas enlargedthe floodplain upslope, the riparian corridors and the zero-order basins upstream, encompassing the Q-EC value rangeslisted in Table 3. These values were verified during thefield survey reported in Sect. 3.1 and used for the hydro-geomorphological analyses of the next section.

4 Results

For the storm study, the variability of the contributing areawas obtained by combining the hydro-chemical procedureand the object-based hydro-geomorphotype map. As a resultof this analysis, contributing area space–time variability wasobtained for the selected storm event by combining hydro-chemical procedure outcomes, the hydro-geomorphotypemap and the contributing area scenarios.

On the right-hand side of Figs. 9–13, hydro-chemographevolution at the five time steps discussed in Fig. 6a is illus-trated, while on the left-hand side of Figs. 9–13, we can seethe progressive expanding contributing areas shown on thehydro-geomorphotype map. Specific observations are pro-vided in the figure captions and the corresponding values forthe increasing contributing area are listed in Table 4.

Figure 9 shows pre-event conditions, when only the base-flow and the decreasing groundwater ridging from previousevents were activated.

By plotting the S vs. Q data from Table 3 on a normalplot, we can follow the pattern of the progressive involve-ment of the runoff components as specific contributing areasin streamflow (Fig. 14).

In our case, a positive exponential function was obtainedfor each hydro-geomorphotype curve as shown in Fig. 14.This approach is similar to the calculations proposed by La-tron and Gallart (2007), but in this case the contributing areais calculated according to the baseflow component as well asthe other components related to hydro-geomorphotypes. Allthe curves have a general exponential pattern (Eq. 1):

S(t)= S0eaQ(t), (1)

where S(t) is the total contribution area at instant t , S0 theinitial contribution area, ea is a constant for a specific com-



Figure 10. Initial hydro-chemograph conditions just after the beginning of the storm event, with approximately Q= 350 L s−1, filled bluesquare, and approximately EC= 170 µS−1, filled green diamond, (b) scenarios corresponding to increasing groundwater ridging and initialsaturation excess contributions to streamflow. The first occurs along the riparian corridor, and the second at the apical transient channels justdownstream from the colluvial hollows, respectively.

Figure 11. (a) Progressive hydro-chemograph conditions after approximately 60 mm of rainfall, with approximately Q= 1000 L s−1, filledblue square, and approximately EC=120 µS−1, filled green diamond, (b) scenarios corresponding to a full saturation excess contribution tostreamflow along the riparian corridor and at transient channels within the colluvial hollows, respectively.

ponent considered, and Q(t) is the discharge at the time ofS(t).

Equation (1) can be re-written as

logS(t)= aQ(t)+ logS0. (2)

The riparian contribution trend is higher than the hollow andhillslope trends for a discharge from 50 to 1000 L s−1, butthe specific hollow and hillslope contributing areas progres-sively reach the same values as the riparian corridor in theevent of high discharge. In fact, a slight increase in the dis-charge from the riparian corridor was observed during the

event (a = 0.0012). On comparing the behavior of the hol-low and the hillslope, it seems that the hollow has a highercontributing area for lower discharge (from 50 to 600 L s−1)than the hillslope contributing area (Fig. 14). However, af-ter the discharge increased, the two hydro-geomorphotypesreached the same percentages as the contributing areas (A2in Table 4). A lower contribution originated from the nose,whose contributing area is not influenced by the dischargeuntil it reaches 1000 L s−1, after which it increases rapidly(a = 0.0041).



Figure 12. Advanced hydro-chemograph conditions, after approximately 80 mm of rainfall, with approximately Q= 1550 L s−1, filledblue square and approximately EC= 90 µS−1, filled green diamond, (b) scenarios corresponding to a full saturation excess contribution tostreamflow along the riparian corridor and the whole colluvial hollows, respectively.

Figure 13. Final hydro-chemograph conditions, after approximately 100 mm of rainfall inducing a peak discharge of approximatelyQ= 2400 L s−1, filled blue square, and about EC= 80 µS−1, filled green diamond, (b) corresponding both to full saturation excess contri-butions to streamflow from the riparian corridor and colluvial hollows, as well as to macropore (soil pipe and fracture) and excess infiltrationon noses and partially on the ridges, respectively.

