SESAM Sediment Export from large Semi-Arid Catchments: Measurement and Modelling

Transcription

1 SESAM Sediment Export from large Semi-Arid Catchments: Measurement and Modelling Annual Report University of Potsdam, Germany E. N. Müller, G. Mamede, T. Francke, R. J. Batalla, J.C. de Araujo, A. Güntner, A. Bronstert Final Version 15/01/06

2 Table of Contents 1 Introduction 4 2 Study sites and monitoring campaigns of the SESAM project Spain Isabena Esera Catchments Ribera Salada Watershed Brazil Aiuaba Experimental Basin Bengue Representative Basin Orós Watershed 16 3 Modelling approaches within the SESAM project Hillslope modelling River modelling Reservoir modelling Summary 34 4 Fieldwork campaigns within the SESAM project Measurement of hillslope parameters related to water- and sediment-export in the Esera Watershed, NE Spain Fieldwork campaign for the remote sensing based characterisation of land-cover and terrain properties of the Bengué watershed in semi-arid northeast of Brazil Derivation of representative river stretches: A field survey of a mountain river in the Pyrenees, the Isabena River in NE Spain Sedimentological characterisation of the Barasona Reservoir, NE Spain Characterization of the Bengue Reservoir in the semi-arid north-eastern part of Brazil Classification of badlands regarding their morphological characteristics in the Isabena Catchment 71 5 Theses and Reports within the SESAM project Current student projects SESAM Working Reports 80 6 Past and future conference presentations 81 7 Summary and future tasks 87 2

3 PARTNERS OF THE SESAM PROJECT The SESAM project is funded by Deutsche Forschungsgemeinschaft DFG. The following partners are involved: Institute of Geoecology University of Potsdam Postfach Potsdam Germany Website GeoForschungsZentrum Potsdam Telegrafenberg Potsdam Germany Website Department of Environment and Soil Sciences University of Lleida Lleida, Catalonia Spain Website Forest Technology Centre of Catalonia Pujada del Seminari Solsona (Lleida) Spain Website Department of Hydraulic and Environmental Engineering Federal University of Ceara, Fortaleza Campus do Pici Fortaleza, Ceara Brazil Website 3

4 1 Introduction The SESAM project (Sediment Export from Semi-Arid Regions: Measurement and Modelling) is a DFG (Deutsche Forschungsgemeinschaft) funded, joint research project of institutions in Germany (University of Potsdam, GFZ), Spain (University of Lleida, Forest Technology Centre of Catalonia) and Brazil (University of Ceara, Fortaleza). The SESAM project deals with severe soil-erosion problems and resulting sedimentation of reservoirs that commonly occur in dryland regions of Spain and NE Brazil. In dryland environments, water availability for human, agricultural and industrial use often relies on the retention of surface waters in artificial reservoirs. Erosion of the land surface and deposition of the eroded material in such reservoirs threatens the reliability of reservoirs as a source of water supply and often has adverse effects on the local population. The SESAM project objective is the development of a model system that enables the assessment of sediment production at the hillslope scale, the sediment transport in river system and sediment retention in reservoirs for meso-scale catchments in semi-arid and sub-humid regions. The necessary data for model development and application are being derived from extensive data collection programs within experimental catchments, dryland rivers and reservoirs of Spain and North-East Brazil. This annual project report is a summary of the work that has been carried within the SEAM project during the first project year Chapter 2 introduces the two study sites in the dryland regions of Brazil and Spain and describes the previously existing and the within the SESAM project newly installed monitoring schemes for the measurement of water and sediment fluxes. Chapter 3 describes the modelling approaches that are employed to simulate the transport of water and sediment at the hillslope and at the river and to model the sedimentation in large reservoirs. Besides the permanently installed monitoring schemes, it was necessary to collect field data on geomorphological, hydrological and vegetation parameters for the parameterisation of the model directly in the field. For this purpose, three field campaigns were carried out at the specified study sites in Spain and Brazil in 2005 that focused on six different aspects of model input data collection, such as the characterisation of reservoir sedimentation data in Spain and Brazil, the assessment of badland regions and their spatial variations in dryland areas of NE Spain, the applicability of CS radiotracers for the assessment of soil erosion rates in Spain, the collection of ground truth data for land-use estimation of remotely sensed imageries in NE Brazil, and detailed mountain river survey and classification in the lower Pyrenees. The corresponding field reports are given in Chapter 4. Chapter 5 contains a summary on current PhD, MSc and student projects that are directly linked to the scope of the SESAM project, as well as the links to the latest SESAM working reports. Chapter 6 summarises current SESAM conference presentations. Finally, Chapter 7 gives an outlook on future work and tasks. 4

5 2 Study sites and monitoring campaigns of the SESAM project The SESAM project concentrates on soil-erosion and sedimentation processes in drylands located in the semi-arid to subhumid north-eastern part of Spain and the semi-arid regions of north-east Brazil. Main characteristics of the study sites are summarised in the following two chapters. 2.1 Spain Two study sites have been selected in the north-eastern part of Spain as depicted in Figure 2-1: the Esera-Isabena catchments in Aragon, and the Ribera Salada catchment in Catalonia. The catchment sites are within the Ebro watershed as shown in Figure 2-1. The field monitoring program of the study areas is carried out by the University of Lleida and the Forestry Centre of Catalonia. Climatic, vegetation, geological and topographical characteristics of the two study areas are described in the two following sections, each section being followed by information concerning the existing and newly installed monitoring equipment for the measurement of water and sediment fluxes. Figure 2-1 Location of the study sites in NE Spain (Left: Ebro Catchment, Right: Isabena Catchment in dark green) Esera Catchment in light green and Ribera Salada Catchment in red) Isabena Esera Catchments Figure 2-2 displays a map of the Esera and Isabena Catchments and their confluence into the Barasona Reservoir. The Esera Catchment covers an area of 906 km 2, the neighbouring Isabena Catchment an area of 435 km 2. The climate of the Esera and the Isabena Catchments is a typical Mediterranean mountainous type with a mean annual precipitation rates of 600 to 1200 mm and an average potential evaporation rate of 550 to 750 mm. Both rates show a strong south-north gradient, which can be related to the strong topographical difference of altitude within the watersheds ranging from 580 m to 2020 m over MSL. The major rivers in the catchment never dry up, although flows are low during the summer and some of the 5

