Characterisation of badlands and modelling of soil erosion in the Isábena watershed, NE

Spain

Charakterisierung und Modellierung von Bodenerosion auf Badlands im Isábena-Einzugsgebiet, NO-Spanien

Diplomarbeit

von: Katharina Joana Appel

Vorgelegt bei:

Prof. Dr. Christian Opp

Lehrstuhl für Hydrologie und Bodenkunde am Fachbereich Geographie der Universität Marburg

Dr. Eva Nora Müller am Institut für Geoökologie der Universität Potsdam

Oktober, 2006

2

Abstract Badlands are hillslopes of unconsolidated sediments with no or little vegetation that cannot be

used for agriculture and are characterized by their desert like appearance and their very high

soil erosion rates. This study assesses the feasibility of modelling soil erosion rates from

badlands of the Isabena Watershed in the Pre-Pyrenees, NE Spain using a field-work

integrated, process-based, semi-distributed modelling approach. For this purpose, a field

campaign was carried out to collect key geomorphological and soil hydraulic data for model

parameterization. Four typical badland formations were selected in the field as a function of

form, extent and internal gully system to enable a comparative study of badland processes.

The four badland types were then modelled using a soil-erosion routine based on the MUSLE

(Modified Universal Soil Loss Equation) approach coupled with the hydrological model

WASA. Altogether, three spatial discretizations of increasing complexity were applied.

The fieldwork results as well as the modelling results and a sensitivity analysis are presented

and discussed. The model version applied in this study is attached on a CD.

Zusammenfassung Badlands sind kahle Hänge aus Lockermaterial die extrem hohe Erosionsraten aufweisen.

Diese Arbeit befasst sich mit der Modellierung von Erosionsraten auf Badland-Hängen im

Isábena – Einzugsgebiet, einem Teileinzugsgebiet des Ebrobeckens, in Nordost - Spanien. Bei

dem angewandten Model WASA (Water Availability in Semi-arid Areas) handelt es sich um

ein räumlich verteiltes, prozessorientiertes hydrologisches Model welches mit der Modified

Universal Soil Loss Equation (MUSLE) kombiniert wurde um ereignisbezogene

Erosionsraten zu errechnen. Die Parametrisierung des Modells beruht auf einer Erhebung

relevanter Parameter wie Form, Größe, Exposition, Hangneigung, Vegetationsbedeckung,

Infiltrabilität und Korngrößenverteilung des Oberbodens, die in vier exemplarischen Badlands

im Untersuchungsgebiet durchgeführt wurde. Weitere Parameter wurden anhand von Werten

aus der Literatur ergänzt. Insgesamt wurden drei räumliche Diskretisierungen

unterschiedlicher Komplexität angewendet. Des Weiteren wurde eine Sensitivitätsanalyse

durchgeführt. Die Validierung der Modelergebnisse basiert auf Werten aus der Literatur in

Ermangelung von Messdaten aus dem Untersuchungsgebiet. Durchführung und Ergebnisse

der Feldarbeit sowie der Modellierung werden in der Arbeit vorgestellt und diskutiert. Die

Version des Modells die für diese Studie angewendet wurde ist in digitaler Form auf einer CD

beigefügt.

3

Acknowledgement I would like to thank Dr. Eva Nora Müller, who guided me through this work and introduced me to environmental modelling, for all the help and support I received during this last year. I would also like to thank Prof. Dr. Christian Opp for his critical comments and uncomplicated support. My gratitude goes to all the other members of the SESAM-Project, especially Till Francke for his patience in answering my questions. Many thanks to my friends Katrin Friedrich and Henrik von Wehrden for proofreading this work. And finally, I would like to thank my family for always supporting my decisions.

4

1. Introduction 8

2. Characterization of Badlands – a literature review 10

2.1. Definition and characteristics 10

2.1.1. Arid badlands 10

2.1.2. Semi-arid badlands 10

2.1.3. Humid badlands 11

2.2. Erosion processes 11

3. Study area 14

3.1. Localization 14

3.2. Climate 15

3.3. Hydrology 16

3.4. Geology and geomorphology 17

3.5. Soils 17

3.6. Vegetation and land use 18

3.7. Socioeconomic aspects 18

4. Methodology 19

4.1. Introduction to the modelling concepts 19

4.1.1. Introduction to the WASA-Model 19

4.1.2. Process representation 22 4.1.2.1. Interception model 22 4.1.2.2. Evapotranspiration model 22 4.1.2.3. Infiltration model 24 4.1.2.4. Soil water model 26 4.1.2.5. Lateral distribution of surface and subsurface flow between soil

vegetation components 26 4.1.2.6. Lateral distribution between TCs of surface and subsurface runoff 27

4.1.3. The Modified Universal Soil Loss Equation 29 4.1.3.1. Introduction to the Modified Universal Soil Loss Equation 29 4.1.3.2. Lateral distribution of sediment 32

4.2. Field work 34

4.2.1. Survey 34 4.2.1.1. Description 34 4.2.1.2. Results 35

4.2.2. Detail study 38 4.2.2.1. Description 38 4.2.2.2. Results 43

5

4.3. Parameterization of the WASA-Model 45

4.3.1. Spatial discretization 1 – the sensitivity analysis 45

4.3.2. Spatial discretization 2 and 3 46

4.3.3. Derivation of soil types and their hydraulic properties 49

4.3.4. Derivation of vegetation types and their hydraulic properties 50

4.3.5. Derivation of climate data 51

4.4. Summary 52

5. Results and discussion 53

5.1. Results of spatial discretization 1 53

5.1.1. Hydrological parameters 54

5.1.2. MUSLE Parameters 55

5.2. Results for spatial discretization 2 58

5.2.1. Results at the level of terrain components 58 5.2.1.1. Runoff generation at the level of terrain components 58 5.2.1.2. Sediment generation at the level of terrain components 59

5.2.2. Results at the level of subbasins 63 5.2.2.1. Runoff generation at the level of subbasins 63 5.2.2.2. Sediment generation at the level of subbasins 64

5.3. Results of spatial discretization 3 67

5.3.1. Runoff generation for spatial discretization 3 67

5.3.2. Sediment production for spatial discretization 3 at the level of terrain components 68

5.3.3. Sediment production for spatial discretization 3 at the level of subbasins 69

5.4. Validation of the model 71

6. Summary and conclusion 73

7. References 75

8. Appendix 81

8.1. Appendix 1: Results of pH measurements and electric conductivity measurements for the soil samples taken within the detail study 81

8.2. Appendix 2: vegetation parameters needed for parameterization of the WASA module 82

8.3. Appendix 3: Poster about the sensitivity analysis conducted. Presented at the European Geosciences Union General Assembley 2006 in Vienna 83

6

Tabels

Table 4-1: Routing and transport modes for sediment applied during this study ---------------------------34

Table 4-2: Main characteristics of the four Badlands --------------------------------------------------------------37

Table 4-3: Saturated hydraulic conductivity measured on badland slopes and derived by pedotransfer functions applying the ROSETTA model (Shaap, 2000). Sample ID is composed of the measurement number and the depth in which it was conducted. 0 = on natural soil surface, 30 = 30 cm below soil surface.------------------------------------------------------------------------------------------------------------------------40

Table 4-4: Results of chemical soil analysis -------------------------------------------------------------------------44

Table 4-5: Range of values as used for the sensitivity analysis-------------------------------------------------46

Table 4-6: Spatial discretization 2 for the four badlands ----------------------------------------------------------48

Table 4-7: Soil parameters required by the WASA model --------------------------------------------------------49

Table 4-8: Landcover classes used for parameterization in WASA and the corresponding classes of the PlaPaDa -----------------------------------------------------------------------------------------------------------------50

Table 4-9: Model input parameters for vegetation------------------------------------------------------------------51

Table 5-1: Mean annual runoff [m³/a] per TC ------------------------------------------------------------------------58

Table 5-2: Mean annual runoff yield [m³/m³a] per TC--------------------------------------------------------------58

Table 5-3: Size of each TC in m² ---------------------------------------------------------------------------------------59

Table 5-4: Slope length of terrain component in m-----------------------------------------------------------------59

Table 5-5: Mean annual sediment production [t/a] for the four badlands at the level of terrain components ------------------------------------------------------------------------------------------------------------------61

Table 5-6: Mean annual sediment yield [t/m²a] per TC and routing mode------------------------------------62

Table 5-7: Portion rill erosion of the sediment production of one TC ------------------------------------------62

Table 5-8: Runoff, runoff yield and discharge coefficient at the level of BL ----------------------------------63

Table 5-9: Sediment concentration in [g/l] at the level of BL for the four routing and transport modes63

Table 5-10: Mean annual sediment production [t/a] for each BL------------------------------------------------64

Table 5-11: Mean annual sediment yield [t/m²a] for each BL ----------------------------------------------------65

Table 5-12: Portion rill erosion of the sediment production of each BL----------------------------------------65

Table 5-13: Annual erosion rates in [t] of the four badlands for the years 1997 to 2005 modelled with the time series from the climate station of Pont de Suert ---------------------------------------------------------66

Table 5-14: Mean annual runoff [m³/a] at the level of terrain components and at the level of subbasin for spatial discretization 3-------------------------------------------------------------------------------------------------67

Table 5-15: Mean annual sediment production [t/a] per TC for spatial discretization 3 -------------------68

Table 5-16: Mean annual sediment yield [t/m²a] for the four BL with TC2 divided into two equal TCs 68

Table 5-17: difference in sediment production on TC2 for spatial discretization 2 and the sum of sediment production on TC2 and TC3 of spatial discretization 3 in %. Sediment production of TC2, spatial discretization 2 = 100%------------------------------------------------------------------------------------------69

Table 5-18: Mean annual sediment production [t/a] at the level of subbasins for spatial discretization 3----------------------------------------------------------------------------------------------------------------------------------69

Table 5-19: Differences in mean annual sediment production at the level of subbasin between spatial discretization 2 and 3 in %. Mean annual sediment production of spatial discretization 2 is set 100%70

Table 5-20: Comparison of results with data from literature (if not indicated, results in [t/ha a]) --------72

7

Figures

Figure 2-1 a and b: Coarse material in the channel of a badland probably deriving from mass movements-------------------------------------------------------------------------------------------------------------------13

Figure 3-1: Location of the Isábena cacthment whithin the Ebro basin; yellow area = Ebro basin; red area = Isábena catchment------------------------------------------------------------------------------------------------14

Figure 3-2: Location of the Isábena catchment within the catchment of the Barasona reservoir and of the Villacarli sub catchment within the Isábena catchment-------------------------------------------------------14

Figure 4-1: Hierarchical multi-scale disaggregation scheme for structuring river basins into modelling units in WASAFigure taken from Güntner (2002) with permission.---------------------------------------------21

Figure 4-2: Lateral distribution of runoff between soil vegetation components on one TC. (Figure taken from Güntner (2002); with permission) --------------------------------------------------------------------------------27

Figure 4-3: Lateral transfer of water and sediment from higher to lower TC. TC = Terrain component, the number indicates the position within the landscape unit. (1= highland, 2 = slope, 3 = lowland), taken from Güntner, 2002, with permission. -------------------------------------------------------------------------28

Figure 4-4: the four catchments of the badlands used for modelling spatial discretization 3 and 3. (Aerial picture form Sept. 1982, taken by I.G.N.) -------------------------------------------------------------------35

Figure 4-5: The Villacarli sub catchment with the four study sites ----------------------------------------------35

Figure 4-6: The four study sites. In green their outer limitation, in red the main channels. Aerial fotography by I.G.N. 1982. -----------------------------------------------------------------------------------------------37

Figure 4-7: Mosses and lichens found in the forest around BL2 on a north facing slope. ----------------37

Figure 4-8: Mean slope and standard deviation of the four exemplary badlands ---------------------------38

Figure 4-9: Comparison between mean vegetation cover of slopes -------------------------------------------38

Figure 4-10: Left: Aerial photography of BL1 taken in right: Aerial photography (M.A.P.A. 2005) showing Badland 1 which has been studied in detail. Crosses mark the sample localities where the soil samples were taken during the detail study --------------------------------------------------------------------43

Figure 4-11: Mean grain size distribution for the four geomorphological units ------------------------------44

Figure 4-12: Particle size distribution of soil samples taken on badlands ------------------------------------44

Figure 5-1: Results of the sensitivity analysis for important hydraulic parameters -------------------------56

Figure 5-2:Relative runoff contribution of each TC in % ----------------------------------------------------------59

Figure 5-3: Fraction of the mean annual sediment production of every TC on four badlands for different routing modes. Routing mode A = no transfer between TCs, no transport limit; B= routing between TCs, no transport limit; C= routing between TCs, transport capacity limit according to Govers (1990) and Morgan et al. 1998 (, D=routing between TCs, transport limit maximal erosion according to the MUSLE equation. ------------------------------------------------------------------------------------------------------62

Figure 5-4: Mean annual sediment production [t/a] on each Badland for the four routing modes ------65

Figure 5-5: Event erosion rates (dots) and relative cumulative erosion (red line) on BL3 with routing mode C for climate data of the year 2000 ----------------------------------------------------------------------------66

Figure 5-6: Mean annual sediment production at the level of badlands for spatial discretization 3. Full = spatial discretization 2; stripes = spatial discretization 3----------------------------------------------------------70

8

1. Introduction Badlands are hillslopes of unconsolidated sediments. Sparse vegetation cover and steep slopes

lead to high erosion rates. In the study area, the catchment of the Barasona reservoir in NE

Spain, previous studies have identified badlands as the main sediment source (Valero-Garcés

et al. 1999). The Barasona reservoir supplies the region with drinking water, electricity and

water for irrigation purposes. The high amount of sediment input coming from the badlands

leads to a severe reduction of the storage capacity of the reservoir. Especially in dry years, the

decrease in volume leads to supply shortages and socioeconomic problems.

This study aims at gaining information on sediment production and transport rates on badland

hillslopes in order to derive erosion rates for exemplary hillslopes by means of soil erosion

modelling. For this purpose, the hydrological model WASA (Water Availability in Semi-arid

Areas) developed by Güntner (2002) was combined with the Universal Soil Loss Equation

(MUSLE) (Williams, 1975). WASA is a deterministic model composed of process-based

conceptual submodels applied to a semi distributed spatial discretization.

The erosion routine based on the MUSLE is an empirical equation that integrates over soil

erosion processes on the hillslope scale. It was developed for the estimation of soil loss on

agricultural crop land. Spatial discretization applied in WASA to account for spatial

variability of parameters is an opposed concept. Furthermore, a mathematical problem arises

due to the fact, that the area being considered is not a linear factor in the MUSLE. Its result,

sediment production, is scaled linearly from subordinated spatial units to a superordinate

spatial unit. The same accounts for slope length. Within this study, it is tested if the MUSLE

approach is able to reproduce the extreme erosion rates on the badland slopes and whether or

not it can be combined with the spatial discretization concept applied in WASA.

For parameterization of the model, two weeks of field work were conducted in order to

characterize the badlands in the study area and to measure and estimate soil hydraulic

characteristics on the badlands slopes. Parameters that could not be determined by

measurements were derived by pedotransfer functions, intersection of thematic maps in a

geographic information system or endorsed by values from literature.

Altogether, three spatial discretizations were applied: a simple one to conduct a sensitivity

analysis for estimating the range within the result would vary and to identify the importance

of relevant parameters to modelling results. The second spatial discretization applied is the

one best representing the four study sites as characterized during the field work.

The third one differs from the second one only in the number of spatial units. It was

performed to see how spatial discretization affects sediment production.

The results of these three scenarios are analyzed at different spatial levels. Since only

preliminary results of measurements of erosion rates are available for the study area,

modelling results are validated by the comparison with values from literature.

9

Recapitulating, the four objectives of this study are a) to estimate required parameterization

data from badlands in the field, b) parameterize the hydrological and soil erosion model

considering different spatial discretizations, c) carry out a sensitivity analysis and d) test and

discuss the outcome of the models.

10

2. Characterization of Badlands – a literature review

2.1. Definition and characteristics

Badlands are hillslopes of unconsolidated sediments or poorly cemented bedrock as marls,

mudstone or shales. Vegetation cover on badlands is sparse. Slopes are dissected by a dense

drainage network within v-shaped valleys (Bryan and Yair, 1982). Dominant processes

contributing to soil erosion on the hillslopes are rainsplash and overland flow as well as

piping and mass movements. Badlands are different from gullies for they include slopes and

divides (Nogueras et al. 2000) and are not only linear, discrete phenomena but can compose

whole landscapes. They usually form a mosaic of stable areas with soil and vegetation cover

and unstable, bare areas where bedrock is only covered by a thin regolith layer. Badland

development is bound to linear incision and gully erosion (Gallart et al., 2002b). The main

factor controlling badland formation is the character of the bedrock. Usually, a protective

caprock has been removed and a softer rock less resistant to erosion surfaces. Tectonics and

uplift are sometimes involved in this process but also land use changes may trigger the

formation of badlands. They are considered to be characteristic of dryland areas but they also

occur in wetter climate with high intensity storm events such as in the Mediterranean.

Gallart et al. (2002a) classify badlands according to the climate, they occur in and the

corresponding processes dominating on the hillslopes.

2.1.1. Arid badlands

Arid badlands occur in areas with about 200 mm of annual precipitation. Vegetation cover is

low due to the shortage of water. These badlands are exclusively controlled by the

characteristics of bedrock and regolith. Erosion rates are lower than expected (Wise et al.,

1982). Erosion only occurs during high intensity rainfall events, which are scarce. The slopes

are thus actually quite stable. Missing vegetation is ascribed to the low potential of seedling

survival. These landscapes are thus usually very old. The best known example for this type

are the badlands in the Northern Negev, Israel (Yair et al., 1980).