Since 1970 authors have studied the relationships betweenthe contributing area and the baseflow discharge (Fig. 15a).In fact, Ambroise (1986), Myrabo (1986) and Latron (1990)found good relationships for some catchments in which theincreasing rate of the relative saturated area decreases withthe increase in a specific discharge.

Dunne et al. (1975) observed that an increase in the sat-urated area leads to an increase in the discharge. More re-cently the same relationship was observed by Martinez-Fernandez (2005). Latron and Gallarat (2007) found a lin-

ear relationship between the specific discharge and the extentof the contributing area. The authors believe that, unlike theother catchments, the linear trend could be reasonable sincethe saturation of the catchment under study is not conditionedby its topography.

For the Ciciriello catchment we examined the relation-ships between the percentage of the contributing area (A1in Table 4) and the specific discharge for each hydro-geomorphotype considered (Fig. 15b), and we believe thatthis trend is similar to that observed by Dunne et al. (1975).



Figure 14. Plot of the contributing area vs. discharge from data in Table 3.

Figure 15. (a) Relationship between the total extent of contributing saturated areas and the baseflow discharge in several small (less than10 km2) catchments (modified from Latron and Gallarat, 2007); (b) relationship between the contributing areas and the specific dischargefor each hydro-geomorphotype of the Ciciriello catchment.

When a low discharge occurs, the riparian corridorslowly contributes to the increasing discharge, and only forq = 100 L s−1 km−2 does this hydro-geomorphotype widenits contributing areas. Fig. 15 shows the increase in fastercontributing areas for hollow, hillslope and nose at specificdischarges q = 300, 200 and 100 L s−1 km−2, respectively.In this case these q values are considered as the q thresholdvalues for activating runoff mechanisms.

There is an evident anomaly regarding the riparian corri-dor, as it shows a percentage of contributing area over 100 %.In our opinion, this result is due to a DEM resolution and theriparian corridor must be carefully defined due to the possi-ble overlap with other hydro-geomorphotypes, especially thehollows. In Fig. 15 it is important to note the intersection be-tween all the curves at high q values. In our opinion, it showsthe interaction between all the runoff mechanisms occurringin the catchment during high-magnitude events before reach-ing the stream, as assumed by Cuomo and Guida (2016).

One of the most interesting results of this study is the ex-perimental confirmation of the pre-event water contributions

to streamflow by the rapid mobilization of the capillary fringeinducing groundwater-ridging mechanisms. This mechanismis still poorly understood despite the number of processesproposed and widespread acceptance (Cloke et al., 2006).Therefore, this case study can be considered the preliminaryidentification, recognition and quantification of the mecha-nisms at catchment scale.

5 Conclusion

According to the premises, the case study confirms the closelink between geomorphometry and hydrology, since geo-morphometry describes land surface quantitatively and landsurface is the spatial expression of the geomorphic pro-cesses acting in time and resulting in landforms that aregenerated by hydrological mechanisms mainly in temper-ate and Mediterranean eco-regions. This demonstrates howgeomorphometry can support hydrological analysis, by im-proving an interdisciplinary approach for future research de-velopments in connecting hydrology and geomorphology in



Table 4. Synoptic values of the Q-EC scenarios and contributing areas (S) values for each hydro-geomorphotype. Legend: q is the specificdischarge calculated for the catchment area; A1 is the ratio between the contributing area and the hydro-geomorphotype; A2 is the ratiobetween the contributing area and the catchment area.

Hydro- Scenario Discharge Specific Contributing A1 A2geomorphotype Q (L s−1) discharge S (km2)(HGT) q (L s−1 km−2)

Riparian corridor 1 50 16.47 0.057 0.143 0.0187042 300 98.79 0.102 0.257 0.0336383 600 197.58 0.157 0.396 0.0517834 1000 329.30 0.227 0.570 0.0746785 1900 625.68 0.576 1.448 0.189588

Hillslope 1 50 16.47 0.001 0.00157 0.0004862 300 98.79 0.003 0.00280 0.0008643 600 197.58 0.015 0.0155 0.0047834 1000 329.30 0.038 0.0400 0.0123655 1900 625.68 0.420 0.447 0.138233