6 tributaries of the Isabena and Esera Rivers exhibit ephemeral behaviour with no flow at the end of the dry season. Most floods occur due to the passing of cold fronts in spring and to local thunderstorms in autumn and winter. The vegetation includes evergreen oaks and pines in the valley bottoms and deciduous oaks in the upper areas. Figure 2-2 Map of the Esera and Isabena Catchments (Projection: UTM 30N) Figure 2-3 displays a map of the lithology for both catchments. The lower parts of the two catchments are mainly dominated by Miocene continental sediments, the middle part by carbonate rocks and marls, and the upper part by Paleozoic rocks. Figure 2-3 Lithology of the Esera and Isabena Catchments (Valero-Garces et al. 1999) 6

7 Erosion processes are very intense in many parts of the basin even under normal precipitation conditions. A significant part of the erosion processes occur on badlands. Badlands are a typical landform of this region within the upper middle part of the catchments, dominated by Mesozoic carbonate rocks and marls. Badlands are heavily dissected barren terrain that is composed of poorly cemented debris and marls lacking any vegetation cover. Badlands are vulnerable to flash flood erosion and produce large amounts of eroded material, that are then transported downstream as suspended sediments in the Esera and Rivers. The large amount of suspended sediments in the river system creates severe problems of reservoir sedimentation at the outlet of the Esera River. The Isabena River confluences into the Esera River, which then flows into the Barasona Reservoir. The Barasona Reservoir was built in 1932 with an initial water capacity of 71 x 10 6 m 3 mainly for irrigation purposes and power generation. The crest elevation of the dam was increased in 1972 which resulted in an augmented storage capacity of 92 x 10 6 m 3 (Valero-Garces et al. 1999). The Barasona Reservoir is heavily affected by the sedimentation of suspended sediments that reach the reservoir via the Esera and Isabena River. The badlands are thought to be the major cause for the sedimentation of the Barasona Reservoir (Fargas et al. 1996,. According to Sanz Montero et al. (1996), the reservoir had lost about one third of its initial water storage capacity. They report that the volume of accumulated sediment in the reservoir was about x 10 6 m 3 with a maximum thickness of m near the dam wall (in 1995). Most of the sediments are delivered to the Barasona Reservoir during flood events (Valero-Garces et al. 1999). The sedimentation of the reservoir is a severe problem for the region as it is a long-lasting threat for the water outlets to the canal of Aragon and Catalunya. Part of the sediment accumulated in the reservoir was sluiced between 1995 and 1997 after intense engineering works to make the sediment fluid enough to move through the dam bottom gates. Existing monitoring scheme A two-year database with hydrological data for precipitation and flood runoff is available for the Isabena Catchment (Verdú, 2003). Furthermore, there is a gauging station with a record of 60 years of discharges at the Isabena basin outlet, and three gauging stations along the Esera River with a record of 55 years; their locations are depicted in the map in Figure 2-2. In total nine rainfall gauges are available within the catchment areas that provide information on daily rainfall and temperature with a record of at least 15 years, four of them with a record of 55 years. New monitoring scheme within the SESAM project In July 2005, a turbidity meter was installed at the outlet of the Isabena Catchment (at the gauging station number 47 if the Ebro Water Authorities), as depicted in Figure 2-4, which enables the continuous measurement of turbidity with a temporal resolution of 15 minutes. The device is a NEP-390-CBL Turbidity probe NTU (-2.5 v to * 2.5 v dc). The turbidity measurements are going to be used as a measure for the content of suspended sediment concentration mostly during baseflows and small floods in the Isabena River. Data is collected by means of Campbell CR10X data-logger. 7

8 Figure 2-4 Turbidimeter probe and ISCO-3700 sampler water intake at the gauging station 47, Isabena River At the same location, an ISCO-3700 Sampler with water level actuator was installed that takes up to 24 1-litre water samples for a single flood event (Figure 2-5). Direct water samples are regularly taken to support the calibration of the turbidity and automatic sampler measurements. The water samples are collected and the data processed by the Spanish partners of the SESAM project. Figure 2-5 ISCO-3700 Sampler at the gauging station 47 during a small flood in the Isabena River (18/08/2005) 8

9 2.1.2 Ribera Salada Watershed Ribera Salada is a 225 km 2 catchment in Catalonia that includes two nested experimental catchments: the Canalda Watershed with an area of 65 km 2 and the Cogulers Watershed with an area of 2.5 km 2 as depicted in Figure 2-6. The smaller watershed is thought to be controlled mostly by slope-driven processes, whereas the larger one possesses a fully developed fluvial system. Figure 2-6 Schematic drawing of the nested watersheds at the Ribera Salada The climate is a typical Mediterranean mountainous climate, where rivers never dry up, although flows are very low during the summer. Mean annual precipitation is around 700 to 800 mm, mean annual evaporation varies between 700 and 750 mm. Snow melt plays a secondary role, and most floods are due to autumn and winter thunderstorms. The catchment is mostly on conglomerate supporting sandy-loamy soils, the erosion processes being rather limited under normal precipitation. The vegetation includes evergreen oaks and pines in the valley bottoms and deciduous oaks in the upper areas. The mean altitude of the catchment is 700 m MSL ranging between 460 m in the southwest and 2200 m in the northeast. Erosion processes on the headwater slopes were monitored and subsequently modelled by means of the EROSION 2D (Schmidt, 1992) and the EUROSEM (Morgan et al. 1998) models. Results published in Verdú et al. (2000) suggested a predominance of non-hortonian processes and a limited sediment supply to the fluvial system. Suspended load transport is thought to be the dominant sediment transport process, although in a much lower proportion than in the Isabena River, whereas bedload sediment transport is considered to be lower (Batalla et al. 2005, sediment transport data for the period ), but much more relevant than in the Isabena. Under present climatic and land-use conditions, colluvium on the footslopes and the drainage network itself are probably the main sediment sources. 9