2.1.2. Semi-arid badlands

In semi-arid climate, where rainfall lies between 200 and 700 mm/a, vegetation cover on the

badlands is usually higher. Limiting factor to vegetation growth is still water availability as

suggests the asymmetric mosaic of dense vegetation cover of up to 100% on north facing

slopes and almost no plant growth on the south facing slopes due to higher insolation (Solé et

al. 1997; Cantón, 1999). Semi-arid badlands are also usually quite old and have experienced

phases of stability and instability (Calvo et al. 1991; Nogueras et al. 2000). Wise et al. (1989)

11

proved that archaeological structures had survived 4000 year on the surface of badland

landscapes in south east Spain.

Their initialisation is mostly natural. Once initiated though, they are highly susceptible to

human impact as grazing, agriculture and especially the concentration of runoff in artificial

ditches which is comparable to the creation of a gully which can easily incise in the highly

erodible regolith and soft bedrock. Examples for this type of badland can be found in

southeastern Spain and southern Italy (Bouma and Imeson, 2000; Cantón et al. 2001).

2.1.3. Humid badlands

Humid badland are found in mountainous areas as the Southern Alps (Draix, Haute Provence,

France) and the Pyrenees (Vallcebre, Catalonia and Isábena, Aragon, both in NE Spain).

Mean annual precipitation is around 700 mm or higher. Rainfall mostly occurs in form of high

intensity storm events. Vegetation growth is no longer limited by water availability but by the

high erosion rates and freezing on north exposed slopes (Regüés et al., 2002). In general,

badlands of this type are younger than the others (Clotet et al. 1988). Their formation is

triggered by mass movements that may be caused by degradation of vegetation due to human

impact. The badlands studied in this thesis belong to this type considering that mean annual

rainfall is 767 mm/a (see section 3.2). The distribution of vegetation would rather fit to the

semi-arid type though. South facing slopes within the catchment considered show a less dense

vegetation cover than the north facing slopes that carry coniferous and mixed forests (see

section 4.2.1).

2.2. Erosion processes

Parameters influencing erosion on badlands are the degree and agent of cementation of the

bedrock and particle size distribution. Soils or weathering material developing from marls,

shales or mudrock are usually rich in silt and clay but have low sand content. These

sedimentary rocks have a high porosity volume of about 10 % (Gallart et al. 2002b). The

solution of cementing agents as there are salts, sulphates or carbonates leads to enhancement

of the pores and an increase in pore volume to up to 50 %. Wetting and drying circles causing

swelling and shrinking of three-layer clays and solubilization and cristallization of salts

further destabilize the weathered bedrock and produce a layer of regolith with a characteristic

popcorn structure (Cantón et al. 2001). In areas where freezing occurs, freezing - thawing

cycles become the dominant weathering process (Gallart et al. 2000b).

Processes leading to the destruction of aggregates and particle detachment are first of all

splash erosion and slaking. Splash erosion is caused by the impact of raindrops striking the

soil surface. It increases with rainfall intensity (Cerdá, 1997). Slaking is the bursting of

aggregates occurring when aggregates wet fast and air gets trapped in the interior. Soil

resistance against these processes are related to soil cover and intrinsic characteristics as clay

12

content and content of organic matter which contribute to aggregate stability. Resistance of

regoliths against erosion on badlands is low due to the low content in organic matter (lees

than 1%). If the sodium content in the soil is high, clays become dispersive and highly

erodible. Furthermore, their dispersion leads to sealing of pores and even crusting which

reduces infiltrability and increases runoff. Cracks on the other hand enable fast infiltration at

least at the beginning of an event for they might close due to swelling if the rainfall event lasts

long enough. In areas where freezing occurs, regolith characteristics show a seasonality which

affects infiltration and runoff generation (Gallart et al, 2000a). The popcorn structure

produced in winter by freezing and thawing is destroyed and compacted over the year due to

splash erosion and slaking (Gallart et al. 2002a). The compacted soil is less permeable and

leads to higher runoff production. Runoff generation thus depends on lithology, antecedent

soil moisture conditions, duration of storms and the season.

Runoff occurs as surface runoff in rills and channels or as sheetflow but also as subsurface

runoff through pipes. Rills are nonpermanent microforms on the badland slopes and play an

important role in water and sediment conveyance.

Pipes emerge from the convergence of subsurface cracks or macropores that rapidly enlarge.

Considerable amounts of water can run through the pipes and lead to subsurface erosion.

Piping is thus a self-energizing process. The collapse of pipes is often the initialization of a

channel (Gutierrez et al., 1988). The occurrence of pipes seems to be bound to high levels of

sodium in the soil for it leads to diflocculation of clay particles which become more erodibilty

(Romero Díaz et al., 2006).

Regüés and Gallart (2004) found out, that dominant erosion processes on humid badlands

have a seasonal pattern. During winter, physical weathering produces a thin layer of regolith

of maximal 15 mm depth. During the high intensity rainfall events of spring and summer, soil

detachment and transport from the hill into the channel takes place whereas for sediment

transport within the channel into the river network longterm rainfall producing persistent

channel flow is necessary as it occurs in autumn.

Mass movements are also an important process in the formation of badlands. They are caused

by instabilization of the regolith or even bedrock and reach from smaller mudflows to larger

slumps. Huge amounts of sediment and rock are mobilized during such events (see Figure

2-1).

Badlands appear to have extremely high erosion rates. This is valid at the scale of single storm

events but mean annual rates are not necessarily high in arid to semi-arid badlands with high

interannual variability in rainfall. With increasing runoff though, erosion rates grow rapidly

(Gallart et al. 2002a). On the other hand, increasing runoff leads to more vegetation cover and

thus better protection from erosion (Nogueras et al. 2000). Cantón (1999) state that erosion

rates in humid badlands can be limited by weathering rates and the amount of disposable

sediment as show the bedrock outcrops in channels which are depleted completely almost

every year, at least in humid badlands as studied here (Clotet et al. 1988; Regüés et al 1995) (.

Event erosion rates area transport limited though (Campbell, 1989).

13

Erosion rates found in literature are highly variable due to the application of different methods

in different climates. For the humid badlands in Vallcebre, NE Spain, values between 120 t/ha

a (Gallart et al. 2005) and 550 t/ha a (Clotet Perernau et al. 1988) were recorded and ground

lowering between 7.3 and 37 mm / a (Clotet and Gallart, 1986).

Figure 2-1 a and b: Coarse material in the channel of a badland probably deriving from mass movements

14

3. Study area

3.1. Localization

The study area is a sub catchment of the Isábena watershed which is located in the Central

Pyrenees in north eastern Spain (Figure 3-1). The Isábena catchment is situated within the

autonomous community of Aragón in the province of Huesca. It is part of the Ebro basin and

extends over 445 km² which is about 0.48 % of the Ebro watershed. Mean annual runoff is 5.7

m³/s which is equivalent to 1.34 % of the total runoff of the Ebro (Verdú Arnal, 2003).

Altitude ranges between 650 m a.s.l. at its mouth and 2720 m a.s.l. at the Pico de Gallinero in

the north. The course of the Isábena river from the spring to the mouth is about 50 km long

(Gayúbar, 2000). After the confluence with the Ésera river (see Figure 3-1) a dam forms the

Barasona reservoir of 92 x 106 m3 which serves the generation of electricity, supplies

drinking water and water for irrigation purposes.

The very inhomogeneous morphology of the catchment forms various sub catchments (see

Figure 3-2), one of them being the Arroyo de Villacarli (see Figure 3-2). In this transversal

valley, the ephemeral Villacarli river carves into a layer of soft, gray marls on which badland

development occurs. Four exemplary badlands within the Villacarly sub catchment have been

investigated.

#*

#*

#*

#*#*

#*

#*

ISABENA

ES

ER

A

Cabecera

Villacarli

Ceguera

BARASONA

Corva

Seira

Campo

Escales

Serraduy

Las Paules

Pont de Suert

275000

275000

290000

290000

305000

305000

320000

320000

4666

000

4666

000

4684

000

4684

000

4702

000

4702

000

4720

000

4720

000

rivers

Isábena catchment

Villacarli catchment

#* climate stationUTM 31 N

�

Figure 3-1: Location of the Isábena cacthment whithin the Ebro basin; yellow area = Ebro basin; red area = Isábena catchment

Figure 3-2: Location of the Isábena catchment within the catchment of the Barasona reservoir and of the Villacarli sub catchment within the Isábena catchment

15

3.2. Climate

The mountainous part of Aragon

mediates between two different

climatic regimes: the oceanic,

which dominates the Occidental

Pyrenees and the mediterranean

climate in its continental variant,

which dominates the Central

Pyrenees. The Isábena catchment

is dominated by the latter. Main

characteristics of this climate

regime are ample thermal

contrasts between the dry winter

with high insolation and the hot

summer with rainfall occurring in

high intensity storm events. The

Isábena catchment itself can

again be sub divided in a warmer

and drier part reaching from the

Barasona reservoir up to the

Turbon, the highest peak in the

Villacarli sub catchment with

2373 m a.s.l (Figure 3-2). Mean

annual temperature varies

between 11 – 14 °C.

The northern part is dominated by a colder and wetter climate regime with a mean annual

temperature of 9 – 11°C (Verdú et al. 2002). Precipitation varies between 450 and 1300 mm

and increases with altitude. The mean annual precipitation for the whole catchment amounts

to 767 mm. High rainfall intensities occur during spring and autumn. July is the month of less

precipitation.

Due to exposition, altitude and the heterogeneity of the catchment, great climatic contrasts can

be found within a short distance gradient. According to Verdú (2002), the highest

precipitation occurs in the uppermost sub catchment, the Cabecera catchment (see Figure

3-2). However, most storm events were recorded in the sub catchments of Villacarli and

Carrasquero (see Figure 3-2 and Table 3-3 ).

The highest temperatures occur in July and August while December and January are the

coldest months (Table 3-1). Frost can occur between September and May with the highest

probability in December and February. For mean monthly and annual values for temperature

and precipitation see Table 3-1 and Table 3-2, respectively.

Figure 3-3: Topographic map of the Isábena catchment.

ISABENA

16

Table 3-1: Mean monthly and annual temperatures for five stations in or near the study area in °C (Ninot et al., 1993)

Station J F M A M J J A S O N D annual

Campo 3.3 4.5 8.3 10.6 15.1 18.3 21.4 21.1 18.2 13.4 7 4 12.1

Seira 1.7 3.4 6.6 9.3 13.4 17 20 20.2 16.4 11.4 6.3 3 10.7

Serraduy 3.4 3.9 5.4 9.5 18.6 18.3 22.6 22.4 18.9 10.6 6.7 4.1 12

Pont de Suert

1.9 2.6 6.1 9 12.8 16.4 18.9 19 15.8 11.1 5.6 2.9 10.2

Escales 5.7 6.2 8 11 14.7 18.6 22.6 21.2 18.3 15 8.6 4.9 12.9

Table 3-2: Mean monthly and annual precipitation for five stations in or near the study area in mm (Ninot et al., 1993)

Station J F M A M J J A S O N D annual

Campo 78.9 65 71.6 84.2 128 97.7 62.1 91 72.7 68 63 88 970.2

Seira 75.4 58.8 66.3 105.2 128.2 108.6 71.8 97 91.5 94.8 81.2 93.6 1072.4

Pont de Suert

41 48 78 65 99 76 53 97 82 69 78 70 856

Escales 43 52 47 66 87 53 37 65 68 63 118 34 733

Table 3-3: Rainfall intensity and total precipitation by pluviograph for all storm events during. (Verdú, 2002)

Pluviograph n° of storm events

mean intensity [mm/h]

Corva (upper catchment)

8 6.5 (+- 4.0)

Las Paúles (altiplano) 9 4.16 (+- 2.0) Serraduy (central catchment)

13 5.26 (+-3.4)

3.3. Hydrology

The Isábena river is characterized by a pluvionival hydrological regime (Verdú et al., 2002)

with runoff peaks in late spring / early summer initiated by the snow melt in the upper

reaches. Minima are reached in summer (August and September) with a slight delay to the

season with less rain (July-August). The maxima occur in autumn triggered by storm events

with mean runoff rates of 40 m³/s. Mean annual runoff is 5.7 m³/s with a standard deviation of

2.4 m²/s indicating the high interannual variability (Verdú, 2002). According to Verdú (2002),

the Cabecera catchment attributes about 60% and the Villacarli, although being an ephemeral

stream, attributes up to 20% of runoff during flood events. During storm induced flood

events, the maximum runoff lowers to the centre of the catchment to the Villacarli and the

Carrasquero catchment (see Figure 3-2).

17

3.4. Geology and geomorphology

The Isábena headwaters are cut into

granitic and metamorphic, siciciclastic or

carbonate rocks from the Paleozoic era,

which form the Axial Pyrenees. The

Isábena runs in a narrow gorge here. To

the south follows a geosynclinal mainly

filled with calcareous Cretaceous and

Palogene sediments (see Figure 3-2).

During the alpine orogeny, these have

been pressed and folded against the Axial

Zone. The resulting ranges are called the

“Internal Ranges” (Sierras Interiores). In these erodible materials as marls and sandstones,

transverse valleys could develop such as the Arroyo de Villacarli or the Carrascquero

Catchment (see Figure 3-2). In case of the Arroyo de Villacarli, a sandstone layer once

covered the Mesozoic marls. Wherever this sandstone layer has been removed, the highly

erodible marls surface and badland formation takes place. South of the Internal ranges extends

the Intermediate Depression, a basin filled with synorogene flysch, marls, and Oligocene

conglomerates. These lowlands with less inclination are used for agriculture. The southern

limit of the catchment is formed by the External ranges, which are the transition to the Ebro

basin. In some parts, they form mountain chains of over 1700 m and in other part already have

characteristics of foothills (Lautensach, 1964). They are mainly formed of cretaceous

limestone in combination with clays and conglomerates from the Cenozoic era. The Isábena

flows between two morphological units: the intra-pyrenean depression formed by the Vall de

Lierp, the Corredor de Merli and the Sierra del Chordal on the right side and the Sierra de Sis

on the left side.

3.5. Soils

The soils in the Isábena catchment are characterized by their poor development (Martinez-

Cassasnovas and Poch, 1998). Mainly Xerorthents, Litic Xerorthents on calcic slopes with

little soil depth (A-R-profile) can be found. In more stable positions and on less resistant

bedrock, Tipic Xerorthents with a A-C or A-Bw-C profiles develope. They are mostly basic

soils with a silty- loamy to sandy-loamy structure. With 2% and less, the content of organic

matter is low. Soils are well drained but have low water retention capacity due to low soil

depth. The most eroded sites are, besides the badlands, the agricultural croplands which are

often situated on steep hillslopes.

Figure 3-4: Lithology of the Ésera-Isábena-catchment (Valero-Garcés et al., 1999)

18

3.6. Vegetation and land use

The potential natural vegetation for this area can be presumed as „Carrasca“, forests of

Quercus Ilex ssp. Ballota (Ninot et al. 1993). Currently, this vegetation type only has a small,

disperse extension. The main part of the catchment is covered by more degraded vegetation

forms dominated by shrubs and herbs.

The northern part is mainly covered by Pinus sylvestris forests from 600 m. a. s. l. From 1600

up to 2300 m a.s.l. Pinus unicata dominates (Verdú et al., 2003). Deciduous woodlands are

formed by Quercus faginea, Betula pendula and Fraxinus sp.. These stand form mixed forests

accompanied by Pinus unicata. Within the whole catchment, grass communities become more

and more important as grazing ground for livestock, mainly sheep. In the southern part of the

catchment where the valley is wider and expanded floodplains have developed, agriculture

activities are the dominant land use. The main crops are wheat (Triticum aestivum), barley

(Hordeum vulgare) and sunflowers (Helianthus annuus). Permanent crops are mainly

almonds (Prunuc dulcis) and olives (Olea europea).

3.7. Socioeconomic aspects

The administrative district of Ribagorza has suffered great population losses during the 20th

century. From 13.6 inhabitants / km² at the beginning of the century, the number decreased to

5.5 inhabitants /km² in 1998. Nowadays, over 60% of the villages have less then 200

inhabitants. Only two villages have over 1000 inhabitants. The main income derives from

agro-pastoral activities; industry is of little importance. The largest proportion of the income

derived by agriculture is obtained by livestock, mainly sheep, followed by cattle and pork

(Verdú, 2003). Agricultural crops become more important in the area downstream below the

Barasona reservoir. The dam was constructed in 1932 for irrigation purposes and power

generation. It has a capacity of 92*106 m³, surface of 692 ha and a maximum depth of 60m.

The average depth is 16.5 m. The capacity of the power house is 26 MW. A channel

originating in the reservoir (Aragón and Cataluña channel) supplies 104.850 ha of cropland

with irrigation water during March to October. In former times, the reservoir was flushed

every year in order to clean out the sediment. Since the 1950s, the reservoir has no longer

been flushed regularly. As a consequence, in 1995 a layer of sediments with a maximum

thickness of 25 m near the dam (Valero-Garcés et al., 1999) occupying 16-18 km³ had

reduced the storage volume of the basin about one third. During three years, the reservoir had

to be drained completely after the irrigation period and the sediments near the dam had to be

dredged to change the plugged bottom outlets. However, most of the sediment remained in the

basin and flushing is not performed regularly (Valero-Garcés et al., 1999). The storage

volume of the basin thus constantly decreases.