Nose 1 50 16.47 0.00008 0.00012 2.47× 10−5

2 300 98.79 0.00020 0.00032 6.59× 10−5

3 600 197.58 0.001 0.00131 0.0002724 1000 329.30 0.015 0.0241 0.0050145 1900 625.68 0.119 0.188 0.039129

Hollow 1 50 16.47 0.007 0.00994 0.0022972 300 98.79 0.015 0.02151 0.0049723 600 197.58 0.050 0.07109 0.0164324 1000 329.30 0.093 0.13316 0.0307825 1900 625.68 0.450 0.64116 0.148211

Ridge 4 1000 329.30 0.00030 0.000814 9.88× 10−5

5 1900 625.68 0.005 0.0145 0.001762

data acquisition, mapping, analysis, modeling and general-purpose applications. This is the purpose of object-basedhydro-geomorphology, based on the methods for recogniz-ing and classifying distinctive hydro-objects within catch-ments, involving ontology and semantics of landforms andprocesses in significant catchment areas with distinctive hy-drological behavior and response in order to allow for theirobjective description, holistic analysis and inter-catchmentcomparison.

From this perspective, firstly by means of a recursivetraining-target approach (Guida et al., 2015), good agreementwas observed between expert-based geomorphological map-ping and an object-based geomorphometric map.

Therefore, by combining hydro-chemical analysis and anobject-based hydro-geomorphotype map, the variability ofthe contributing area during a significant storm event wasspatially modeled using the log values of the flow accumula-tion. In spite of its simplicity, a good agreement was observedbetween the spatial distribution of these parameters with theobserved contributing areas detected during the event by car-rying out direct surveys and taking surface and groundwaterdischarge measurements. The runoff components were de-termined for the storm event under study and specific runoff

discharge from each contributing hydro-geomorphotype wascalculated for each time step on the hydro-chemograph.

This study is the experimental confirmation of the roleand entity of pre-event water contributions to streamflow bythe rapid mobilization of the capillary fringe inducing thegroundwater-ridging mechanism in steep sloping terrains.This mechanism is still poorly understood despite the num-ber of processes proposed and widespread acceptance (Clokeet al., 2006); therefore, this case study can be considered asbeing a preliminary identification, recognition and quantifi-cation of this particular mechanism at catchment scale. Ac-cording to Marcus et al. (2004), this study emphasizes thefact that field-based process studies must “continue to formthe underpinning of hydrologic application in GIS’s” and“GIScience should not come at the expense of sacrificingfield-based studies of hydrologic processes and responses”.

This is an approach that can fill the gap between simplelumped hydrological models and sophisticated hydrologicaldistributed models based on numerous quantitative param-eters and expensive data collection. This kind of interdis-ciplinary and integrated approach can be applied to simi-lar, rainfall-dominated, forested no-karst catchments in theMediterranean eco-region by using an inexpensive, parsimo-



nious and effective methodology for water resource assess-ment and management as suggested by the Biosphere2 pro-gram. In fact, in UNESCO International Designation Areas(such as the Cilento Global Geopark), the Global GeoparkNetwork mission must guarantee hydro-geodiversity in com-pliance with the regulations laid down by the World HeritageCultural Landscape Management, and natural and managedecosystems (A1) must be safeguarded as established by theMAN AND BIOSPHERE program.

From this perspective, geomorphometry plays a funda-mental role in quantifying and objectively mapping hydro-geomorphological entities with hydrological relevance thatrequire monitoring and modeling in production, transferringand routing the flows between the various units in the catch-ments, as the base knowledge of progressive ecological plan-ning for the sustainable use of water resources and best prac-tices in land use improvements.

6 Data availability

The underlying research data can be publicly accessed andare available from the Supplement.

The Supplement related to this article is available onlineat doi:10.5194/hess-20-3493-2016-supplement.

Competing interests. The authors declare that they have no conflictof interest.

Acknowledgements. The paper was financed with ORSA155417University of Salerno research funds. The authors would like tothank Pasqualino Lovisi for taking field measurements, GiuseppeBenevento for his scientific support (CUGRI), Aniello Aloiaand Angelo De Vita, Cilento Global Geopark manager anddirector, for their institutional support, and Mauro Biafore for therainfall data obtained from the Campania region monitoring system.

Edited by: A. GuadagniniReviewed by: two anonymous referees

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