10 Table 2-1 Existing water and sediment instrumentation in the Ribera Salada experimental watersheds (Rius et al. 2001) Nested catchments Instrumentation Cogulers Canalda Ribera Salada * (2.6 km 2 ) (65.2 km 2 ) (110 km 2 ) Meteorological station 1 Rain-gauges 3 8 (5+3) 9 (1+5+3) Water level sensors Automatic water and sediment sampling 1 1 Manual water and sediment sampling 1 1 Bedload traps 6 * This section was operating between 2000 and 2002 Existing monitoring data A five-year database containing precise hydrological and sediment transport data is available within the two nested watersheds Cogulers and Canalda. Table 2-1 summarises the existing instrumentation including a meteorological station, several rain gauges, water level sensors, automatic and manual water and sediment samplers and bedload traps that were already installed prior to the start of the SESAM project. New monitoring scheme within the SESAM project In July 2005, the SESAM partners from the University of Lleida and from the Forestry Centre of Catalonia started the installation works of a total sediment transport station in the middle reaches of the catchment (with a contributing area of around 100 km 2 ). The station is composed of three automatic bedload pit traps, a NEP-390-CBL Turbidity probe, an ISCO automatic sampler with 24 1-litre sample bottles and a water level actuator, and a water level sensor. Figure 2-7 Sediment transport station at the Ribera Salada Watershed 10

11 The bedload trap was installed to enable a continuous measurement of bedload flux for single rainstorm events with a sampling period of at least five hours. The bedload trap consists of a concrete structure which contains a metal box with a capacity of 0.22 m 3 supported on top of a water pillow (MSC Survival), which records the increase by weight of the entering bedload and transmits it by a means of a pressure sensor (PTX-1730) to a Campbell CR1000 datalogger. Sediment capacity of each trap is around 330 kg (submerged weight). Recording interval is 5 minutes. Pit traps have been preliminary calibrated and fully operate since mid- November 2005, together with the rest of the instrumentation. A water temperature sensor will be installed during December Figure 2-8 displays photographs of the installation of the sediment transport station, with particular attention to the bedload pit trap. The specific design of the bedload trap was based on previous works done by Garcia et al. (2000). Besides permanent instrumentation, sediment transport devices will be deployed from the bridge above the station during floods to complement the automatic sediment transport records. Bedload will be measured by means of a 76 kg 152 mm-intake Helley-Smith Sampler and suspended sediment by means of a US DH-74 depth integrated sampler (for details see Vericat and Batalla 2005). Figure 2-8 Installation of the bedload trap at the Ribera Salada Watershed 11

12 2.2 Brazil The field monitoring program of the study areas in Brazil is carried out by the Federal University of Ceara, Brazil. Three nested experimental catchments have been selected in the north-eastern part of Brazil, as depicted in Figure 2-9: the Oros catchment and nested inside the Bengue Representative Basin and the Aiuaba Experimental Basin, all located within the Federal State of Ceara. Figure 2-9 Location of study sites in Brazil (Picture supplied by Benjamin Creutzfeldt) Aiuaba Experimental Basin The Aiuaba Experimental Basin has a size of 11.5 km² and is controlled by a reservoir with a present storage capacity of about 100,000 m 3. The climate in the watershed is typical of the Brazilian semiarid zone, with a mean annual precipitation of 430 mm, accompanied by a high interannual (as well as intra-annual) rainfall variability and a potential annual evaporation rate of 2,200 mm. The rainfall occurs in the rainy season between January and June. The mean annual temperature in the area is approximately 25 C. The natural vegetation of the basin is called caatinga. It is woodland with a mixture of trees and shrubs with a height of 3 to 7 m and a density which can vary from very high dense dry forests to almost desert sites, where bushes are isolated. Thorn-bearing species and xerophytes are frequent in the drier parts of the area. In most areas, farmers use the land for cattle breeding and for corn and beans crops, the most important economic activities in the region. The main geological unit is the Precambrian and Proterozoic crystalline basement. Soils on the crystalline basement tend to be shallow, clays and often contain a significant amount of rock fragments. 12

13 Existing monitoring data Rainfall gauges Within the Aiuaba Experimental Basin, three automatic rainfall gauges are installed. Each station is composed of one automatic pluviometer with resolution of mm, which computes rainfall every 5 min and 6 h, one soil humidity sensor (installed at a depth of 15 cm), which measures soil humidity every hour and one data-logger. Figure 2-10 left: rain gauge, right: class A pan within the Aiuaba Experimental Basin Close to one of the rain gauge, a class A pan is installed as depicted in Figure 2-10 for the measurement of the potential evaporation rate. This pan has an automatic ultra-sound level sensor, which measures hourly and a micrometer for manual measurements which are carried out once a day. Rainfall interception An experimental site (10x10 m 2 ) was constructed to study rainfall interception of vegetation (dense dry forest caatinga), close to one of the automatic rain gauge (50 m). Eleven Ville de Paris pluviometers (ten underneath the vegetation with random relocation every two weeks and one in an open spot) and sample bottoms for the collection of stemflow were installed to compute daily interception losses. Figure 2-11 shows the instruments within the experimental plot. Figure 2-11 left : Ville de Paris pluviometers, right : stemflow measurement as part of the interception study 13