19

4. Methodology This study aims at modelling erosion by runoff on badland hillslopes applying the WASA-

model. A new routine calculating sediment production based on the MUSLE (Modified

universal soil loss equation) has been introduced to the hydrological model and is tested for

the first time in this study. During a field trip, badlands in the study area were characterized

and relevant model input parameters surveyed. The remaining parameters were derived

through the application of other models and evaluation of literature. Based on these values, a

sensitivity analysis was conducted in order to identify the range within results could probably

vary. Furthermore, one parameterization was composed which is assumed to be the best

spatial representation of the modelled slopes. That configuration was applied in two variances

in order to quantify the effect of submodel disagreements between the spatial discretization of

WASA and the MUSLE approach.

The chapter is divided into an introduction to the modelling approaches, the description of the

field work and its results which are then applied for the parameterization of the model.

Modelling results will be presented and discussed in chapter 5.

4.1. Introduction to the modelling concepts

4.1.1. Introduction to the WASA-Model

WASA stands for “Model of Water Availability in Semi- Arid Environments”. It is a

hydrological model developed by Güntner (2002) for the quantification of water availability

in semi-arid environments in view of environmental changes. WASA is a deterministic model

composed of process-based conceptual approaches applied to a semi distributed spatial

discretization. The model requires a minimum of calibration of parameters which makes it

useful for impact studies in areas with low data availability.

The model is process-based in the sense that all water fluxes are represented by realistic

hydrological processes. It is conceptual though in the sense that process representation is

based on approximating and simplifying assumptions. Nevertheless WASA can be called a

physically based model for its parameters can be derived from measurable physical

characteristics of the study area (Güntner, 2002). Hydrological processes represented in

WASA by individual routines are interception, evaporation, infiltration, surface and

subsurface runoff, transpiration and ground water recharge as well as lateral distribution of

water between the different spatial units (for details see Güntner, 2002). Furthermore, WASA

20

can account for reservoirs storage, river flow and water use within a catchment. These latter

processes are not of importance for this study and are thus not described here.

Considering the spatial discretization applied in WASA, it is a semi distributed model. The

catchment is represented by discrete georeferenced spatial units down to a level below which

the spatial units are no longer georeferenced but only represented by their fraction of area

within the superior spatial unit (see Figure 4-6). The watershed being modelled is divided into

subbasins (further on referred to as SB) which represent sub catchments. Within the sub

catchment areas with a similar toposequence, lithology and soil characteristics form a

landscape unit (further on referred to as LU). Subbasins and landscape units are

georeferenced areas within the catchment being modelled. Landscape units are composed of

terrain components (further on referred to as TC) that have similar slope angles and soil

associations. Down to this scale, a distributed structure is followed. The lowest level of spatial

discretization is formed by the soil-vegetation components which result from the intersection

of soil types with landcover classes. These are no longer georeferenced but only given by

their spatial fraction of the TC they occupy. That makes WASA a semi-distributed model.

The characteristics of each soil vegetation component are given by a soil profile representing

the corresponding soil type and vegetation cover. At this level, WASA applies a lumped

concept, meaning that spatial variability is no longer represented within this lowest spatial

unit.

What makes WASA especially applicable to semi-arid areas are, among others, the two layer

evapotranspiration model applied, which accounts for energy transfer at the soil surface and

thus soil evaporation (see 4.1.2.2.). Furthermore, it is capable of displaying the high fraction

of infiltration excess surface runoff (Hortonian runoff), which is typical for runoff generated

by high intensity storm events. WASA offers the possibility to account for changes in

hydrology induced by the seasonality of plan growth due to dry and rainy seaond.

Although being applied to a rather sub-humid than semi-arid area here, WASA should still be

able to display the relevant parameters which are similar from the hydrologically point of

view. Rainfall occurs mainly in form of storm events. Seasonality is also given here with high

rainfall in spring and autumn and lower rainfall in summer and winter. Seasonality of

vegetation parameters is rather induced by changes in temperature than due to the lack of

water though. WASA permits adjusting the according file based on the relevant climate data

(see below, section 4.3.5). Vegetation cover is sparse in large parts of the Isábena catchment

and especially on the badland slopes represented in this study. Evaporation from the soil

surface thus is also an important parameter.

21

Level Type and criteria of delineation Process working on this level

1. Subbasin (SB)

• geographically referenced polygon of a sub catchment

• wherever possible delineated with the Arc Hydro tool box, or based on aerial fotographies and field measurements

• runoff routing

2. Landscape unit (LU)

geographically referenced polygon with similarities in: • major landforms

• general lithology

• soil association

• toposequence

• modelling unit with similar characteristics referring to lateral processes and similarity of subscale variability in vertical processes

• composed of 1 – 3 terrain components

• runoff response of al landscape units are summed up to give total runoff of SB

3. Terrain component (TC)

fraction of area of LU (no geographic reference) with similarities in:

• slope gradient

• slope position within toposequence

• soil association

• lateral transfer of surface and subsurface flow between TCs along the hillslope (from highlands to lowlands)

• reinfiltration and exfiltration (return flow) in TC with lower topographic position

• sediment production and lateral transfer between TCs as with runoff

4. Soil vegetation component (SVC)

fraction of area of TC characterized by specific combinations of • soils

• vegetation

• Represents the variability of soil hydraulic properties within a TC. (partial area approach for saturation-access surface runoff)

• lateral transfer of surface ad subsurface runoff of TC among all svc

5. Profile

representative profile of soil vegetation component

• calculation of water balance I the profile of each soil-vegetation component

• determination of vertical and lateral water fluxes for individual horizons

Figure 4-1: Hierarchical multi-scale disaggregation scheme for structuring river basins into modelling units in WASAFigure taken from Güntner (2002) with permission.

22

4.1.2. Process representation

In the following section, sub models of WASA representing the hydrological processes are

described roughly to give an overview. For detailed description see Güntner (2002). It will be

limited to the processes on the hillslope which are relevant for this study and omit processes

dealing with deep groundwater recharge, processes in the river network or in reservoirs.

4.1.2.1. Interception model

The interception model integrated in WASA is a simple bucket approach (see Eq. 4-1). The

interception storage depending on the leaf area index (LAI) and a given maximum height for

the water film on the leaves. For this study the maximum height is set to 0.03 mm. Rainfall

over the canopy is added to this storage until it is filled. Excess water is assumed to reach the

soil surface and thus added to the infiltration routine. The maximum height of the water film

might appear high but this accounts for the fact that currently, daily time steps are calculated

underestimating processes as evaporation and refilling over the day.

Eq. 4-1: Simple bucket approach for the representation of interception

111tt EPII −−−−++++==== −−−−

It Water in interception storage at time step t [mm]

P Precipitation [mm]

PI Intercepted precipitation [mm]

Ei Evaporation from interception storage [mm]

4.1.2.2. Evapotranspiration model

Evapration from open water bodies and the interception storage is described by a simplified

form of the classical Penman – Monteith approach (Penman (1948); Monteith (1965)) (see

Eq. 4-2).

For the representation of total evapotranspiration the two-layer approach from Shuttleworth &

Wallace (1985) is applied, consisting of a model for plant transpiration and evaporation from

soil surface (only applied to the upper most horizon) (see Eq. 4-3 to Eq. 4-5). These

components are determined separately but interrelated by the vapour pressure deficit of the

air. Evapotranspiration is characterized by nonlinearity during day and night. To consider this

phenomenon, a concept of Schulla (1997) is applied to the input data on daily basis dividing it

into a daytime and a nighttime period. Currently, this is simply done by dividing it into two

twelve hour time steps, an assumption that is valid for the study area near the equator treated

23

by Güntner (2002). For the study area in Spain, a dynamic approach should be integrated to

represent changing day length over the year. Energy input as driving force to this system is

net radiation in [W/m²]. For more detailed description of parameters see Güntner (2002), pp.

38 – 40.

Eq. 4-2: Simplified Penman-Monteith approach for simulating evaporation from the interception storage and open water bodies (Penman, 1948; Monteith, 1965)

(((( ))))

++++++++

++++====

aa

cs

aap

PMr/r1

r/DcAtE

λλλλ∆∆∆∆

ρρρρ∆∆∆∆

λλλλ

Epm Evapotranspiration [m / time step]

t Number of seonds per time step [-]

λ Latent heat of vaporization of water [J / kg]

∆ Gradient of the saturated vapour pressure curve

[hPa / K]

A Available energy [J / ms] = [W / m²]

ρ Density of air [kg / m³]

dp Specific heat of moist air [J / kg K]

D Vapour pressure deficit at referece level

[hPa]

aar

Aerodynamic resistance [s / m]

csr

Canopy resistance [s / m]

γ Psychrometric constant [hPa / K]

Eq. 4-3: Two-layer approach for the estimation of evapotranspiration after Shuttleworth and Wallace (1985)

ST EEE ++++====

Eq. 4-4: Plant transpiration

(((( ))))

(((( ))))

++++++++

++++−−−−====

ca

cs

camps

Tr/r1

r/DcAAtE

γγγγ∆∆∆∆

ρρρρ∆∆∆∆

λλλλ

Eq. 4-5: Evporation from soil

(((( ))))

++++++++

++++====

sa

ss

samps

Sr/r1

r/DcAtE

γγγγ∆∆∆∆

ρρρρ∆∆∆∆

λλλλ

24

ET Plant transpiration [mm]

ES Soil evaporation [mm]

AS Available energy at the soil surface [J/m s]

Dm Vapour pressure deficit inside the canopy

[hPa]

car Bulk boundary layer resistance

which controls the transfer between leaf surface and hypothetical mean canopy air stream at height zm

[s/m]

sar Aerodynamic resistance controlling

transfer between soil surface and zm

[s/m]

ssr Soil surface resistance [s/m]

4.1.2.3. Infiltration model

The infiltration module is based on an adaptation of the Green-Ampt approach by Peschke

(1977, 1987) and Schulla (1997) (see Güntner, 2002; pp. 41). The routine is applied at the

level of soil vegetation components. Input to the routine is precipitation (P) reduced by

interception (PI). This amount is increased by lateral surface inflow from higher TCs (Rs,TC)

and other soil vegetation components within the TC currently computed (Rs,SVC). To capture

runoff from SVCs within the same TC, two iterations of Eq. 4-6 have to be conducted. During

the first run, Rs,SVC is set to 0. Possibel runoff is then redistributed between all SVCs.

Afterwards real infiltration for all SVCs is calculated.

Eq. 4-6: SVC,sTC,sIF RRPPR ++++++++−−−−==== (all in mm/∆t)

The infiltration procedure starts by calculating the moment of time (ts,i) when the input rate

(RF) is higher than the saturated hydraulic conductivity (ks,i). ts,i is calculated according to Eq.

4-7). This is the moment, when saturation at the soil surface is reached. Is ts,i smaller than the

modelling time step, the whole rainfall infiltrates into the soil. Saturation at soil surface is not

reached. From ts,i on, infiltration rate decreases approaching its limiting value ks,i at the end of

the modelling time step (see Eq. 4-11 and Eq. 4-12). If I = 1 (uppermost horizon) and RF

exceeds Fi, the difference between both parameters describes the amount of infiltration excess

runoff, or Hortonian Runoff (Horton, 1933).

The routine ends here. Only if the depth of the wetting front suction (ds,i) reaches below the

lower limit of horizon i, or the entire refillable porosity of horizon i had filled up, the next

lower horizon would be taken into account. In both cases, the horizon would be saturated

within ts,i but following Eq. 4-13 instead of Eq. 4-7 and setting ds, in Eq. 4-8 equal to dh,i.. No

25

saturation excess is produced. Subsequently, this procedure is repeated for all lower horizons

until at one step infiltration excess is produced or all input is infiltrated. Saturation at the soil

surface is produced, if all upper horizons plus the current horizon are saturated before the end

of the time step being modelled.

The scaling is one of the few calibration parameters in WASA. It is introduced to balance the

underestimation of rainfall intensities by daily rainfall data. If used in this way, a ratio of

average high resolution rainfall data to average low resolution rainfall data used in the model,

describes it best. The factor can also be applied for adjusting the infiltration routine

accounting for the high spatial variability of infiltration due to surface crusts, macropores and

so on. In this study, it is set to 1.

Eq. 4-7: Fi,si,s R/Ft ====

Eq. 4-8: i,ai,si,s ndF ⋅⋅⋅⋅====

Eq. 4-9: (((( )))) 1s/k/R

dFi,sF

i,f

i,s−−−−

====ψψψψ

Eq. 4-10: ii,ti,a nn θθθθ−−−−====

Fs,i Infiltration volume until ts,i [mm]

Fi Infiltration volume of whole time step [mm]

ds,i Depth of wetting front below top of horizon i at time ts,i

[mm]

dh,i Depth of horizon i [mm]

na,i Refillable porosity of horizon i [-]

nt,i Total porosity of horizon i [-]

Θi Internal water content of horizon i [Vol%100][-]

Ψf,i Suction at wetting front of horizon i [mm]

ks,i Saturated hydraulic conductivity [mm/∆t]

sF Scaling factor [-]

i Index for soil horzon currently computed [-]

Eq. 4-11:

i,s

i,s

ii,si,si F

cF

cFlnc)tt(FF ++++

++++

++++⋅⋅⋅⋅++++−−−−====

Eq. 4-12:

i,fi,anc ψψψψ⋅⋅⋅⋅====

Eq. 4-13:

Fi,ai,hi,s R/)nd(t ⋅⋅⋅⋅====

26

4.1.2.4. Soil water model

The soil water model describes the soil moisture at each time step for each horizon. It

balances incoming and outcoming fluxes. The incoming fluxes are infiltration according to

section 4.1.2.3, lateral subsurface flow from higher TCs and SVCs within the same TC and

percolation from upper horizons. The outgoing fluxes are evaporation from the soil surface

(see section 4.1.2.2) which only takes into account the uppermost horizon. Transpiration (see

section 4.1.2.2) is applied to the whole rooting zone by weighting every horizon according to

its fraction of field capacity in relation to the total field capacity of the entire routed zone.

Furthermore percolation to the lower horizon or deep groundwater and outgoing lateral

subsurface flows to other SVCs of the same TC or lower TCs have to be mentioned.

Whenever the actual water content exceeds water content at field capacity, water for outgoing

fluxes is available.

Percolation depends on the hydraulic conductivity (unsaturated) of the soil. It can be limited

by the refillable porosity or the saturated hydraulic conductivity of the lower horizon or of the

bedrock when treating the lowest horizon of the profile.

Lateral flow is described by the Darcy equation (see Eq. 4-14). Hydraulic gradient is given by

the slope of the corresponding TC. Runoff routing from SVC to SVC is done between

horizons of more or less the same depth. If a profile is too shallow, the additional input is

added to the next higher horizon. Excess water is added to the surface runoff of a SVC.

.Eq. 4-14:

TCi,sq skA ⋅⋅⋅⋅====

4.1.2.5. Lateral distribution of surface and subsurface flow between soil vegetation components

Runoff generated on a SVC is divided into flow to the lower TC and flow to the adjacent

SVCs. The amount of runoff routed to a lower TC is determined by the fraction of area, the

SVS occupies within a TC.

Runoff is evenly distributed between al SVCs although they are not necessarily connected for

they do not represent georeferenced, discrete areas but the statistical fraction of a combination

of soils and vegetation. They do not represent an area but rather patches.

Runoff distribution is performed according to the fraction of area of the receiving SVC within

its TC (see Figure 4-2). This distribution pattern is also applied to subsurface runoff.

Wherever possible, subsurface runoff is routed to a horizon of similar depth below ground

27

surface. If the soil subsurface runoff is routed into is too shallow or reaches saturation, surplus

us added to surface runoff as return flow (see 4.1.2.4).

Eq. 4-15:

(((( ))))∑∑∑∑====

⋅⋅⋅⋅====n

1xx,SVCSVCTC aQQ

Figure 4-2: Lateral distribution of runoff between soil vegetation components on one TC. (Figure taken from Güntner (2002); with permission)

4.1.2.6. Lateral distribution between TCs of surface and subsurface runoff

A fraction of surface runoff is directly transferred into the river (Eq. 4-16) representing

concentrated flow in rills. The amount of water directly contributed to the river is calculated

by the fraction of area the corresponding TC occupies within the sum of its own area and the

lower TCs. At each step, the area of reference is composed of the TC currently computed plus

the lower TCs. In case of the lowest TC the reference area is just the area of the TC itself.

Runoff output is thus completely transferred into the river.

The rest of the output is divided between all lower TCs according to their fraction of area (see

Eq. 4-17) within the LU. Surface Runoff production at the level of landscape unit is calculated

by summing up the direct output of the upper TCs (rill flow9 plus the output of the lowest TC.

Subsurface runoff is completely transferred to the next TC and then equally distributed among

all SVCs.

28

Eq. 4-16:

∑∑∑∑∑∑∑∑====

⋅⋅⋅⋅====

m

1xm

x

x,TC

x,TCx,TCriver

a

aQR

Eq. 4-17:

∑∑∑∑∑∑∑∑

−−−−

====

⋅⋅⋅⋅====

1y

1xm

x

x,TC

y,TCx,TCy,TC

a

aQR

aTC Fraction of area of TC in landscape unit

x Index of TC which is runoff source are for flow to be redistributed

y Index of TC which is runoff sink area of redistributed flow

m Number of TCs within a landscape unit

Figure 4-3: Lateral transfer of water and sediment from higher to lower TC. TC = Terrain component, the number indicates the position within the landscape unit. (1= highland, 2 = slope, 3 = lowland), taken from Güntner, 2002, with permission.

29

4.1.3. The Modified Universal Soil Loss Equation

4.1.3.1. Introduction to the Modified Universal Soil Loss Equation

Within the SESAM project, the WASA-Model has been extended by new routines describing

the sediment transport at the three different levels the model works: on the hills, in the river

network and in reservoirs. The sediment-transport routine for the hillslopes, which is

investigated in this work, is based on the MUSLE approach (Modified Universal Soil Loss

Equation) by Williams (1975) (see Eq. 4-18). It is, as the USLE (Universal soil loss equation,

by Wischmeier and Smith (1978)) an empirical equation based on the evaluation of field

measurements acquired predominantly on agricultural fields in North America. The MUSLE

offers several advantages over the USLE especially when working at the level of catchments.

Instead of rainfall energy, the energy input to the MUSLE is a runoff variable composed of

the surface runoff and the peak runoff rate (see Eq. 4-18). This allows the application to

individual storm events (Williams, 1995). Furthermore, it eliminates the need of a sediment

delivery ration necessary in the USLE. Using runoff as energy component increases the

accuracy of sediment yields because runoff accounts for more variation in soil erosion than

rainfall (Smith et al., 1984).

Currently, the MUSLE is applied at the level of TCs, a subunit of the catchment to account

for spatial variability of hydraulic properties of soil and vegetation. The MUSLE implies a

nonlinear relationship between the area and slope length of the spatial unit it is applied to and

sediment production. Sediment production is routed linearly though between the TCs along

the hillslope though. The division of hillslopes into spatial subunits in combination with linear

routing of sediment, as it is currently done in WASA and other spatially distributed models,

(e.g. SVAT, see Chen and Mackay, 2000) is not in agreement with this integrated approach of

the MUSLE designed for entire hillslopes. Area and slope length, both factors changed by

different spatial distributions, are in nonlinear relationship with the output of the equation –

sediment production.

The quantification of the effect this disagreement of sub model assumptions has on sediment

production is one of the tasks dealt with in this study (see section 4.3.2 and 5.3).

Eq. 4-18: Modified universal soil loss equation after Williams, 1975

ROKFLSPCK)areaq(Q11.8sed USLEUSLEUSLEUSLE0.56

tcpeaksurf ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅====

30

Sed sediment yield (t/ha)

Qsurf surface runoff volume (mm/ha)

qpeak peak runoff rate (m³/s)

area area of the terrain component m²

KUSLE USLE soil erodibility factor after Williams, 1995

cmtonm

hrmt013.03

2

⋅⋅⋅⋅−−−−

⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅

CUSLE USLE cover and management factor [-]

PUSLE USLE support practice factor [-]

LSUSLE USLE topographic factor (-) [-]

ROKF coarse fragment factor (-) rock)0053.0(exp ⋅⋅⋅⋅−−−−

Qsurf is generated in the hydrological routine of the WASA-Module. The peak runoff rate (qpeak) reads

Eq. 4-19:

conc

tcsurfconc_t

peakt6.3

AQq

⋅⋅⋅⋅

⋅⋅⋅⋅⋅⋅⋅⋅====

αααα

αt_conc amount of rainfall during tconc [mm] )1log(t2 5.0concexp1

αααα−−−−⋅⋅⋅⋅⋅⋅⋅⋅−−−−

Atc area of Terrain component [m²] GIS

tconc time of concentration [h]

Q

TC

v3600

l

⋅⋅⋅⋅

α0.5 fraction of daily precipitation that falls within 30 min

[mm/0.5h] 0.21

lTC absolute slope length of TC [m] GIS

vQ overland flow velocity [m/s] 6.0nmanning/3.0

TCslope4.0ovq

⋅⋅⋅⋅

qov average overland flow rate on a 1 m strip

[m3/s]

6tc

TCtin_sufout_surf

10A2

l3600d)qq(

⋅⋅⋅⋅⋅⋅⋅⋅

⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅++++

dt Time step in [h] 24

qsurf_out surface runoff towards lower TC

[m³] See section

qsurf_in surface runoff from upper TC [m³]

31

Kusle originally depended on the content of organic material, soil structure and soil

permeability. Willams (1995) proposed a new approach reading:

Calculation of the KUSLE factor Neitsch et al. 2002

Eq. 4-20: hisandorgsiclcsandUSLE ffffK ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅==== −−−− with

−−−−⋅⋅⋅⋅⋅⋅⋅⋅−−−−⋅⋅⋅⋅++++====

100

m1m256.0exp3.02.0f silt

scsand

a factor that reduces soil erosion for soils with a high content in coarse sand

3.0

siltc

siltslcl

mm

mf

++++====−−−−

a factor which leads to low erodibility for soils with high clay to silt ratio

[[[[ ]]]]

⋅⋅⋅⋅−−−−++++

⋅⋅⋅⋅−−−−====

orgC95.272.3exporgC

orgC25.01forg

a factor that reduces erodibility for soils with a high content in organic carbon

−−−−⋅⋅⋅⋅++++−−−−++++

−−−−

−−−−⋅⋅⋅⋅

−−−−====

100

m19.2251.5exp

100

m1

100

m17.0

1fss

s

hisand

a factor which reduces erodibility for soils with extremely high sand contents (Neitsch et al. 2002)

These calculations are based on particle size classes as used in the US soil taxonomy. Sand

ranges from 2 to 0.05 mm, silt from 0.05 to 0.002 mm and clay is smaller than 0.002 mm

(Brown, 2003)

CUSLE is the cover and management factor that accounts for reduction in soil erosion by

vegetation cover or residue left on the field. On the badland slopes, this value is set to 1,

representing maximum erosion. For the other soil vegetation components the value is derived

by multiplying the value for the corresponding vegetation cover and the value for the

corresponding tillage method (see Stone, 2005). PUSLE is the support and practice factor

describing the ratio of soil loss ascribed to a specific support practice to the corresponding

loss with up-and-down slope management. Support practices would be stripcropping, counter

tillage and terrace systems. Since no such practices could be observed in the field, the factor is

set to the maximum of 1 on the badland slopes and 0 on vegetated slopes. For agricultural

crops, the corresponding values were inserted (see Neitsch et al., 2002).

The topographic factor (LSUSLE) is described by the following equation:

32

Eq. 4-21: topographic factor of the MUSLE

(((( ))))(((( ))))065.0sin56.4sin41.651.22

LLS hillhill

2

m

hillUSLE ++++⋅⋅⋅⋅++++⋅⋅⋅⋅⋅⋅⋅⋅

==== αααααααα

with Lhill Slope length [m]

m Exponential term [[[[ ]]]](((( ))))slp835.35exp16.0m ⋅⋅⋅⋅−−−−−−−−⋅⋅⋅⋅====

slp Slope [m/m] (rise over run)

αhill Slope angle [°]

hilltanslp αααα====

Slope length in WASA is initially given by the mean slope length of the landscape unit. Slope

length for the TC is computed as a weighted fraction of the original slope length according to

the fraction of area the corresponding terrain component occupies within the landscape unit

and its slope.

4.1.3.2. Lateral distribution of sediment

The soil erosion routine is applied on the level of TCs. Other than the hydrological routines, it

is not applied to every SVC but mean values of all SVCs within a TC weighted by their

fraction of area are calculated and applied to the MUSLE.

Sediment generation is not represented in the sum of its processes but my means of an

empirical equation that cannot account for additional sediment input from upper TCs. To

avoid an overestimation of erosion through sediment input from upper TCs, two additional

limit modes were implied in WASA:

• a limit based on the transport capacity of runoff according to Govers (1990) and Morgan et al. (1998).

• a limit based on the sediment produced on the corresponding area assuming maximum erodibility (K=0.5) (see 0.).

The first one is a process-based approach based on the transport capacity after Govers (1990)

and Morgan et al. (1998) (see Eq. 4-22). Is the sediment concentration higher than the

transport capacity, the rest of the sediment remains on the TC representing resdeimentation.

The second approach compares the sum of incoming sediment and sediment produced on the

TC with the erosion rate calculated for the corresponding TC and its configuration but with

KUSLE (erodibility factor) set to the maximum of 0.5 cmtonm

hrmt013.03

2

⋅⋅⋅⋅−−−−

⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅.

33

Transport capacity according to Govers (1990) and Morgan et al. (1998)

Eq. 4-22 :

(((( ))))ηηηηϖϖϖϖϖϖϖϖ crcTC −−−−====

TC transport capacity c experimentally derived coefficient − 6.0

50 ]32.0/)5d[( −−−−++++

ω unit stream power [cm/s] sv100 ov ⋅⋅⋅⋅⋅⋅⋅⋅

vov velocity of overland flow [m/s]

6.0nmanning/3.0

TC4.0

ov slopeq ⋅⋅⋅⋅

qov average overland flow rate [m³/s] 6tc

TCtin_sufout_surf

10A2

l3600d)qq(

⋅⋅⋅⋅⋅⋅⋅⋅

⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅++++

manningn Manning coefficient of roughness [m173/s] Values from literature, Neitsch et al. (2002)

Atc area of terrain component [m²] See section 4.2.1.2 for derivation

ωcr critical value of unit stream power [cm/s] 0.004 m/s

s slope [%] See section 4.2.1.2 for derivation

η experimentally derived coefficient − 25.050 ]300/)5d[( ++++

d50 median particle size of soil [µm] Computet within WASA

lTC absolute slope length [m] See section 4.2.1.2 for derivation

dt time step [h]

In case of the sensitivity analysis, sediment transfer was not applied which is not of relevance

due to the fact that the spatial discretization applied used only one terrain component per

badland (see section 4.3.1). For the other two spatial discretizations, 4 different combinations

of routing and transfer mode were applied (see Table 4-1).

Mode A omits sediment transfer between TCs and allows unlimited erosion. Mode B, C and

D all transfer sediment from upper to lower TCs along the hillslope but have different

transport limits. Mode B has, as mode A, no transport limit. It allows maximum erosion.

Mode C is limited by the transport capacity of runoff and Mode D is limited by the amount of

sediment production computed with maximum erodibility. Mode A was applied in spite of

having more than one terrain component on BL1, BL3 and BL4 in order to quantify the effect

of sediment transport at the level of terrain components and to test weather or not erosion

rates represent the spatial variability of soil and vegetation factors.

34

Table 4-1: Routing and transport modes for sediment applied during this study

Case Routing mode Transport mode

A no sediment transfer between TCs no transport capacity limit

B sediment transfer between TCs no transport capacity limit

C sediment transfer between TC transport capacity according to Govers, 1990; Morgan et al., 1998

D sediment transfer between TC transport capacity = max. soil erosion after MUSLE equation, K = 0.5

4.2. Field work

During twelve days of field work (01.09.-12.09.05), the specific characteristics of badlands in

the Isábena catchment were investigated. As mentioned before, various authors have

identified the badlands in the sub catchment of the Arroyo de Villacarli as the main sediment

source responsible for the siltation of the Barasona reservoir (Valero-Garcés et al. 1999).

The field campaign aimed at getting an overview of the morphological and hydrological

processes acting on the badland slopes. It was tried to characterize the badlands by relevant

parameters and in addition, data was collected which was used for the parameterization of the

model in order to compute sediment and soil production and transport along representative

hillslopes.

4.2.1. Survey

4.2.1.1. Description

The investigation aimed at detecting spatial variation of parameters relevant for model

parameterization on the badland hillslopes. Four exemplary badlands, characteristic for the

badlands occurring in the study area, situated in the Arroyo de Villacarli (see Figure 4-4 and

Figure 4-5) were chosen to conduct the survey. Additionally, more detailed investigations

were carried out on one badland of optimal size and accessibility. Measurements were

conducted from within the ephemeral channels of the badlands. The main channels were

followed and every 50 (BL11 and BL2) or 150 steps2 in case of the larger badlands 3 and 4 the

following parameters were mapped:

1 BL = badland, used when referred to one of the four study sites 2 one step = ca. 0.7 m

35

• Slope angle of the left and right slope facing upstream in the internal gully. The data was recorded in percent by means of a handheld inclinometer

• Aspect is measured by means of an electronic compass in built in the Garmin etrex summit GPS-device

• Vegetation cover was estimated in percent considering a strip from the ridge to the gully of about 2 m width and roughly described using the categories “bush”, “tree”, “grass” and “herbaceous”

• Width of the gully was measured with measuring tapes

• Outlines of the edges were measured with two Garmin etrex summit GPS-devices as far as possible. The missing data was endorsed by air photographs which are available at a scale of 1:5000 (M.A.P.A, 2005) and processing in a geographical information system.

Figure 4-4: the four catchments of the badlands used for modelling spatial discretization 3 and 3. (Aerial picture form Sept. 1982, taken by I.G.N.)

Figure 4-5: The Villacarli sub catchment with the four study sites

4.2.1.2. Results

BL1 is situated on the northern side of the Villacarli and is exposed to south-southeast (see

Figure 4-5). It is shaped like a caldera and has two main internal channels of about 60 m

length. It is the smallest of the four badlands. The surrounding vegetation is a Mattoral,

formed of bushes, smaller trees, grasses and herbs, with a vegetation cover of about 90%. A

relatively large fraction of the actual badland slopes is covered with shrubs and grasses on a

shallow soil. On the bare slopes, no soil development can take place due to the high erosion

rates. A regolith layer with a mean depth of 15 cm covers the grey marls. The badland extends

36

over 4000 m² with a mean slope length of 70.68 m and a mean slope angle of 68.61 %. Its

pediment is large in comparison to the rest of the badland (see Figure 4-6 and Table 4-6).

The other three badlands are located on the southern side of the valley. BL2 has a similar

form as BL1. The extension is about 40677.73 m², but the actual surface of the slope is larger

than in case of BL1 due to the steeper (101.33 %) and longer (133.36 m) slopes (see Table 4-2

and Figure 4-6). It is situated right next to the course of the Arroyo de Villacarli near its

confluence with the Isábena (see Figure 4-5). No real pediment can be differentiated.

Surrounding vegetation is a mixed, dense forest. The mesoclimate seems to be wetter than on

the northern side of the valley assuming by the mosses growing on branches and leaves (see

Figure 4-7). The slopes have only sparse vegetation cover.

BL3 and BL4 are long stretched out along one main ephemeral channel of about 1.5 km

length which is directly connected to the Arroyo de Villacarli (see Figure 4-6). In the lower

third, the channel forms meanders. In between those, stable pediment areas have developed

that bear vegetation and only seem to be flooded during extreme flood events. This

phenomenon is less pronounced in BL4 than in BL3. The upper channels are narrower and

deeper. West of BL3, agriculture is practiced, while the rest of the surrounding area within the

sub catchment of BL3 is covered by coniferous forest. Vegetation on the badland slopes

themselves is also very sparse. The comparison of the mean slope angle (see Figure 4-8)

between the four badlands shows that exept for BL3, the eastern slopes have a higher

inclination than the western slopes. The variation between the different badlands is bigger

then between the eastern and western slopes, though. (see Figure 4-8). Figure 4-9 shows the

mean vegetation cover which is clearly higher on the western than on the eastern slopes. In

general, vegetation cover is higher on the southern side of the valley, i.e. on the north facing

slope. In contrast to the observations from the Vallcebre catchment where less vegetation

cover was found on the north facing slopes due to freezing (Regüés et al., 1995), in the

Villacarli valley, the humidity on the north facing slopes seem to favour plant growth.

The badlands studied here belong to the humid type. Sporadical pipe erosion has been

observed but is not as dominant as on other badlands (see Romero Díaz et al. 2006). Only

small mass movements were observed during field work but the huge bolders in the channels

of the larger badlands suggest that bigger events do occur (see Figure 2-1).

37

BL1 BL2 BL3 BL3

Figure 4-6: The four study sites. In green their outer limitation, in red the main channels. Aerial fotography by I.G.N. 1982.

Table 4-2: Main characteristics of the four Badlands

Badland 1 Badland 2 Badland 3 Badland 4

Area [km²] 40000 40677.73 266200 296800

Slope [%] 68.61 101.33 71.66 81.45

Slope length [m]

39.99 93.747 69.3 76.14

Aspect SSE NE NNE NNE

Surrounding vegetation

Matorral Dense forest

Coniferous forest

Coniferous forest

Figure 4-7: Mosses and lichens found in the forest around BL2 on a north facing slope.

38

0.00

20.00

40.00

60.00

80.00

100.00

120.00

slo

pe %

eastern slope 68.61 107.67 74.69 83.55

w estern slope 71.39 95.00 68.63 79.36

standard deviation 16.00 25.70 16.42 10.50

standard deviation 13.89 14.18 25.99 16.84

BL1 BL2 BL3 BL4

0

10

20

30

40

50

ve

ge

tati

on

co

ve

r [%

]

eastern slope 25.50 4.00 19.66 14.40

western slope 44.53 17.33 42.25 47.09

standard deviationeast

35.17 13.57 40.30 28.06

standard deviationwest

35.17 13.57 40.30 28.06

BL1 BL2 BL3 BL4

Figure 4-8: Mean slope and standard deviation of the four exemplary badlands

Figure 4-9: Comparison between mean vegetation cover of slopes

4.2.2. Detail study

4.2.2.1. Description

4.2.2.1.1. Choice of the study site

As can be seen in Figure 4-11 (left side), the course of the Villacarli River once cut the slopes

of BL1. In the mid 1980s the street was regulated and with it the river. Therefore, nowadays a

dam the street was built on delimits the catchment of BL1 (see Figure 4-11, right side). The

only outlet is a culvert which was filled half with sediments during the field work in

September 2005. Although water drains through the dam during heavy storm events, it still is

an effective sediment trap. Estimating the amount of sediment accumulated behind the dam

since its construction would allow estimations of erosion rates during the past 20 years

(currently done within the SESAM project). In addition to this favourable situation, BL1 is

easily accessible, not too large and still allows the monitoring of the important processes

acting on the slopes. Therefore, BL1 was chosen for the detail study consisting of in situ

measurement of soil hydraulic parameters and numerous soil samples. It is also predestined

for instrumentation which is planned for anotother field campaign within the SESAM project.