14 River flow measurement A Parshall flume, connected to a pressure transducer and a data-logger, has been installed at the main river of the watershed, controlling a catchment area of about 9 km 2. Figure 2-12 Parsahll flume within the Aiuaba Experimental Basin Reservoir level station The change or water table level at the reservoir at the outlet of the Aiuaba Experimental Basin is measured twofold, as shown in Figure 2-13: first, an OTT Thalimedes Shaft Encoder Water Sensor connected to a data-logger is installed close to the outlet of the reservoir and measures the water level on an hourly basis. Second, four limnimetric rulers are installed at different locations within the reservoir and readings are taken by a technician once a day. Figure 2-13 left: automatic sensor support structure, right: limnimetric rulers inside the reservoir at the outlet of the Aiuaba Experimental Basin New monitoring scheme within the SESAM project A water-stage sediment sampler was installed in the summer 2005 inside the main stream of the basin, ca. 5 m downstream of the Parshall flume. The sampler enables the measurement of suspended sediments in the river water with sediment concentration being measured at different depths of water flow inside the river. Figure 2-14 shows photographs with, from left to right, the metal sampler holder in the dry, ephemeral riverbed, the box containing 15 sampling bottles, and the installed sediment sampler. Two similar samplers are under installation in the main rivers upstream the Bengue reservoir. 14

15 Figure 2-14 Automatic sediment sampler, left: dry riverbed, middle: sampling box, right: installation Bengue Representative Basin The Bengue Watershed has a size of ca. 933 km 2 and contains the Aiuaba Experimental Watershed. The main river of the watershed is the Umbuzeiro River which disembogues into the Bengue Reservoir. The Bengue Reservoir has a storage capacity of 19.6 million m 3 and floods an area of ca. 350 ha. The average inflow into the reservoir is about 30 million m 3 water per year, but since evaporation (2100 mm per year) and yearly inflow coefficient of variation (Cv = 1.2) are very high, the reservoir yields only 6.5 million m 3 /yr (with 90% guarantee level). According to Araújo et al. (2003), the sedimentation rate in Ceará for similar watersheds varies from 130 ton.km -2.yr -1 to 690 ton.km -2.yr -1, depending on land use, so that sedimentation expectation for the Bengue dam ranges from 1.4 to 7.3 million tons per decade, or from 1.2 to 5.6 million m 3 storage capacity reduction per decade. This means that, if erosion is not controlled, the dam could be lost in less than 40 years due to sedimentation. Climatic, soil and vegetation characteristics are as described above for the Aiuaba Experimental Basin. About half of the catchment is located in a steep area with elevation varying between 400 and 800 metres. Existing monitoring data In the Bengue watershed, there are ten rainfall gauges operated by the federal Water Resources and Meteorology Agency FUNCEME on a daily basis. Water level is measured inside the Bengue reservoir with a set of limnimetric rulers with a daily resolution by the federal Water Resources Management Agency COGERH. In addition, discharge data are available the overflow of the reservoir and for the pumping station for the water supply to the local municipality; all structures are operated by COGERH. New monitoring scheme within the SESAM project In 2005, a climate station was installed close to the Bengue Reservoir for the measurement of rainfall and climatic data with a high temporal resolution. Parameters comprise rainfall, temperature, wind velocity and wind direction, air humidity, radiation and evaporation. 15

16 Figure 2-15 Climate station close to the Bengue Reservoir Two water-stage sediment samplers of the same type as described above for the Aiuaba Experimental Basin were installed in the summer 2005 within the two major river reaches into the Bengue Reservoir. A map with the sampling locations is presented in Figure 4-28, and additional information on the choice of the locations can be found in Chapter Orós Watershed Orós, the second largest reservoir in Ceará is built at the Jaguaribe river, stores 1,940 million m 3, floods 35,000 ha and controls a catchment area of about 25,000 km 2 (about 1/6 of the State area). The Oros Watershed contains the Bengue and the Aiuaba Experimental Basins. Average rainfall is 860 mm/yr and runoff is restricted to only 7% of precipitation, i.e., 60 mm/yr. The reservoir yields about 630 million m 3 /yr with 90% guarantee level and is the most important water resource in Ceará: it supplies not only the most productive irrigation areas in the State (middle and lower Jaguaribe basin), but also the metropolitan area of Fortaleza, by means of a 120 km channel. A Basin Committee has been in place since 2000, which manages its water together with the State Water Resources Secretariat and COGERH. References Araújo, J.C.de, Fernandes, L., Machado Júnior, J.C, Oliveira, M.L.R. and Sousa, T.C. (2003): Sedimentation of reservoirs in semiarid Brazil. In: Gaiser, T., Krol, M.S., Frischkorn, H. and Araújo, J.C.de: Global change and regional impacts: Water availability and vulnerability of ecosystems and society in the semi-arid Northeast of Brazil. Springer-Verlag, Berlin Heidelberg, Germany, Garcia, C., Laronne, J. B., and Sala, M., 2000, Continuous monitoring of bedload flux in a mountain gravel-bed river: Geomorphology, v. 34, p Morgan, R.P.C, Quinton, J.N., Smith, R.E., Govers, G., Poesen, J.W.A., Auerswald, K., Chisci, G., Torri, D. & Styczen, M.E. (1998): the European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surface Processes and Landforms, 23,