39

4.2.2.1.2. Infiltration measurements

The measuring device used for the infiltration

measurements is a hood-infiltrometer, model UGT

Münchenberg (see Figure 4-9). The hood is connected to

a Mariotte apparatus which assures a steady state flow

while a constant hydraulic potential at the boundary layer

between soil and water is maintained. No contact layer of

sand has to be applied as necessary for other measuring

devices. Measurements on the badland slopes proved to

be difficult in the field for the hood has to be set up in a

horizontal position. On the badlands this could only be

assured measuring on a ridge or by levelling the soil

which disturbs natural soil surface conditions. The results

of the measurements conducted on the badlands are thus not trustworthy.

A steady state flow results from the measurement. For calculation of the saturated hydraulic

conductivity (kf), measurements under at least two different potentials are necessary.

Potentials of zero and one were applied during this campaign. Wherever possible, the

measurement was repeated in 30 cm below the natural soil surface. In addition, a soil sample

was taken with a 100 cm³ ring cylinder at most sample sites for determination of bulk density

and soil texture analysis. The saturated soil hydraulic conductivity can be calculated by

applying equations proposed by Gardener (1958) (see Eq. 4-23) and Wooding (1968) (see Eq.

4-24).

Eq. 4-23: Hydraulic conductivity after (Gardner, 1985) )h(

fu expkk ⋅⋅⋅⋅⋅⋅⋅⋅==== αααα

ku Hydraulic conductivity

[distance/day]

Kf Saturated hydraulic conductivity [distance/day]

h hydraulic tension

Eq. 4-24: Stationary flow after Wooding, 1968

)a

41(krQ u

2

⋅⋅⋅⋅⋅⋅⋅⋅++++⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅====

ααααππππππππ

with

Figure 4-10: Infiltration measurement

40

Q Volume/time

r Radius of circular infiltration surface

α

)hh(

)Q

Qln(

21

2

1

−−−−

Q can also be expressed as

Eq. 4-25:

t

ArQ 2

∆∆∆∆

∆∆∆∆ππππ ⋅⋅⋅⋅====

That gives

Eq. 4-26:

)r

41(

r

Q

k2

1

u

ααααππππ

ππππ

⋅⋅⋅⋅⋅⋅⋅⋅++++

⋅⋅⋅⋅====

Table 4-3 shows the saturated hydraulic conductivity for the measured infiltration rates

according to the different soil types found on BL1. Values are highly variable and especially

for the badland slopes, the most important unit of this study, the measurements with the hood-

infiltrometer seem to overestimate infiltration with a mean value of 8876.44 mm/d. Regües et

al. (2000) mention rates of 512 mm/d on badland slopes in north eastern Spain. To back up

the measured values ks was also estimated with the ROSETTA model (Shaap, 2000). By

means of pedotransfer functions, the model predicts water retention after van Genuchten

(1980) (see Eq. 4-27) and estimates saturated hydraulic conductivity from soil textural data

and bulk density, if available. That allowed to include some of the soil samples of the detail

study (see below), therefore for model parameterization, the mean values estimated by the

ROSETTA model were applied although ROSETTA underestimates ks (see Table 4-3). Only

in case of the regolith on the badland slopes, infiltration according to Regüés et al. (2000) was

assumed.

.

41

Table 4-3: Saturated hydraulic conductivity measured on badland slopes and derived by pedotransfer functions applying the ROSETTA model (Shaap, 2000). Sample ID is composed of the measurement number and the depth in which it was

conducted. 0 = on natural soil surface, 30 = 30 cm below soil surface

Soil type Sample ID ks [mm/d] measured

ks [mm/d] ROSETTA

5_0 417 No sample

6_0 1446 No sample

9_0 18663.9 111.58

10_0 14978.0 11.25

mean_0 8876.44 61.41

s.d._0 9305.72 70.94

5_30 7692.33 113.53

6_30 372.88 121.54

mean 15 7054.97 117.54

Regolith on bare badland slopes

s.d. 5175.63 5.66

13_0 4212 353.91

14_0 Not valid 115.62

mean_0 4212 234.76

Badland slope with vegeation

s.d._0 - 168.50

2_0 0 No sample

3_0 0 99.1

4 Not valid 99.64

Mean_0 0 99.37

s.d. - 58.34

4_30 214 182.15

Mean_30 - -

Pediment

s.d._30 - -

1_0 13219.93 510.29

8_0 14145 232.74

11_0 9580 3736.27

12_0 9220 119.64

Mean_0 11541.17 1149.73

s.d._0 2505.34 1732.15

1_30 4168 128.26

11_30 6960 197.35

12_30 838 195.75

Mean_30 3988.75 173.79

highlands

s.d.30 3065.26 39.43

42

Eq. 4-27: Water retention after van Genuchten (1980)

n/11n

rsr

])h(1[)h(

−−−−++++

−−−−++++====

αααα

ΘΘΘΘΘΘΘΘΘΘΘΘΘΘΘΘ

Θ(h) Water content Θ [cm³/cm³] at a certain water pressure head h [cm]

Θr Residual water content [cm³/cm³]

Θs Saturated water content [cm³/cm³]

α Curve shape parameters [1/cm]

n Curve shape parameters

4.2.2.1.3. Soil sampling and analysis of samples

Soil texture is necessary for the derivation of most of the soil hydraulic parameters required

by WASA. Therefore, 35 soil samples were taken from gullies, slopes (with and without

vegetation) and ridges to detect spatial variability of soil hydraulic properties (for sampling

design, see Figure 4-11). These samples were analyzed in the laboratory of the Institute of

Geoecology at the University of Potsdam. Soil texture was determined by wet sieving for the

particle sizes coarse sand (<2 mm – 0.63 mm), medium sand (<0.63 mm – 0.2 mm) and fine

sand (0.2 mm – 0.063 mm). Coarse silt (<0.063 mm – 0.02 mm), medium silt (<0.02 mm –

0.0063), fine silt (<0.0063 mm – 0.002 mm) and clay (< 0.002mm) were determined by

analyzing 50 ml of soil suspension in a diffractometer. The suspension was collected at the

very beginning of the sieving process at the outlet of the sieving machine.

In addition, electrical conductivity and pH (in H2O) were measured. For the measurements of

pH 10 g of fine soil (< 2 mm) were suspended with 25 ml of distilled water and left over

night. Before measuring the pH, the soil was suspended again.

Organic substance was estimated following the instructions of the AG Boden (1994). The

regolith on the badlands has less than 1 mass percent. The soil on the highland has 2-4 % and

the soil on the pediment as well as on the slopes with vegetation cover has 1-2 % content of

organic matter.

43

Figure 4-11: Left: Aerial photography of BL1 taken in right: Aerial photography (M.A.P.A. 2005) showing Badland 1 which has been studied in detail. Crosses mark the sample localities where the soil samples were taken during the detail study

4.2.2.2. Results

Figure 4-12 shows the mean grain size distribution for the four geomorphological units

“edge”, “slope”, “slope with soil” and “gully”. In all cases, over 50 % are composed of fine

silt or clay. The edge and slope samples even show a content of over 75% clay and silt. At the

slopes with vegetation and in the gullies, the content of sand increases. The higher sand

content on the vegetated slopes probably derives from rests of the sandstone layer that once

covered the marls. The higher contents in the channel might indicate selective erosion.

pH varies very little over the whole area of BL 1 whereas the electrical conductivity ranges

between values from 901 to 2320 µs/ cm (see Table 4-4). The maximum value is a single

outlier taken on what seemed to be a salty crust. By eliminating this value, mean electrical

conductivity becomes 1161.58 µs and standard deviation 295.83 µs.

44

0.0

0.2

0.4

0.6

0.8

1.0

fracti

on

clay % 31.83 17.03 30.04 26.02

fine silt % 46.38 31.71 45.02 38.93

medium silt % 18.16 22.12 20.15 18.01

coarse silt % 0.00 0.67 0.00 0.01

fine sand % 3.01 15.74 3.39 7.84

medium sand % 0.60 7.67 0.80 6.07

coarse sand % 0.00 5.05 0.60 3.11

edge slope (with vegetation) slope (bare) gully

Figure 4-12: Mean grain size distribution for the four geomorphological units

Figure 4-13: Particle size distribution of soil samples taken on badlands

Table 4-4: Results of chemical soil analysis

pH (H2O) Electrical conductivity [µs/cm]

Max. 7.92 901

Min. 7.42 2320

Mean value 7.66 1127.41

Standard deviation 0.15 352.92

Variation coefficient 0.02 3.19

45

4.3. Parameterization of the WASA-Model

During this study, three different spatial discretizations were applied. Spatial discretization 1

is the simplest approach which was applied for the sensitivity analysis. Spatial discretization 2

is assumed to be the best representation of the considered sub catchments. Spatial

discretization 3 is a slightly modified version of spatial discretization 2, testing the hypothesis

that the MUSLE approach does not support the spatial discretization as applied in the WASA

model.

The derivation of parameters will be described explicitly. Values will be mentioned

exemplarily for the most important spatial unit, TC2 representing the badland slopes. The

complete parameterization can be found on the CD in form of Access data bases.

4.3.1. Spatial discretization 1 – the sensitivity analysis

The first spatial discretization is a very simple approach which was applied during a

sensitivity analysis. Each badland is represented by one SB, one LU, one TC and one SVC.

Only the badland slopes were taken into account and the soil-vegetation component applied

represents the bare regolith on the marls.

The sensitivity analysis served to determine the influence of changes in relevant parameters

on the production of surface runoff and sediment production. Knowing the sensitivity of a

parameter allows estimating which input parameters have to be chosen carefully and which

ones may be less accurate due to low influence on the results. Furthermore, it allows

evaluating the range of uncertainty of the calculated variables. Therefore, relevant parameters

were varied within a range around the default value which is assumed to be the most realistic

one (see Table 4-5). Parameters taken into consideration were parameters from the

hydrological model as well as from the MUSLE equation. A period of one month, September

2000, was modelled. Parameters applied in this scenario were preliminary. Their derivation

will thus not be described in detail here and the results, as there are sediment production in

tonnes and runoff in m³, are not directly comparable to the other two scenarios. The results on

model performance though can be transferred to the other two studies.

46

Table 4-5: Range of values as used for the sensitivity analysis

Parameter WASA Unit Default value Derivation / source

Range over which value was varied during sensitivity analysis

Hydraulic conductivity

[mm] 512.6 Regüés et al, 2000 0.01-1000

Precipitation [mm] 50 Set arbitrarily 0-100

Manning’s roughness coefficient for overland flow

[m173/s] 0.07 Neith et al. 2002, table 19-1 mean value for no till and no residue

0-1

Peak runoff intensity - 0.9 Set arbitrarily 0-0.9

Parameters MUSLE

Erodibility factor K [(ton acre hr) / (acre ft-ton inch)]

0.21 Automatically computed by the model after Williams, 1995.

0.1-0.5

Slope [%] BL1 = 68.61 BL2 = 101.33 BL3 = 71.66 BL4 = 81.45

Field measurements (see section 4.2.1.2)

50-120

4.3.2. Spatial discretization 2 and 3

In case of spatial discretization 2, each of the four badlands is represented by one subbasin in

the model. Only one landscape unit for each subbasin was assigned. Three terrain components

represent the different characteristic geomorphological units defined by the characteristic

slope angles; in this case, highland, slope and lowland. All together, 10 soil vegetation

components (further on referred to as SVC) were applied (see Table 4-6).

For spatial discretization 3, TC2 of spatial discretization 2 was divided into two equal TCs.

TC1 and TC4 (TC3 on spatial discretization 2), were not changed. Former TC2 is now

represented by two TCs of smaller size and slope length This was done in order to quantify

how the nonlinearity relationship of area and slope length defined in the MUSLE affected the

results in sediment production.

For BL1, the watershed was derived from reference points taken in the field with the handheld

GPS. For BL3 and BL4, the catchment was derived with the “catchment grid delineation”

function of the ArcHydro toolset Version 1.1 Beta (ESRI and CRWR 2004) based on a digital

elevation model with a resolution of 45 m from Verdú (2002). The derived catchments were

then adjusted based on air photographs from the years 2001 and 2002 (M.A.P.A, 2005).

Neither of these two methods was applicable in case of BL2. The dense vegetation did not

allow the differentiation of the divide in the field and the area is too small to be properly

represented by the DEM. Since BL 2 is completely surrounded by dense forest, only little

47

surface runoff from upper terrain components is expected which is why only the area of the

badland itself was considered as one subbasin. The delineation of the badland was performed

based on the GPS points taken during field work in combination with the aerial photographs

mentioned before.

Badland 1, 3 and 4 are divided into three TCs representing highland, slope and valley bottom.

BL2 consists of just one TC representing the slopes. Area and length of each TC were derived

by digitizing them on the air photographs of 5 m resolution and by taking into account the

measuring points from the field campaign wherever possible. Slope angles for TC2 which

represent the badlands, were derived from the data gained during the field trip (see section

4.2.1). The value for the valley bottom was derived by the measuring points taken on BL1.

BL2 does not have a valley bottom. The slope descends directly in the gully which is why it is

only composed of one TC. The slope of the highland terrain component in badland one was

estimated from the GPS-Points taken in that area. For BL3 and BL4 the area of the terrain

components 1 and 3 was intersected with the slope layer, derived from the digital elevation

model with the slope tool of the Extension “Spatial Analyst” in the ArcGIS – software. A

mean value was derived.

48

Table 4-6: Spatial discretization 2 for the four badlands

SB1 =

57083 m²

LU = 57083 m² Slope length = 135.71 m

TC1 f.o.a.

+ = 0.30%

Slope = 64.46%

TC2 f.o.a. = 0.55%

Slope = 68.61%

TC3 f.o.a. = 15%

Slope = 2.9%

SVC 4 = 100% SVC1 = 73.6% SVC2 = 0.26% SVC 3 = 76.8% SVC 7 = 23.2%

Highland Marls Marls-veg Pediment Pediment

Mattoral None Shrubs Shrubs none

SB2 =

40678 m²

LU =40678 m² Slope length = 93.76 m

TC2 f.o.a. = 100%

Slope = 101.33%

SVC 1 =73.6% SVC2 = 0.26%

Marls Marl-veg

None shrubs

SB3 =

836880 m²

LU = 836880 m² Slope length = 267.39

TC1 f.o.a.= 63%

Slope = 20%

TC2 f.o.a. = 36%

Slope = 71.66%

TC3 f.o.a. = 1%

Slope = 2.53

SVC1 = 1.0% SVC5 = 72.8%

SVC6 = 26.2%

SVC1 = 67.5%

SVC2 = 23.6%

SVC8 = 8.90%

SVC 3 = 99.3% SVC 7 = 0.7%

Marls Highland Highland Marls Marls-veg Marls Pediment Pediment

None Coniferous

forest Crop None Shrubs Mattoral Matorral none

SB4 =

728275 m²

LU = 728275 m² Slope length = 194.16 m

TC1 f.o.a. = 65% Slope = 15%

TC 2 f.o.a. = 34.9%

Slope = 81.46%

TC3 f.o.a. = 0.1

Slope = 2.9%

SVC1 = 3.4% SVC5 = 96.9% SVC1 = 80.7%

SVC2 = 12.9%

SVC8 = 6.30%

SVC3 = 7.40%

SVC7 = 91.1%

SVC10 = 1.40%

Marls Highland Marls Marls Marls veg Pediment Pediment Pediment

None Coniferous

forest None shrubs Mattoral Matorral none Matorral

f.o.a. = fraction of area

49

4.3.3. Derivation of soil types and their hydraulic properties

Based on the soil samples acquired during the field campaign (see 4.2.2), four different soil

types were discriminated. On the badland slopes themselves, only a regolith layer ca be

distinguished. The soils were classified according to their location and hydrological

properties:

a) Soils on the highland with vegetation growth, a content of organic matter of 3% and two

horizons differentiated. Mean soil depth is 1.1 m. This soil has the highest saturated hydraulic

conductivity (kf) with 1363 mm/d. organic content is set to 1%.

b) Shallow regolith on the marls of the badland slopes with no vegetation cover and content

of organic matter at all. Mean soil depth is 15 cm. kf is moderate with 512.1 m/d.

c) Soil on vegetated marls (marls-veg) that shows soil development. The mean organic

content is 2 %. Infiltration is lower than on the badland slopes.

d) Soil on the pediments which has the lowest infiltrability for the sediment deposited here is

compacted (kf = 99.37).

For the complete description of the soil types, see tables “soils” and “horizon” in the

corresponding Access databases on the CD.

Table 4-7 shows all soil hydraulic parameters needed for model parameterization and their

derivation.

Table 4-7: Soil parameters required by the WASA model

Parameter Description Unit Source Value for SVC1, representing the badland slopes

ks,LU Hydraulic conductivity of bedrock [mm d-1] 2 measurements in the field 403.26

dmax Mean maximum depth of soil zone [mm]

[m] Field experience 300

drb Depth of river bed below terrain surface

[mm] Field experience 1800 (value is given at the level of LU, here for BL1)

nt Porosity [Vol%] [-] Rawls et al. (1992), p. 5.14 0.46

ks Saturated hydraulic conductivity [mm d-1]

• ROSETTA model

• Measurements

• Literature

512.1 mm/d (after Regüés et al. 2000)

ψf Suction at wetting front [mm] after Rawls and Brackensiek as cited in Rawls et al. 1992, p. 5.36

27.41

sF Scaling factor for hydraulic conductivity [-] WASA-intern 1

Φc Content of coarse fragment [Vol%]

[-] Estimated, field experience 0.03

θres Residual water content [Vol%] [-] After van Genuchten, as cited in Rawls et al. 1992, p. 5.6

0.08

λs Pore-size index [-] After Brooks and Corey as cited in Rawls et al. 1992, p. 5.15 0.27

hb Bubbling capillary pressure [mm] After van Genuchten as cited in Rawls et al. 1992, p. 5.6.

137.67

50

4.3.4. Derivation of vegetation types and their hydraulic properties

Vegetation cover and the corresponding hydraulic properties are important input parameters

for the WASA-Model. Based on a map by Fernández-Ruffet and Cereyo (1998), field

observations and the evaluation of air photographs, four land cover types relevant for this

study were distinguished being:

• Shrubs and shrub like trees (Matorral)

• Coniferous forest

• Agriculture and annual crops

• Bare soil

The corresponding hydraulic properties relevant for the parameterization of WASA (see Table

4-9) were taken form literature. The difficulty working with literature values is the fact that

studies either only comprise one of the parameters required or are limited to one species.

Breuer and Frede (2003) compiled a database based on a comprehensive literature review, the

so called PlaPaDa (Plant Parameters Database, available under http://www.uni-

giessen.de/~gh1461/plapada/plapada.html).

The database lists species and landcover classes. Table 4-8 lists the records taken into

consideration to calculate the value for the WASA landcover class. The file

vegetation_sum.xls on the CD contains the full tables from the PlaPaDa and tables containing

only the records considered for the derivation of parameters.

Table 4-8: Landcover classes used for parameterization in WASA and the corresponding classes of the PlaPaDa

WASA landcover class Records of PlaPaDa considered

Matorral (33% trees, 8.5% shrubs, 58.5% herbs and grasses; personal estimation)

• For trees a mean value of coniferous and deciduous trees was derived

• For grasses a mean value of the PlaPaDa landcover class “Pasture” was derived

• For shrubs a mean value of relevant shrubs named In the PlaPaDa was derived. (See)

• Value for WASA category as a weighted average according to the fraction each class covers (see file “vegetation_sum.xls” on the CD)

Coniferous forest

• Coniferous forest

• Pinus sylvestris

• Pinus nigra (mentioned in Sánchez Navarro and Ollero Odeja, 2003)

• Pinus Pinaster (mentioned in Bärtels, 1997)

Agriculture

• Sunflower • Wheat

• Barley (All mentioned as major crops in Verdú, 2003)

Bare soil (bare rock or soil, 5% grassland, 5% shrubs personal estimation)

• Value for WASA category as a weighted average according to the fraction each class coveres

• Value for grass = mean value of all records in PlaPaDa landcover class “pasture”

• Value for shrubs see derivation of WASA landcover class “matorral”

• Values for bare soil = mean of all values for “bare soil” in the PlaPaDa landcover class “soils”

51

Ψw (soil matrix potential at wilting point) and Ψcr (critical soil matrix potential below which

stomata closure occurs) where derived from the root water uptake module of the hydrological

model “Hydrus 1D 3.0“(Simunek et al. 2005). The model contains an internal database for

different vegetation types. For pasture, a mean value of “pasture” and “grass” was derived.

For woody vegetation, the value for deciduous fruit was applied, assuming it to be the best

estimation of the value. The values do not differ much after all. To allow for changes

according to the seasons, the date for the inset of rainy seasons needs to be set in WASA. It is

assumed that the factor inducing seasonality in this study area is not so much the dryness but

low temperatures. The dates for the inset of the seasons have been derived by calculating the

moving average of daily mean temperature for 7 days. The day this value is higher than 9°C is

set to be the beginning of spring. After this date, the first day the moving average is lower

than 9°C autumn sets in. Since no detailed data for transition seasons is available, only the

differentiation between summer and winter is applied. The dates were derived for every year

that was simulated (see file “WASA\Input\spat_dis_2\- hillslope\rainy_season.dat”).

Table 4-9: Model input parameters for vegetation

Parameter Description Unit Source Value for bare soil

h Canopy hight (var) [m] PlaPaDa 0.09

dr Root depth (var) [m] PlaPaDa 0.1

Λ Leaf area index (var) [-] PlaPaDa 0.47

α Albedo (var) [-] PlaPaDa 0.3

rls, min Minimum stomatal resistance

[s m-1] PlaPaDa, converted

770

Ψwp Soil matrix potential at wilting point

[hPa] Hydrus_1D 8000

Ψcr Critical soil matrix potential below which stomata closure occurs

[hPa] Hydrus_1D 613

h1 Interception coefficient [mm]

WASA-intern parameter, following Güntner, 2002

0.3

4.3.5. Derivation of climate data

WASA recalls for the following climate parameters in at least daily resolution:

• Rainfall [mm]

• Air Temperature [°C]

• Relative Humidity [%]

• Incoming shortwave radiation [W/m²]

52

Furthermore, extraterrestrial radiation in monthly resolution is needed. Within the Isábena

catchment, no data for radiation and relative humidity was available which is why data from

the climate station “Pont de Suert” were used. It lies further east but at almost the same

altitude as the Arroyo de Villacarli (see Figure 3-2). Time series from 01.05.1996 –

31.12.2005 were available. Since the model needs values for every day, wherever data was

missing the value was set to the mean monthly value.

Values for extraterrestrial radiation were derived by the GRASS model r.sun (Hovierka and

Šúri, 2002) by Francke and Wichmann (2005). For the complete time series used for

modelling see files under “WASA\Input\spat_dis_2\Time_series\”

4.4. Summary

The hydrological model WASA has been extended by an erosion routine based on the

empirical Modified Universal Soil Loss equation. The MUSLE integrates over processes

causing soil erosion on entire hillslope. Its application to spatial subunits (terrain components)

as done in WASA could lead to poor model performance.

Spatial discretization 1 is the less detailed approach which as been applied for the sensitivity

analysis. Spatial discretization 2 is the approach best representing the four exemplary

hillsplopes dividing them into thee terrain components being highland, slope and lowland.

Only BL2 I represented by just one TC. Spatial discretization 3 represents the same spatial

variability of soil and vegetation parameters as spatial discretization 2 but displays the

hillslope by four terrain components instead of three. For model parameterization, field

measurements were used as far as possible and completed with data from literature.

53

5. Results and discussion This chapter presents the results of water and sediment production for the four study sites as

simulated with the WASA model and the new soil erosion routine based on the MUSLE

equation. The chapter is divided into three parts describing the three spatial discretizations

applied (see section 4.3).

First of all, the results of the sensitivity analysis are presented, identifying the most sensitive

parameters. Then, the results of spatial discretization 2, which is considered to be the most

realistic approach so far, are presented at the level of terrain components (TC) and at the level

of subbasins (SB). The third spatial discretization aims at quantifying the effect of sub model

disagreement. Results are presented as mean daily values in case of the sensitivity analysis or

as mean annual values in case of spatial discretization 2 and 3. The complete time series

computed with the WASA model can be found as excel worksheets on the attached CD

(WASA\Output\spat_dis_2\summary_spat_dis_2.xls and WASA\Output\spat_dis_3-

\summary_spat_dis_3.xls)

5.1. Results of spatial discretization 1

Spatial discretization 1 is the simplest discretization and was applied to conduct the sensitivity

analysis which was performed for the four badlands. Each subbasin is represented by just one

homogenous TC representing the badland hillslope. Four hydrological parameters and three

MUSLE parameters were varied over a broad range of values (see section 4.3.1). As climate

input data (except rainfall which was set to 50 mm rainfall per day) the mean monthly value

for September 2000 were chosen. Figure 5-1 and Figure 5-1 show the runoff yield in blue and

the sediment yield in brown for each badland and the varying values of the parameter

considered. The red circles and subtitles indicate the value, which was taken as default value

for the other scenarios.

54

5.1.1. Hydrological parameters

The four hydrological parameters analyzed were precipitation, saturated hydraulic

conductivity (ks), runoff intensity factor and the Mannings roughness coefficient.

Precipitation, as the amount of water getting into the system is a highly sensitive factor for

runoff production as well as for the sediment yield. It was varied from 0 to 100 mm per day.

With 50 mm per day as default value, quite a high value was chosen. But it is the high

intensity rainfall events that are relevant for this study. Runoff yield in m³/m² d varies from 0

to 0.1 m³/m² d. Sediment yield varies between 0 and 0.125 t/m² d.

Güntner (2002) found the saturated hydraulic conductivity to be a very sensitive parameter for

the water balance. Results of this study do state a low sensitivity, though. This is ascribed to

the low soil depth of only 150 mm and in consequence poor storage capacity. No matter how

high ks is set, the pore volume is filled very fast assuming a constant rainfall of 50 mm/day as

in this sensitivity analysis and saturation excess runoff is generated . Values for runoff yield

vary between 0.05 m³/m² for the minimum ksat value = 0 mm/d and 0.04 m³/m² for the

maximum ksat value =1000 mm/d.

The runoff intensity factor is a value introduced in WASA to estimate the maximum 0.5 hour

rainfall intensity if rainfall data is only available in daily resolution by estimating the fraction

of daily rainfall that comes down during half an hour as suggested by Williams (1995). This

value is used to calculate the peak runoff rate. Since the peak runoff is part of the energy

factor of the MUSLE (see Eq. 4-19), sediment yield is very sensitive to changes. A variation

between 0 and 0.9 leads to variation in sediment production between 0 and 0.05 t/m² d.

Runoff instead does not show any reaction at least when using a daily time step. With high

resolution climate data, the changes in antecedent soil moisture due to evaporation and

evapotranspiration should lead to changes in runoff. With a default value of 0.9, the runoff

intensity factor was also set very high for the sensitivity study.

The Manning’s roughness coefficient for overland flow describes the roughness of the soil

surface. The lower it is, the higher is the runoff velocity, i.e. the higher is the transport

capacity of the runoff (see Eq. 4-22). The factor cannot be set to 0 (see Eq. 4-19) thus was

varied between 0.01 and 0.6. It is a very sensitive factor, affecting runoff as well as sediment

yield. Runoff varies between 0.8 and 0.02 m³/m² d. Sediment production varies between 0.06

and 0.001 t/m².

55

5.1.2. MUSLE Parameters

The three parameters of the MUSLE which were tested in the sensitivity analysis were the

erodibility factor (KULSE), slope and the fraction of coarse fragment in the soil matrix.

None of the MUSLE factors affects the runoff yield, as expected. Runoff results for the three

parameters are equal to the result for peak runoff intensity shown in the previous section. The

K-factor is the erodibility factor of the MUSLE. It is varied between 0.1 and 0.5. Sediment

yield within this range varies between 0.01 t/m² d and 0.7 t/m² d.

Slope is part of the MUSLE slope length factor. Slope was varied from 50 % to 120 %.

Sediment production ranges from 0.01 up to 0.06 t/m² d. The graphs shows as well the

importance of the factor slope length. BL1 and BL2 are about the same size but differ in slope

and slope length (see Table 4-5). For equal slope values, BL2 derives about 30% more

sediment production as BL1. Slope length for BL1 is 39.99 m and for BL2 93.75 m. This

illustrates the importance of the slope length factor in the MUSLSE.

The coarse fraction describes the MUSLE ROFK (Rock fragment factor) as exp(− 0.053 *

coarse fraction). The factor is not sensitive at all. Changes between 0 to 0.3 cm³/cm³ which

correspond to 30% of the soil volume only lead to changes in sediment yield of maximum 0.5

%. Changes in coarse fraction should affect the runoff yield because a higher content in

coarse fraction reduces the storage capacity of the soil. As in the case of the saturated

hydraulic conductivity, the shallow soils which saturated immediately at this high rainfall rate

and complete rainfall becomes runoff.

56

Figure 5-1: Results of the sensitivity analysis for important hydraulic parameters

Reference value: 50mm

Reference value: 512.1 Regüés et al 2002

Reference value: 0.9

Reference value: 0.07

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 200 400 600 800 1000

hydraulic conductivity [mm/d]

se

dim

ent

yie

ld [

t/m

² d

]

BL1 BL2 BL3 BL4

c

runoff yield

0.00

0.02

0.04

0.06

0.08

0.10

0 0.2 0.4 0.6 0.8

peak runoff intensity factor

run

off

yie

ld [

m³/

m² d

]

BL1 BL2 BL3 BL4

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 0.2 0.4 0.6 0.8

peak runoff intensity factor

se

dim

en

t y

ield

[t/

m² d

]

BL1 BL2 BL3 BL4

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 0.1 0.2 0.3 0.4 0.5 0.6

Mannings roughness coefficient

se

dim

en

t y

ield

[t/

m² d

]

BL1 BL2 BL3 BL4

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 20 40 60 80 100

precipitation [mm]

se

dim

en

t y

ield

[t/

m² d

]

BL1 BL2 BL3 BL4

runoff yield

0.00

0.02

0.04

0.06

0.08

0.10

0 0.1 0.2 0.3 0.4 0.5 0.6

Mannings roughness coefficient

run

off

yie

ld [

m³/

m² d

]

BL1 BL2 BL3 BL4

runoff yield

0.00

0.02

0.04

0.06

0.08

0.10

0 200 400 600 800 1000

hydraulic conductivity [mm/d]

run

off

yie

ld [

m³/

m² d

]

BL1 BL2 BL3 BL4

runoff yield

0.00

0.02

0.04

0.06

0.08

0.10

0 20 40 60 80 100

precipitation [mm]

run

off

yie

ld [

m³/

m² d

]

BL1 BL2 BL3 BL4

57

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.1 0.2 0.3 0.4 0.5

K-factor

se

dim

en

t y

ield

[t/

m² d

]

BL1 BL2 BL3 BL4

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

50 70 90 110

slope [%]

se

dim

en

t y

ield

[t/

m² d

]

BL1 BL2 BL3 BL4

sediment yield

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 0.05 0.1 0.15 0.2 0.25 0.3

coarse fraction [cm³/cm³]

se

dim

en

t y

ield

[t/

m² a

]

BL1 BL2 BL3 BL4

runoff yield

0.00

0.02

0.04

0.06

0.08

0.10

0.1 0.2 0.3 0.4 0.5

K-factor

runo

ff y

ield

[m

³/m

² d

]

BL1 BL2 BL3 BL4

runoff yield

0.00

0.02

0.04

0.06

0.08

0.10

0 0.05 0.1 0.15 0.2 0.25 0.3

coarse fraction [cm³/cm³]

runo

ff y

ield

[m

³/m

² d

]

BL1 BL2 BL3 BL4

runoff yield

0.00

0.01

0.02

0.03

0.04

0.05

0.06

60 70 80 90 100 110 120

slope [%]

runo

ff y

ield

[m

³/m

² d

]

BL1 BL2 BL3 BL4

Reference value: different for each BL. See Table 4-2

Figure 5-2: Results of the sensitivity analysis for important factors of the Modified Universal Soil Loss Equation

Reference value: 0.22

Reference value: 0.03

58

5.2. Results for spatial discretization 2

5.2.1. Results at the level of terrain components

5.2.1.1. Runoff generation at the level of terrain components

Table 5-1 shows the mean annual runoff in m³/a for each TC of the four badlands. In the

following tables, maximum values will be indicated by red, minimum values by green

numbers. Table 5-3 contains the size of each TC in m².

Maximum runoff is generated on TC2 of BL3 (45095.29 m³/a). Although TC2 of BL3 is only

the second largest TC, it has a higher runoff response than TC1 of BL3 which is the largest

TC. These results represent the spatial variability of soil and vegetation parameters for TC1 of

BL3 is coverd with coniferout forest and has high interception storage volume and

evapotranspiration losses. Less runoff is produced than the bare slopes of TC2.

TC1 of BL1 produces the minimum runoff (914.39m³/a) although being larger than TC3 of

BL4. TC3 of BL4 though, receives high runoff input from the upper both TCs (see section

4.1.2.5) and has thus the highest runoff yield with 1.89 m³/m² (see Table 5-2). TC1 of BL3

and BL4 produce the minimum runoff yield of 0.04 m³/m², which reflects the dense

vegetation cover on both areas.

Figure 5-3 shows the fraction of runoff each TC contributes to total runoff.

Table 5-1: Mean annual runoff [m³/a] per TC

TC1 TC2 TC3

BL1 914.39 4411.44 3629.99

BL2* - 5175.12 -

BL3 23541.48 45095.29 2889.76

BL4 21236.43 39291.98 1706.88

red=maximum; green = minimum

Table 5-2: Mean annual runoff yield [m³/m³a] per TC

TC1 TC2 TC3

BL1 0.05 0.14 0.41

BL2* - 0.13 -

BL3 0.04 0.15 0.31

BL4 0.04 0.16 1.89

59

0.0

0.2

0.4

0.6

0.8

1.0

BL1 BL2 BL3 BL4

run

off

[%

]

TC1 TC2 TC3

Figure 5-3:Relative runoff contribution of each TC in %

Table 5-3: Size of each TC in m²

TC1 TC2 TC3

BL1 17103.05 31229.83 8749.99

BL2* 40677.73

BL3 529186.24 298347.28 9356.13

BL4 473936.38 253435.09 903.37

* BL2 has only one TC

Table 5-4: Slope length of terrain component in m

TC1 TC2 TC3

BL1 48.44 90.03 20.77

BL2 - 133.46 -

BL3 170.43 116.12 5.35

BL4 126.24 86.14 2.53

5.2.1.2. Sediment generation at the level of terrain components

Table 5-5 contains the mean annual sediment production in t/a for each badland at the level of

terrain components for the four different routing modes (see section 4.1.3.2.). Table 5-6

follows the same structure and contains the sediment yield in t/m² a. Mode A shows unlimited

sediment production on each TC without input from higher TCs. It was applied to see how

WASA displays spatial variability expressed by spatial discretization. In case of BL1,

sediment production on the highland (TC1) is quite high (29.86 t/a) in comparison to BL3 and

BL4 (17.07 and 0.67 t/a, respectively). This reflects the spatial variability of vegetation cover.