17 Rius, J., Batalla, R. J., and Poch, R. M., 2001, Monitoring water and sediment yield in Mediterranean mountainous watersheds: preliminary results, in Mohtar, R. H. and Steinhardt, G. C., editors, Sustaining the global farm. Selected papers from the 10th International Soil Conservation Organisation Meeting, Purde University and USDA-ARS National Soil Erosion Research Laboratory: p Sanz Montero, M. E., Cobo Rayan, R., Avendano Salas, C., and Gomez Mantana, J. L., 1996, Influence of the drainage basin area on the sediment yield to Spanish reservoirs.: Proceeding of the First European Conference and Trace Exposition on Control Erosion. Schmidt, J. (1991): A mathematical model to simulate rainfall erosion. Catena Suppl., 19, Vericat, D. and Batalla, R. J., (2005). Sediment transport in a highly regulated fluvial system during two consecutive floods (lower Ebro River, NE Iberian Peninsula): Earth Surface Processes and Landforms, v. 30, p Valero-Garces, B. L., Navas, A., Machin, J., and Walling, D. E., 1999, Sediment sources and siltation in mountain reservoirs: a case study from the Central Spanish Pyrenees: Geomorphology, v. 28, p Verdú, J.M., Batalla, R.J., Poch, R.M. (2000): Dinámica erosiva y aplicabilidad de modelos físicos de erosión en una cuenca de montaña Mediterránea (Ribera Salada, cuenca del Segre). Pirineos, 155, Verdu, J. M., 2003, Analysis and modelling of the hydrological and fluvial response of a large mountainous Mediterranean catchment (Isabena River, Pre-Pyrenees): University of Lleida. 17

18 3 Modelling approaches within the SESAM project The objective of the SESAM project is to set up and validate a modelling system for the quantitative assessment of sediment production in catchments, sediment transport in the river system, and sediment retention in reservoirs. The focus is laid on meso-scale river basins (several hundreds to thousands of square kilometres in size) in dryland regions. An existing hydrological model, the WASA model, which has been adapted to specific environmental characteristics of semi-arid areas is being extended with components representing erosion and sediment transport processes. The WASA model (Model for Water Availability in Semi-Arid environments) was developed by Güntner (2002) and Güntner and Bronstert (2003, 2004) to enable the quantification of water availability in semi-arid regions. The original WASA model was programmed in Fortran 77 / 90 by Güntner (2002). It contained formerly only routines for the calculation of hydrological processes at the hillslope scale and did not allow sediment-transport calculations. A simplified approach for water transport in rivers using daily linear time-response functions and a basic approach of water retention in reservoirs were implemented and required improvement. Therefore, during the first project year, 2005, the existing model code has been extended extensively to include sediment-transport routines and to some extent modified watertransport approaches for the three conceptual levels of the WASA model: the hillslope scale, river scale and the reservoir scale with calculation of reservoir sedimentation. The hillslope component was extended to include sediment-transport processes using the MUSLE approach. The existing river routine of the WASA model of water flow was modified to include a spatially distributed, semi-process-based modelling approach for the modelling of water and sediment transport through the river network. Furthermore, the WASA model was extended to include a reservoir module that deals with the transport of water and sediment as well as sedimentation processes in reservoirs. The following sections give some preliminary information on the computational background and the functional structure of the corresponding routines for each of the three conceptual levels: hillslope, river and reservoir. 3.1 Hillslope modelling The hillslope module comprises the modelling of the hydrological and sediment-transport processes taking place on the hillslopes. Its output, consisting of water and sediment fluxes, is passed to the river module for further processing. The hydrological modelling accounts for interception, evaporation, infiltration, surface and subsurface runoff, transpiration and ground water recharge. The spatial conceptualisation of the WASA model is explained in great detail in Güntner (2002, Chapter 4), and are only shortly summarised. The following spatial modelling units may be identified for the characterisation of the hillslope components (Güntner 2002, p. 33): 18

19 Sub-Basins: ca km 3, georeferenced, location of gauging stations of river discharge, or large reservoirs with a storage capacity of more than 50x10 6 m 3 and of the confluence of major rivers; Landscape units (LUs): based on the LU concept (e.g. SOil and TERrain digital database, FAO, 1995), i.e. structure of the landscape according to geological, topographic and soil characteristics with similarity in major landform, general lithology, soil associations and toposequences, georeferenced; Terrain components (TCs): fraction of area of a landscape unit with similarity in slope gradients, position within toposequence (highlands, slopes and valley bottoms) and soil association; Soil vegetation components (SVCs): fraction of area of a terrain component, characterised by specific combination of soil type, and vegetation/land-cover class ; Soil profiles: descriptions of characteristic soil horizons. As a fist attempt, sediment generation on the hillslopes in the form of soil erosion by water is modelled using the MUSLE-approach (Williams, 1995). The MUSLE (Modified Universal Soil Loss Equation) is an empirical formula, based on the USLE equation, and is given by: Y = 11.8 Q surf q peak A TC 0.56 K USLE C USLE P USLE LS USLE CFRG where Y is the gross sediment yield [t], Q surf is the surface runoff volume [mm water/ha], q peak is the peak runoff rate [m 3 /s], A TC the area of the TC [ha], K USLE, C USLE, P USLE, LS USLE the USLE-factors and CFRG the coarse fragment factor (Neitsch et al., 2002). In contrast to USLE, the MUSLE approach incorporates the surface runoff Q surf that is produced by the hydrological routines in the computation of the energy component. This improves the sediment modelling performance by eliminating the need for a sediment delivery ratio (SDR) and implicitly accounts for antecedent soil moisture (Neitsch et al., 2002). In WASA, the amount of generated sediment is calculated for each terrain component, using a specifiable time step (currently one hour or one day). The use of high-resolution rainfall data is highly recommended for a sound calculation of the peak surface runoff. If only daily rainfall data is available, the maximum half-hour rainfall intensity, which is needed for the calculation of peak surface runoff, will be assumed as a specifiable fraction of the daily rainfall, making the computation somewhat uncertain. The peak surface runoff is computed with a modified rational method as described in detail by Neitsch et al. (2002). The computations use areal weighted averages of Manning s friction factor n of all SVCs within a terrain component. The computed value for generated sediment is then distributed among the particle size classes, according to the mean composition of the uppermost horizons in the terrain components. Any sediment mass Y TCin coming from upslope area is added to the generated sediment mass Y TC and completely mixed. The resulting sediment concentration Y TCout in runoff water is then compared to the transport capacity of the runoff flow for each time step and, if necessary, reduced. 19

20 Y TCout = min (Y TC + Y TCout, TCap Q surf ) The transport capacity TCap is estimated using either: (a) the unit-stream-power-based equation of Govers (1990) TCap = c (ω - ωcr)η where ω is stream power [cm/s], ωcr is critical value of unit stream power (= 0.4 cm/s), and c,η are particle size-dependent coefficients; or (b) from the maximum value that is predicted by MUSLE assuming unrestricted erodibility (K = 0.5): TCap = 11.8 Q surf q peak A TC C USLE P USLE LS USLE CFRG / Q surf where all parameters are described as above. Currently, no further particle size selectivity in erosion and deposition is by the model. As an alternative option for the computation of sediment production, an approach proposed by de Araujo (2005, in press) using entropy-based equations will be implemented. 3.2 River modelling When choosing an approach for modelling the complex, dynamic transport processes in the river system, one is always confronted with the choice between simple, linear regression or lumped model types and process-based, parameter-intensive models. Current expertise in both types of river process modelling is fairly limited for semi-arid and sub-humid regions and equally for dryland, ephemeral rivers. Within the SESAM project, it is proposed that three modelling approaches will be evaluated for their applicability to quantify water and sediment fluxes in rivers within semi-arid to sub-humid regions (Mueller SESAM working report, May 2005): A) Semi-process based approach with a direct implementation into the WASA code: Muskingum routing method for water and a sediment delivery ratio based on a power function of the peak stream velocity; B) Process-based hydrodynamic sediment transport model such as the HEC-6 model (Hydrologic Engineering Center); C) Neural network modelling. The usage of three very different modelling approaches enables a direct comparison of their applicability to model the transport processes by comparing simulated model output with observed water and discharge data of instrumented catchments. In addition, the various parameterisation approaches can be assessed and the amount and the availability of the input data can be evaluated. The process-based model requires the largest amount of input data, and 20

21 simulates the sub-processes of erosion, transport and deposition in great detail. In contrast, for the lumped routing approach, only few parameters are required for parameterisation, but it heavily relies on the calibration with measured water and sediment discharge data. The third approach based on neural networks is a very novel computational method that has hardly been applied to model sediment-transport processes. According to Yitian and Gu (2003) and Abrahart and White (2001), neural networks are potentially a very powerful tool for modelling nonlinear phenomena such as sediment transport. Description of model approaches: A. Semi-process based approach The existing river routing routine of the WASA model (Güntner 2002) of water flow was extended to include a spatially distributed, semi-process-based modelling approach for the modelling of water and sediment transport through the river network. The implemented modelling approach is similar to the routing routines from the SWAT model (Soil Water Assessment Tool, Neitsch et al. 2002) model and the SWIM model (Soil Water integrated Modelling, Krysanova and Wechsung 2000). The new river modelling approach provides a better process description of river transport processes than the original WASA water routing that was based on daily linear response functions (Bronstert et al. 1999). The new water routing is based on the kinematic wave approximation after Muskingum (e.g. as described in Chow et al. 1988). The Muskingum method is a commonly used channel routing method for handling a variable discharge-storage relationship. Combining the Muskingum storage with the continuity equation, the water inflow rate into a single channel segment is given for each time step by: qout,2 = C1 qin,2 + C2 qin,1 + C3 qout,1 where q in,1 is the inflow rate at the beginning of the time step (m 3 /s), q in,2 is the inflow rate at the end of the time step (m 3 /s), q out,1 is the outflow rate at the beginning of the time step (m 3 /s), q out,2 is the outflow rate at the end of the time step (m 3 /s) and C C C ( t 2KX) = 2K(1 X) + t ( t + 2KX) = 2K(1 X) + t (2K(1 X) t) = 2K(1 X) + t where t is the time increment (h), K is the storage time constant for each segment, X is a weighting factor having the range 0 X < 0.5. Flow rate, velocity and flow depth are calculated for each river stretch and each time step using the Manning s equation: 21

22 qs(t) = vs(t) As v R (t) = 2 / 3 s S n 1/ 2 where q s (t) is the flow rate in the channel segment s (m 3 /s), A s is the cross-sectional area of flow in the channel segment s (m 2 ), R s is the hydraulic radius for a given depth of flow (m), s is the slope given in (m/m), n is the Manning s friction factor (-), v(t) s is the flow velocity and t is time (h). A trapezoidal channel dimension with slope channel sides of 0.5 is used to approximate the river cross-sections. If water level exceeds bankfull depth, the flow is simulated across a predefined floodplain with slope channel sides of Sediment transport is modelled using the transport capacity concept. The maximum concentration of sediment that can be transported by the water is calculated as a function of peak flow velocity given by: q v (t) = s,peak s,peak A s (t) where v s,peak (t) is the peak channel velocity (m/s) and q s,peak (t) is the peak flow rate (m 3 /s). The peak flow rate is given by: q s,peak (t) = peak _ factor v s, peak (t) where v s,peak (t) is the peak rate adjustment factor. The maximum sediment concentration that can be transported in the flow is calculated using a power function of the peak flow velocity: Sed_ conc s,max = a v b s,peak where Sed_conc s,max is the maximum sediment concentration for each segment s in (ton/m 3 ), and a and b are user-defined coefficients. If the actual sediment concentration exceeds the maximum concentration, deposition occurs; otherwise degradation of the riverbed is calculated as a function of a channel erodibility factor. For each time step the final amount of sediment for each river segment is calculated: sed = sed sed + sed s,2 s,1 deposition degradation 22