In case of BL1, the highland is covered with matorral, whereas the highlands of BL3 and 4 are

almost completely covered by coniferous forest. Under coniferous forest, soil erosion

becomes zero. All sediment produced on TC1 of BL3 and 4 derives from soil vegetation

60

components with other vegetation cover (see Table 4-6). This explains the huge difference in

sediment production between the highlands of BL3 and 4. 26% of the highlands of BL3 are

agricultural cropland and about 1% is bare soil as on the badland slopes. In case of BL4, 97%

are forest and only 3% are bare soil (see Table 4-6).

The highest sediment production at all three terrain components occurs on TC2 which is the

one representing the badland slopes. The absolute maximum applying mode A is produced on

TC2 of BL4 with 14401.74 t/a. Although this TC is smaller, shorter and has more vegetation

cover than TC2 of BL3 (see Table 4-6), it produces more sediment due to the fact that it is

steeper which is in agreement with the results of the sensitivity analysis (see section 5.1.2.)

The lowest sediment production and sediment yield occurs on TC3. Being the smallest terrain

component and the one with less inclination, this result again shows that the soil erosion

routine is able to reproduce the structure of landscape given by terrain components.

Mode B, C and D all account for lateral transfer of sediment from higher to lower TCs.

Mode B allows unlimited erosion. Erosion rates for this mode are thus the highest for all TCs

compared to the other routing modes. If a value for the same TC but mode C or D is equal to

the value derived by mode B, no limit is reached and maximum erosion possible (e.g. TC2 of

BL4 mode B and mode D; see Table 5-5, red numbers).

Mode C is limited by the transport capacity of runoff (see Eq. 4-22, section 4.1.3.2). In

general, this leads to lower results than Mode D. Only at TC3 of BL4, the value for mode C is

higher than for mode D. Mode D highly depends on the size of the corresponding TC. Since

TC3 of BL4 is the smallest TC (see Table 5-3), the limit derived by setting the erodibility

factor in the MUSLE to a maximum, is extremely low.

The comparison of routing modes at the level of terrain components shows, that in general

standard deviation and coefficient of variation are very low, even though routing mode A

without any lateral transport of sediment was included in the calculation of these values. On

TC1, no limit comes into action at. In case of mode B, the result is the same as mode A

because no additional sediment input could add to the sediment generated on the TC. In case

of mode C it is due to the fact that transport capacity is simply not exceeded. In case of mode

D TC, the implicit limit is given by the MUSLE factors applied. Without additional sediment

input, the limit cannot be reached.

On TC2, the effect of the limits is already stronger than on TC1 because of the input by

sediment transfer. But TC2 of BL2 shows, that even without additional input, transport

capacity can be exceeded. As mentioned earlier in this chapter, in most cases examined in this

study, mode C is the lower limit compared to mode D. The highest impact of the routing and

61

transport modes is found on TC3, especially with the unlimited mode B. As the lowest terrain

component, TC3 receives input from all upper TCs (see section 4.1.3.2). On the other hand, as

the TC with the smallest area, the limit of mode B is very low. Furthermore, its slope,

influencing the transport capacity, is the lowest of all three terrain components. The

coefficient of variation is much higher than 100%, indicating the high variability on this

terrain component caused by the routing and transport modes.

Sediment yield is highest on TC2 for routing mode A ranging from 0.06 on BL4 to the

maximum of 0.16 t/m² found on TC2 of badland 2 due to the combination of bare, highly

erodible soil with high inclination. Lowest values can be found on TC3 ranging from

6.44*10-5 t/m²on BL1 to 1.37 * 10-6 t/m²on BL4, which is the total minimum (see Table 5-6).

The highest sediment yield of all is produced on TC3 of BL4 applying routing mode B.

Variation coefficient remains the same.

Figure 5-4 shows the fraction of sediment generated on each TC in relation to the total sum of

sediment generated on the LU. It shows the little importance of TC1 and TC3 compared to

TC2 considering sediment yield (compare Figure 5-3). The MUSLE approach in combination

with the WASA model is thus capable of representing spatial variability of soil and landcover

characteristics.

Table 5-5: Mean annual sediment production [t/a] for the four badlands at the level of terrain components

BL1 BL2+ BL3 BL4

TC1 TC2 TC3 TC2 TC1 TC2 TC3 TC1 TC2 TC3

A 29.86 2794.39 0.56 6588.88 17.07 12271.33 0.92 0.67 14401.74 1.24E-03

B 29.86 2815.29 615.89 6588.88 17.07 12279.50 659.33 0.67 14401.98 524.43

C 29.86 2794.94 197.08 6253.25 17.07 12279.03 217.39 0.67 14401.39 223.67 D 29.86 2815.29 1.10 6588.88 17.07 12279.50 1.80 0.67 14401.98 2.90E-03

mean 29.86 2804.98 203.66 6504.97 17.07 12277.34 219.86 0.67 14401.77 187.03 standard deviation

0.00 11.91 289.97 167.82 0.00 4.01 310.17 0.00 0.28 248.42

coefficient of variation [%]

0.00 0.42 142.38 2.58 0.00 0.03 141.08 0.00 0.00 132.83

62

Table 5-6: Mean annual sediment yield [t/m²a] per TC and routing mode

BL1 BL2*

BL3 BL4

TC1 TC2 TC3 TC2 TC1 TC2 TC3 TC1 TC2 TC3

A 1.75E-03 0.09 6.44E-05 0.16 3.23E-05 0.04 9.84E-05 1.41E-06 0.06 1.37E-06

B 1.75E-03 0.09 0.07 0.16 3.23E-05 0.04 0.07 1.41E-06 0.06 0.58

C 1.75E-03 0.09 0.02 0.15 3.23E-05 0.04 0.02 1.41E-06 0.06 0.25

D 1.75E-03 0.09 1.26E-04 0.16 3.23E-05 0.04 1.92E-04 1.41E-06 0.06 3.21E-06

mean 17.07 12277.34 219.86 1.60E-01 3.23E-05 4.12E-02 2.35E-02 1.41E-06 5.68E-02 2.07E-01

standard deviation

0.00 4.01 310.17 0.00 0.00 1.35E-05 0.03 0.00 1.09E-06 0.27

coefficient of variation

0.00 0.42 142.38 2.58 0.00 0.03 141.08 0.00 0.00 132.83

Table 5-7: Portion rill erosion of the sediment production of one TC

TC1 TC2 TC3

BL1 0.30 0.78 -

BL2 - - -

BL3 0.63 0.95 -

BL4 0.64 0.96 -

*BL2 has only one terrain component ** Lowest TC, all output is directly transferred into the river network Maximum is indicated by a red number, minimum is indicted by a green number

BL1

0.0

0.2

0.4

0.6

0.8

1.0

A B C D

routing and transport mode

sed

imen

t p

rod

uc

tio

n

TC1 TC2 TC3BL2

0.0

0.2

0.4

0.6

0.8

1.0

A B C D

routing and transport mode

sed

imen

t p

rod

uc

tio

n

BL3

0.0

0.2

0.4

0.6

0.8

1.0

A B C D

routing and transport mode

se

dim

en

t p

rod

ucti

on

BL4

0.0

0.2

0.4

0.6

0.8

1.0

A B C D

routing and transport mode

se

dim

en

t p

rod

ucti

on

Figure 5-4: Fraction of the mean annual sediment production of every TC on four badlands for different routing modes. Routing mode A = no transfer between TCs, no transport limit; B= routing between TCs, no transport limit; C= routing between TCs, transport capacity limit according to Govers (1990) and Morgan et al. 1998 (, D=routing between TCs, transport limit maximal erosion according to the MUSLE equation.

63

5.2.2. Results at the level of subbasins

5.2.2.1. Runoff generation at the level of subbasins

Table 5-8 shows the hydrological response at the level of badlands. Maximum runoff occurs

on BL3 which is the largest SB and has the largest slope (TC2) with 65283.17 m³/a. Minimum

runoff occurs on the smallest BL which is BL2 with 5175.12 m³/a. The relative values such as

runoff yield [m³/m²] and discharge coefficient [m³/m³], which is the fraction of rainfall that

becomes surface runoff, vary only little. At this level, the similarity between the badlands is

revealed. The badland slopes within BL1 and BL2 are of similar size and shape. Although

BL1 has a more differentiated structure with highland and pediment, relative runoff

production is the same as on BL2 which points out the importance of these bare areas for

runoff generation.

Table 5-9 presents minimum and maximum sediment concentration produced on each

badland. Lowest sediment concentrations are produced on BL1, highest on BL2.

Table 5-8: Runoff, runoff yield and discharge coefficient at the level of BL

Runoff [m³/a] runoff yield

[m³/m² a]

Discharge coefficient [m³/m³ a]

BL1 7351.54 0.13 0.14

BL2 5175.12 0.13 0.14

BL3 65283.17 0.08 0.09

BL4 56135.41 0.08 0.09

Table 5-9: Sediment concentration in [g/l] at the level of BL for the four routing and transport modes

BL1 BL2 BL3 BL4

Min Max Min Max Min Max Min Max

A 0.67 452.60 25.00 1561.22 1.31 231.51 2.68 331.65

B 0.83 585.92 25.00 1561.22 1.38 244.64 2.79 344.19

C 0.63 498.02 25.00 1561.22 1.25 235.79 2.56 336.91

D 0.76 458.66 25.00 1561.22 1.37 231.54 2.68 331.65

64

5.2.2.2. Sediment generation at the level of subbasins

Table 5-10 and Figure 5-7 show the mean annual sediment production, respectively sediment

yield at the level of subbasins for all four routing modes. For routing mode A, maximum

sediment production is produced on BL4 with 1387.74 t/a. Minimum sediment production

occurs on BL1 with 2193.14 t/a. The coefficient of variation for the results of the different

routing modes is as well as in case of the terrain components (see section 5.2.1.2) generally

very low except for BL1, where it amounts to 12.23%. As shown on the level of terrain

component, the TC with the highest coefficient of variation was TC3 which in case of BL1 is

more important than for BL3 or BL4 because it occupies a larger fraction of area (see Table

4-6).

The low variance shows that the difference between the routing modes is fairly low and even

disabling lateral transfer (mode A) does not lead to greater differences (see also Figure 5-7).

Table 5-12 shows the fraction of sediment that is directly routed to the river. Values are

around 90% or even higher. Not much sediment is taken into consideration for the process of

lateral distribution.

Table 5-11 contains the sediment yield for each BL. The highest sediment yield occurs on

BL2 due to its steep slopes as was shown before when describing the results of the sensitivity

analysis (see section 5.1.2). The lowest sediment yield occurs on BL3 which is the largest

subbasin but with a large fraction of inactive soils under coniferous forest which does not

contribute to the sediment production.

Figure 5-6 shows the event erosion rates for the year 2000 and the cumulative erosion in form

of the read line. This figure reveals the fact that annual sediment production is dominated by

only few intensive storm events as proposed by Gallart et al. (2005).

Table 5-13 contains the sediment production for the six individual years modelled.

Table 5-10: Mean annual sediment production [t/a] for each BL

BL1 BL2* BL3 BL4

A 2193.14 6588.89 11626.66 13877.74

B 2824.82 6588.89 12291.12 14402.41

C 2390.10 6253.25 11848.72 14101.08

D 2210.01 6588.89 11633.58 13877.97

Mean 2404.52 6504.98 11850.02 14064.80 Standard deviation 294.04 167.82 311.61 248.46 Coefficient of Variation 12.23 2.58 2.63 1.77

65

Red = maximum; green = minimum; comparing the results of mode B, C and D

Table 5-11: Mean annual sediment yield [t/m²a] for each BL

BL1 BL2* BL3 BL4

A 0.04 0.16 0.01 0.02

B 0.05 0.16 0.01 0.02

C 0.04 0.15 0.01 0.02

D 0.04 0.16 0.01 0.02

Mean 0.04 0.16 0.01 0.02

Standard deviation 0.01 4.13E-03 3.72E-04 0.00

Coefficient of Variation 12.23 2.58 2.63 1.77

Table 5-12: Portion rill erosion of the sediment production of each BL Rill erosion /

sediment output

BL1 BL2** BL3 BL4

A 99.97 - 99.99 100.00

B 78.20 - 94.64 96.36

C 91.75 - 98.17 98.41

D 99.95 - 99.98 100.00 *BL2 has only one terrain component ** lowest TC, all output is directly transferred into the river network Maximum is indicated by a red number, minimum is indicted by a green number

BL1

2193.14 2824.82 2390.10 2210.01

0

5000

10000

15000

20000

25000

30000

A B C D

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

BL4

13877.74 14402.41 14101.08 13877.97

0

5000

10000

15000

20000

25000

30000

A B C D

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

BL3

11626.66 12291.12 11848.72 11633.58

0

5000

10000

15000

20000

25000

30000

A B C D

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

BL2

6588.89 6588.89 6253.25 6588.89

0

5000

10000

15000

20000

25000

30000

A B C D

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

Figure 5-5: Mean annual sediment production [t/a] on each Badland for the four routing modes

66

0

100

200

300

400

500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

sed

imen

t p

rod

uc

tio

n [

t]

0

0.2

0.4

0.6

0.8

1

Figure 5-6: Event erosion rates (dots) and relative cumulative erosion (red line) on BL3 with routing mode C for climate data of the year 2000

Table 5-13: Annual erosion rates in [t] of the four badlands for the years 1997 to 2005 modelled with the time series from the climate station of Pont de Suert

Sediment production [t] Annual precipitation [mm]

Year BL1 BL2 BL3 BL4 Pont de Suert

1997 6065.94 15935.24 27438.18 37802.33 1104.8

1998 1063.39 2811.92 5433.38 4381.03 (min) 564.2

1999 2987.92 7436.28 15934.79 18916.36 989.8

2000 1837.69 5318.99 8721.97 10499.42 948.2

2001 3147.27 6750.12 15251.50 17576.71 890.2

2002 1072.10 3616.32 4636.98 5745.68 1032

2003 3455.63 8925.35 19963.72 23589.40 1104.2

2004 316.13 971.08 1314.20 1607.41 599.4

2005 3758.50 9674.45 18711.66 19373.58 (max) 1507.5

Mean 2633.84 6826.64 13045.15 15499.10 971.148

s.d. 1836.01 4624.77 8857.37 12034.01 282.447

c. of v. 69.71 67.75 67.90 77.64 29.08

67

5.3. Results of spatial discretization 3

As described earlier, the MUSLE approach was developed for entire catchments. Its nonlinear

relation to the area and slope length does not support the spatial discretization as applied in

WASA to account for spatial variability of hydraulic parameters. To observe the effect this

disagreement of submodel assumption has on sediment production, a third spatial

discretization was tested. TC2 from spatial discretization 2 was divided into two identical TCs

at all four badlands.

5.3.1. Runoff generation for spatial discretization 3

Changes in spatial disribution also affect runoff because lateral transfer depends on the size of

a terrain component and its fraction of area within the whole cacthment. Table 5-14 contains

the mean annual runoff at the level of TCs and at the level of SB for the four study sites.

Runoff production on TC1 should be the same as for spatial discretization 2 for this terrain

component remains the same and does not receive any lateral input. The sum of TC2 and TC3

is larger than runoff on TC2 in spatial discretization 2 (compare Table 5-1) although the sum

of their parameters would describe the same area. TC4, which correspods to TC3 in spatial

discretization 2, shows different results due to other input values from the upper TCs.

Minimum and maximum values are distributed as for spatial discretization 2.

Total runoff of subbasins is slightly higher but less than 1% in all cases. Runoff yield remains

thus the same.

Table 5-14: Mean annual runoff [m³/a] at the level of terrain components and at the level of subbasin for spatial discretization 3

TC1 TC2 TC3 TC4 SB Runoff yield

BL1 914.39 2209.49 3076.49 4252.37 7364.18 0.13

BL2* - 2624.25 3887.13 - 5199.27 0.13

BL3 23541.48 22607.22 33395.55 10086.44 65518.85 0.08

BL4 21236.43 19645.99 29261.28 5978.79 56304.19 0.08

*BL2 has only 2 TCs in this spatial discretization

68

5.3.2. Sediment production for spatial discretization 3 at the level of terrain components

Table 5-15 and Table 5-16 contain mean annual sediment production and mean anual

sediment yield of all TCs computed for spatial discretization 3.

Sediment production and sediment yield are the same on TC1 for both scenarios (compare

Table 5-5). TC2 and TC3 have the same size and distribution of SVCs only that TC2 is

situated above TC3. TC3 thus receives runoff from the upper TCs (compare Table 5-1 and

Table 5-14). The sum of sediment produced on TC2 and TC3 is almost twice as high as

sediment output of TC2 with spatial discretization 2 (see Table 5-17).

Results on TC4, former TC3, are also considerably higher, even with routing mode A, where

no lateral transfer of sediment but of runoff is conducted which leads to more runoff as shown

in the previous section and thus higher erosion. Also the higher results of mode C which

applies the transport capacity limit show that effect. The transport limit of mode D is also

increased due to the higher runoff.

Sediment yield on TC1 remains the same. On TC2 it slightly decreases. TC3 has a

significantly higher yield. The results on TC4, former TC3 are of course all higher than with

spatial discretization 3.

Table 5-15: Mean annual sediment production [t/a] per TC for spatial discretization 3

BL1 BL2 BL3 BL4

TC1 TC2 TC3 TC4 TC2 TC3 TC1 TC2 TC3 TC4 TC1 TC2 TC3 TC4

A 29.86 1251.12 2076.44 0.72 2983.38 5629.74 17.07 5509.70 9781.15 1.33 0.67 6415.39 11465.43 2.17E-03

B 29.86 1272.03 2851.28 1022.37 2983.38 7121.42 17.07 5516.09 12686.69 1289.31 0.67 6415.63 14790.08 1039.30

C 29.86 1232.25 2325.98 312.53 2713.17 5597.57 17.07 5515.54 12684.89 404.60 0.67 6415.13 14787.94 406.94

D 29.86 1272.03 2851.28 1.41 2983.38 7121.25 17.07 5516.09 12686.69 2.61 0.67 6415.63 14790.08 0.01

mean 29.86 1256.86 2526.24 334.26 2915.83 6367.49 17.07 5514.35 11959.86 424.46 0.67 6415.44 13958.38 361.56

s.d. 0.00 19.14 388.90 481.66 135.10 870.56 0.00 3.11 1452.47 607.01 0.00 0.24 1661.97 490.86

c. of v. 0.00 1.52 15.39 144.10 4.63 13.67 0.00 0.06 12.14 143.01 0.00 0.00 11.91 135.76

Table 5-16: Mean annual sediment yield [t/m²a] for the four BL with TC2 divided into two equal TCs

BL1 BL2 BL3 BL4

TC1 TC2 TC3 TC4 TC2 TC3 TC1 TC2 TC3 TC4 TC1 TC2 TC3 TC4

A 1.75E-03 0.08 0.13 8.24E-05 0.15 0.28 3.23E-05 0.04 0.07 1.42E-04 1.41E-06 0.05 0.09 2.26E-07

B 1.75E-03 0.08 0.18 0.12 0.15 0.35 3.23E-05 0.04 0.09 0.14 1.41E-06 0.05 0.12 0.11

C 1.75E-03 0.08 0.15 0.04 0.13 0.28 3.23E-05 0.04 0.09 0.04 1.41E-06 0.05 0.12 0.04

D 1.75E-03 0.08 0.18 1.61E-04 0.15 0.35 3.23E-05 0.04 0.09 2.79E-04 1.41E-06 0.05 0.12 6.35E-07

mean 1.75E-03 8.05E-02 1.62E-01 3.82E-02 1.43E-01 3.13E-01 3.23E-05 3.70E-02 8.02E-02 4.54E-02 1.41E-06 5.06E-02 1.10E-01 3.77E-02

s.d. 0.00 1.23E-03 0.02 0.06 0.01 0.04 0.00 0.00 0.01 0.06 0.00 1.86E-06 0.01 0.05

c. of v. 0.00 1.52 15.39 144.10 4.63 13.67 0.00 0.06 12.14 143.01 0.00 3.68E-03 11.91 135.76

69

Table 5-17: difference in sediment production on TC2 for spatial discretization 2 and the sum of sediment production on TC2 and TC3 of spatial discretization 3 in %. Sediment production of TC2, spatial discretization 2 = 100%

BL1 BL2 BL3 BL4

A 72.16 86.59 107.58 105.60

B 111.85 118.89 147.38 144.20

C 83.84 89.37 147.37 144.19

D 111.85 118.86 147.38 144.20

mean 94.92 103.43 137.43 134.55

s.d. 20.11 17.87 19.90 19.30

c. of v. 21.19 17.28 14.48 14.34

5.3.3. Sediment production for spatial discretization 3 at the level of subbasins

Table 5-18 contains mean annual sediment production and yield at the level of subbasins for

spatial discretization 3. Although at the level of TCs, sediment generation is always higher

than in case of spatial discretization 2, total sediment output of the subbasin can be lower

using spatial discretiztion 3. Table 5-19 contains the differences between total sediment

production of the spatial discretizations in %. Results of spatial discretization 2 are set to

100%. Negative values indicate lower sediment production with spatial discretization 3.

Figure 5-7 compares the absolute sediment production for spatial discretization 2 (uni) and 3

(stripes).

Considering routing and transport mode A and B as experimental modes and only C and D as

realistic approaches to reality, in 7 out of 9 cases, spatial discretization 3 leads to higher soil

erosion in comparison to spatial discretization 2 (see Table 5-19). BL1 is little affecte by the

changes undertaken in spatial discretization 3. Sediment production on the other three

subbasins has increased by 20% though.

Table 5-18: Mean annual sediment production [t/a] at the level of subbasins for spatial discretization 3

BL1 BL2 BL3 BL4

A 1831.13 7121.43 11407.70 13751.09

B 3358.16 8613.12 15309.27 17881.52

C 2295.69 6954.17 14422.67 17246.93

D 2337.20 8612.95 14022.55 16842.22

Mean 2455.55 7825.42 13790.55 16430.44

Standard deviation 643.99 912.02 1677.09 1836.73

Coefficient of Variation [%]

26.2260 11.6546 12.1612 11.1788

70

Table 5-19: Differences in mean annual sediment production at the level of subbasin between spatial discretization 2 and 3 in %. Mean annual sediment production of spatial discretization 2 is set 100%

BL1 BL2 BL3 BL4

A -16.51 8.08 -1.88 -0.91

B 18.88 30.72 24.56 24.16

C -3.95 11.21 21.72 22.31

D 5.75 30.72 20.54 21.36

Mean 1.04 20.18 16.24 16.73

Standard deviation 14.98 12.23 12.19 11.82 Coefficient of variance [%]

1437.09 60.62 75.11 70.64

Mean (only considering mode C and D)

0.9 20.97 21.13 21.84

BL1

0

5000

10000

15000

20000

11 21 22 23

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

BL2

0

5000

10000

15000

20000

11 21 22 23

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

BL3

0

5000

10000

15000

20000

11 21 22 23

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

BL4

0

5000

10000

15000

20000

11 21 22 23

routing and transport mode

Sed

imen

t p

rod

ucti

on

[t/

a]

Figure 5-7: Mean annual sediment production at the level of badlands for spatial discretization 3. Full = spatial discretization 2; stripes = spatial discretization 3

71

5.4. Validation of the model

Data on erosion rates for the study area are rare, therefore a measuring campaign is conducted

within the SESAM project at the moment. Preliminary results of this campaign and data from

literature will be taken into consideration for the validation of erosion rates computed by the

WASA model.

As described in section 2.2, erosion rates on badlands are highly variable depending on the

method and time scale investigated, and of course the variability between different locations.

For comparison, the studies performed on the Vallcebre badlands by Clotet et al. 1986;

Balasch et al. 1988; Clotet-Perarnau et al. 1988; Gallart et al. 2002a; Regüés and Gallart, 2004

and Gallart et al, 2005 and the studies of Sirvent et al. 1997 conducted on badlands near

Lanaja (Huesca) seem to be most suitable. Both study areas are located in the Pyrenees and

thus present similar climatic conditions. In addition, the Vallcebre experimental catchment

has been set up in the early 1980s. The badlands thus have been investigated for about twenty

years. Different measurement methods have been applied and compared and the papers

published about the studies conducted there present a complete overview on processes on the

badlands at all scales. Most of the values from literature were derived for the badland slopes

explicitly which agrees with the focus of this work. Results of TC2 shall therefore be treated

predominantly in this chapter. Table 5-20 gives an overview of erosion rates as found in

literature compared with values computed with WASA. Literature values range from 120 t/ha

(Gallart et al. 2002a) up to 550 t/ha a. The modelling results for soil erosion on the badland

slopes vary between 568.36 t/ha a and 1619.78 t/ha a. For the larger badlands 3 and 4, the

results computed by WASA are more or less within the range from literature with 411.51 t/ha

a and 568.26 t/ha a, respectively. For the smaller badlands 1 and 2 WASA seems to

overestimate erosion especially when comparing them with the results derived by means of

erosion pins conducted on BL1 Table 5-20. In general though, the results are within the

magnitude of values from literature.

Reasons for the overestimation of erosion by the WASA model might be the time step of one

day which does not explicitly represent single storm events. As explained in section 2.2, the

largest part of annual soil erosion is produced during only few but intense storm events. An

hourly resolution of rainfall data could return better results.

72

Table 5-20: Comparison of results with data from literature (if not indicated, results in [t/ha a])

Vallcebre, eastern

Pyrenees

Lanaja, (Huesca, eastern

Pyrenees)

Central Ebro basin

Isábena catchment

(different authors)

Sirvent et al. 1997

Benito et al. 1992

Collector

• 258.6

• 300 – 400

Mean annual sediment yield of TC2 in t/ha a (modelling result)

BL1 BL2 BL3 BL4

A 894.78 1619.78 411.31 568.26 B 901.48 1619.78 411.58 568.27 C 894.96 1537.27 411.57 568.25 D 901.48 1619.78 411.58 568.27

mean 898.17 1599.15 411.51 568.26

Erosion pins

• 550

• 230

(Clotet_Perarnau et al. 1988)

• 123.7

• Min = 0.67

• Max = 399.09

Erosion pins applied on BL1, period of time recorder: 1 year (personal communication Till Francke)

Profilometer • 304.2 -

Suspended sediment sampler

• 120

(Gallart et al. 2002a)

-

Single rainfall event of 44 mm rainfall

• 12.5

kg/m²

Sediment yield of TC2 for a rainfall event of 45.2 mm/d; values in kg/m² (modelling result)

BL1 BL2 BL3 BL4

A 15.73 40.04 6.42 9.42 B 15.73 40.04 6.42 9.42 C 15.73 38.55 6.42 9.42 D 15.73 40.04 6.42 9.42

Sediment concentration

240 g/l (during one event)

• Max = 1561.22

Computed with WASA • Max = 2650 g/l

recorded during a storm event. (personal communication Dr. Müller)

73

6. Summary and conclusion This study aimed at estimating soil erosion rates on badland hillslopes in NE Spain by means

of a field work integrated modelling approach. The study area is located in the north eastern

Pyrenees and is characterized by a sub-humid climate with a mean annual precipitation is

767 mm/a. Rainfall distribution is characterized by high intensity storm events during spring

and summer that produce high amounts of sediment from the badland hillslopes. Longlasting

low intensity rainfalls during winter produce persistent runoff that is capable of transporting

the sediment stored in the channels.

For modelling hydrology, the hydrological model WASA (Water availability in semi-arid

areas) was applied. Although WASA was developed for application in semi-arid areas, it is

legitimate to apply it on the badland slopes of the study area for the most important process in

both climates is infiltration excess runoff during storm events. Furthermore, soil

characteristics on the badland slopes resemble the conditions of semi-arid soil. WASA is a

process-based, semi-distributed hydrological model. It accounts for interception, evaporation,

infiltration, surface and subsurface runoff, transpiration and ground water recharge as well as

lateral distribution of water between the different spatial units.

For the estimation of sediment production, WASA was combined with the empirical Modified

Universal Soil Loss Equation (MUSLE) that integrates over processes at the hillslope scale.

Besides this conceptual disagreement, a mathematical disagreement is given by the nonlinear

relationship in MUSLE between the area being considered and sediment production but the

linear routing of sediment between spatial subunits within the WASA-model. During two

weeks of field work, a survey was conducted on four exemplary badlands collecting key

geomorphological and soil hydraulic parameters on the badland slopes including size, slope,

vegetation cover, aspect, infiltration and particle size distribution. Two of the four badlands

are always similar in form and size. They can thus be compared among one another.

Model parameterization was performed based on the results of field work and data from

literature. Soil hydraulic parameters necessary for model parameterization were derived

through pedotransfer functions based on the particle size distribution of soil samples from

badland slopes.

Spatial discretization was derived by interpreting aerial photographies and GIS techniques.

A total of three spatial discretizations of increasing complexity were applied: a simple one for

the sensitivity analysis where every subbasin was represented by one terrain component

(spatial discretization 1); a detailed one which presents the best approach to the morphology

74

of the sub catchments (spatial discretization 2) and a third one which exaggerates spatial

discretization by maintaining the distribution of parameters as in spatial discretization 2 but

dividing the subbasin into a higher number of spatial subunits (spatial discretization 3).

Furthermore, four different modes of sediment transfer and transport limit were conducted

with spatial discretization 2 and 3. Routing mode A enables unlimited sediment production on

a TC but no lateral transfer. Mode B also allows unlimited sediment production but accounts

for lateral transfer from higher to lower TCs. Mode C and D both represent lateral transfer

combined with a transport limit. Mode C is limited by the transport capacity of overland flow

whereas mode D is limited by the maximum erosion possible on the corresponding area by

setting Kusle to a maximum.

The integration of the USLE approach within the hydrological model WASA has been partly

successful. The model produces consistent values at the level of terrain components and at the

level of subbasins. Spatial variability expressed by the statistical distribution of soil and land

cover through soil vegetation components is reproduced by the results for runoff and for

sediment production. Sediment yield seems to be overestimated though. Only the results of

BL1 are within the range of values from literature. The results for the other three badlands are

higher, though still within the order of magnitude of the values from literature. Preliminary

results of recent measurements of sediment yield and ground lowering performed on badland

hillslopes in the study area confirm the values form literature.

Different modes for sediment transport between terrain components produce little variance of

only in sediment production and even less in sediment yield. However, changes in the spatial

discretization lead to higher results on three out of four badlands. The variability introduced

by changes in spatial discretization is higher than those by different routing and transport

modes.

The overestimation of sediment production could derive from the modelling timestep of one

day which over represents rainfall intensity for various rainfall events occurring on one day

for they are aggregated to one large event. Furthermore, the seasonality of regolith availability

with high amounts of weathered material after the winter and decreasing values over the

course of the year has not been included in the model so far.

The effect, spatial discretization has on model output should be further investigated in order to

decide, whether or not the MUSLE is compatible to the WASA model and can be further on

applied for the modelling of soil erosion along hillslopes.

75

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8. Appendix

8.1. Appendix 1: Results of pH measurements and electric conductivity measurements for the soil samples taken within the detail study

ID #sample pH (H2O) electrical

conductivity [µs/cm]

1 G1 7.47 1119

2 G10 7.74 901

3 G2 7.65 960

4 G3 7.6 981

5 G4 7.5 n.d.

6 G5 7.84 1010

7 G6 7.64 1023

8 G7 7.5 992

9 G8 7.6 986

10 G9 7.67 911

11 GB1 7.64 1072

12 GB2 7.61 1187

13 GB3 7.44 1260

14 GB4 7.42 1253

15 GB5 7.5 1173

16 H1 7,69 959

17 H2 7.5 2320

18 H3 7.91 1303

19 H4 7.74 1451

20 H5 7.92 1984

21 R3.3 7.42 1162

22 R3.5 7.73 923

23 R1.1 7.84 1127

24 R2.2 7.7 1357

25 R2.1 7.73 1079

26 R2.4 7.87 1058

27 R1.2 7.7 1162

28 R2.3 7.75 1070

29 R3.2 7.7 920

30 R3.4 7.69 975

31 R3.6 7,84 1054

32 R1.3 7.56 1249

33 R3.1 7.48 991

34 R1.4 7.89 1360

mean value 7.66 1127.41

standard deviation 0.15 352.92

Variation coefficient 0.02 3.19

82

8.2. Appendix 2: vegetation parameters needed for parameterization of the WASA module

description Stomata

resistance

Minimum Suction for

inset of water stress (begin of

stomata closure)

Maximum suction

(total stomata closure)

height1 height2 height3 height4 Root

depth1 Root

depth2 Root

depth3 Root

depth4 lai1 lai2 lai3 lai4

Albedo 1

Albedo 2

Albedo 3

Albedo 4

1 agriculture, annual crops

195 2756 22000 1.8 1.8 1.8 1.8 1.71 1.71 1.71 1.71 3.16 3.16 3.16 3.16 0.24 0.24 0.24 0.24

16 coniferous woodland

405.48 650 8000 10 10 10 10 2.75 2.75 2.75 2.75 4.54 4.54 4.54 4.54 0.1 0.16 0.16 0.1

18 shrubs and

shrubbery like tree vege

400.6 650 8000 1.5 1.5 1.5 1.5 1 1 1 1 2 3.35 3.35 2 0.16 0.29 0.29 0.16

20 bare soil/rock or low vegetation

co 770 613 8000 0.09 0.09 0.09 0.09 0.1 0.1 0.1 0.1 0.47 0.47 0.47 0.47 0.3 0.3 0.3 0.3

30 Gras/tree/shrubs (Matorral) 288.97 407.69 8000 1.41 1.41 1.41 1.41 0.81 0.81 0.81 0.81 2.09 2.57 2.57 2.09 0.25 0.28 0.28 0.25

1000 BL1 sensitivity 0.001 0.001 0 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

83

8.3. Appendix 3: Poster about the sensitivity analysis conducted. Presented at the European Geosciences Union General Assembley 2006 in Vienna

84

Erklärung Ich erkläre an Eides Statt, dass ich meine Diplomarbeit „Characterisation of badlands and modelling of soil erosion in the Isábena watershed, NE Spain“ selbständig ohne unerlaubte Hilfe angefertigt und mich dabei keinerlei anderen als der von mir ausdrücklich bezeichneten Quellen und Hilfen bedient habe. Die Diplomarbeit wurde in der jetzigen oder einer ähnlichen Form noch bei keiner anderen Hochschule eingereicht und hat noch keinen sonstigen Prüfungszwecken gedient. Potsdam,