23 where sed s,1 is the amount of suspended sediment in the reach (tons) at the beginning of the time step, sed s,2 is the amount at the end of the time step, sed dep is the amount of sediment deposited in the reach segment, and sed deg is the amount of sediment reentrained in the reach segment. The river module can be run with variable time steps. Transmission losses through riverbed infiltration and evaporation are accounted for. Unclear is at this moment how to implement the sediment reentrainment of suspended sediments from the riverbed for individual river segments. A disadvantage of the lumped approach is that is does not contain information on the particle-size distribution of the sediment fluxes. B. Process-based approaches (HEC-6) The HEC-6 model is a hydraulic and sediment-transport numerical model developed to simulate flows in rivers and channels with or without movable boundaries. The HEC-6 model is a hydrodynamic, one-dimensional open channel flow and sediment-transport model designed by the US Army Corps of Engineers to simulate changes in river profiles due to erosion and deposition over long time periods of several years or for single events (HEC-6 User Manual, 1993). The software is freely available, however not the source code, a fact which limits potentially needed modification of the computational methods. Water flow is simulated as steady flow: the continuous flow record is broken into a sequence of steady flows of variable discharge and duration. For each flow, a water surface profile is calculated thereby providing energy slope, flow velocity and depth at each cross section. Potential sediment transport rates are then computed at each cross section. One can choose between several sediment-transport equations. The shapes of individual cross sections are updated as a function of erosion and deposition for each reach. The sediment calculations are performed by grain size fraction thereby allowing the simulation of hydraulic sorting and armouring. The modelling studies with HEC-6 are planned to be carried out externally of the existing WASA code, i.e. the hillslope components are calculated with WASA and are fed separately into the HEC-6 software. C. Neural network modelling Neural network is a tool for nonlinear input-output mapping, which consists of a system of interconnected layers of hidden units commonly called neurons. A neural network is constructed to obtain a prediction of system response without attempting to reach an understanding of or provide insight into the nature of the involved processes, i.e. it is a blackbox model. The application of neural network to rainfall-runoff modelling is not new (see Yitian and Gu 2003 for a list of relevant literature), however, its application to sedimenttransport has not been employed extensively. According to the Yitian and Gu (2003), a river network can be represented by a system of interconnected nonlinear reservoirs. Abrahart and White (2001) have presented in another recent study the applicability of neural networks to the modelling of sediment transfer in dryland catchments. Upstream inflows and sediment loads as derived from the WASA model can be used as the model input and downstream discharges and sediment transport rates at a downstream river 23

24 gauging station provides the model output. The neural network modelling approach will be performed as a minor, less significant model study in comparison to the before described hydrodynamic and lumped approaches for the river modelling exercise of the SESAM- Project. 3.3 Reservoir modelling The reservoir sedimentation routine was included into the WASA model to enable the calculation of non-uniform sediment transport along the longitudinal profile of a reservoir and of the reservoir bed changes caused by deposition/erosion processes. Additionally, it is planned to implement routines for the simulation of sediment management scenarios that evaluate the potential reduction of sediment accumulation in reservoirs (to be implemented in the second year of the SESAM project). The simulation of reservoir sedimentation is based on a deterministic, process-based, one-dimensional modelling approach, named SEMRES Model (Mamede, submitted). In order to perform the simulation of sediment transport in reservoirs, four processes have to be considered: (1) water balance of the reservoir, (2) hydraulic calculations in the reservoir, (3) sediment transport along the longitudinal profile of the reservoir and (4) reservoir bed elevation changes. The computation of the water balance is based on the continuity equation, which considers all inflows, outflows and changes in storage of the reservoir. For the calculation of the hydraulic properties, the reservoir is divided into two spatial components: the river sub-reach component and the reservoir sub-reach component. In the river sub-reach, the hydraulic calculation is based on the method of successive approximations for a gradually varied flow (Graf and Altinakar, 1998), while in the reservoir sub-reach, a modelling approach adapted from the GSTARS Model is used (Yang, 2002). For the calculation of sediment transport in the reservoir, four different equations for the calculation of total sediment load were selected from recent literature. Finally, the reservoir bed elevation changes are calculated through the sediment balance in each cross section, taking into account three conceptual layers above the original bed material. The lowest layer is used for storage where the deposited sediments are compacted and protected against erosion. In the intermediate layer, the sediment can be deposited or re-suspended. In the top layer, the sediment-laden flow occurs. Water balance of the reservoir The water-balance module accounts for the interactions between various forms of water going into and out of the reservoir over daily intervals or shorter. The components of the reservoir water-balance model are shown in Figure 3-1. Inflow components include direct runoff from the tributary rivers (Q in ), groundwater inflow (Q gr ) and direct rainfall on the reservoir water surface (Q prec ). Outflows from the reservoir include daily withdrawals for water supply (Q ws ), daily withdrawals for sediment management (Q sm ), overflow discharges through the spillway (Q over ), direct evaporation from the water surface of the reservoir (Q evap ) and groundwater outflow by infiltration (Q inf ). However, groundwater inflow and outflow was assumed negligible relative to other inflow and outflow components. 24

25 River inflow Precipitation Evaporation Overflow Groundwater Infiltration Intake device Bottom outlets Figure 3-1 Schematic representation of the reservoir water balance components In general, the reservoir water balance can be written as showed in the following equation by assumption that the physical properties of the fluid are constant. V = Q in + Q prec + Q gr (Q evap + Q inf + Q over + Q ws ) The reservoir water balance computations used in the reservoir module of the WASA Model are summarized within each timestep as follows: a) given the initial water volume and the inflow discharge obtained from the river module of the WASA Model, determine the overflow discharges in the case that the storage capacity of the reservoir is exceeded by the actual storage volume after inflows. b) compute the storage volume reduction of the reservoir by evaporation. Evaporation values are calculated in the climate module of the WASA Model. c) determine the storage volume increase by rainfall directly on the reservoir. If storage capacity of the reservoir is exceeded by the actual storage volume after precipitation, overflow discharge is updated. d) determine the volume of water withdrawn from the reservoir for water supply. The outflow is a fraction of Q 90, i.e., the annual runoff from a reservoir which is provided with a probability of 90% (in 90% of all years). Q 90 is based on simple hydrological modelling for dryland environments (Güntner, 2004b). e) compute the volume of water withdrawn from the reservoir for sediment management proposes as a fraction of the maximum discharge released through the bottom outlets. This fraction is calculated by interpolation between the storage capacity of the reservoir and a minimum reservoir volume, which allows withdrawals through the bottom outlets, given by user. f) calculate the actual reservoir level by using of stage-volume curves. Hydraulic calculations in the reservoir The determination of hydraulic properties is a pre-requisite for sediment transport modelling in general. Here, the reservoir is divided in the river sub-reach component and the reservoir sub-reach component (see Figure 3-2), taking into account the variation of the reservoir level. Obviously, the length of the river sub-reach becomes longer for lower reservoir levels, whereas length of the reservoir sub-reach decreases. 25

26 For the definition of limits between the two spatial components, the normal depth of each cross section is calculated, not taking into account the existence of the reservoir, and then, it is compared to the depth related to the reservoir at the same cross section. If the value of normal depth is greater than the depth of the reservoir at the same cross section, it belongs to the river sub-reach. River subreach Reservoir subreach Inflow Outflow Stage (m) Distance (km) Figure 3-2 Longitudinal profile of the reservoir (division into river and reservoir sub-reaches) Reservoir routing The computation of reservoir routing is based on the modelling approach of the GSTARS Model (Yang, 2002), with some modifications. In the reservoir sub-reach, the water discharge is computed for each cross section from upstream to downstream using a weighting factor, which represents the influence of input discharges and output discharges on that cross section. In the GSTARS Model, the weighting factor is the reservoir s surface area represented by each cross section. Here, the weighting factor is calculated from the reservoir s volume represented by each cross section, as showed in Figure 3-3. The water discharge for each cross section is calculated as follows: Q j = Q in (Q in Q out ). j k= m v k where: v k is the fraction of reservoir volume represented by that cross section (v k = V k /V res ); V k is the volume represented by cross section k; V res is the reservoir volume; m is the first cross section belonging to the reservoir sub-reach; Q in is the input discharge; and Q out is the output discharge. Given the water discharge and water elevation at a cross section, its hydraulic parameters could be calculated. 26

27 River subreach Reservoir subreach Inflow Outflow Stage (m) Distance (km) Figure 3-3 Fraction of the reservoir volume represented by cross section 11 River reservoir routing For the calculation of the hydraulic properties at the river sub-reach, the method of successive approximations for a gradually varied flow (Graf and Altinakar, 1998) was selected. This method will give a water-surface profile, but a constant of integration must be supplied. In this case, the cross section of control is the first section at the reservoir sub-reach, which has a physical reality. Calculations must begin at the control section and proceed in the direction in which the control operates, i.e. from the control section to upstream. In its general form, the equation of motion was written as d dx (Q / A) dh Sf dx = S e where: Q is the water discharge (m 3 /s); A is the wetted surface (m 2 ); h is the water depth (m); S f is the slope of the bed (-); and S e is the slope of energy-grade line (-). The dynamic equation of motion is rewritten below for the computation of hydraulic properties at the river sub-reach between the running section and the downstream section: H H j Q 2 j+ 1 = Se(x j x j+ 1) + Kss + 2 2g A j 1 A 1 2 j+ 1 where: H = z + h + U 2 /2g elevation of the energy-grade line ( Se ) + ( S ) j e j+ 1 Se = average slope of the energy-grade line V n S e = average slope of the energy-grade line with respect to horizontal 4 / 3 R h x j x j+1 is the distance between the running section and the downstream section; V is the average flow velocity; n is the Manning s friction coefficient for the running section; R h is the hydraulic radius of the running section; Kss is a head-loss coefficient for a singularity as 27

28 caused by a change between two consecutive sections or other possible irregularities. It is internally set to 0.1 for contractions and to 0.3 for expansions. Reservoir sediment transport The sediment transport through the reservoir is calculated with basis on the concept of sediment carrying capacity. For that, four different equations were selected from the literature, as presented in Table 3-1. The criterion to select them was based on the fact that all four equations enable the calculation of bed load transport and suspended load transport. The sediment transport equations were developed for uniform sediment load. Nevertheless, they are frequently used for non-uniform sediment transport problems. Table 3-1 Sediment-transport formulae for the calculation of reservoir sedimentation and their limits of applicability Formulas Finest limit Coarsest limit (mm) Wu et al. (2000) silt 100 Ashida and Michiue (1973) clay 100 Tsinghua University (1985, IRTCES) all size classes Ackers and White (1973) clay 100 Wu et al. Wu et al. (2000) developed a fractional formula for bed load transport and a fractional formula for suspended load. The transport rate of the k-th fraction of bed load per unit width (q b,q ) can be calculated as follows: q b,k γ γ s 3 = Pk. φb,k 1.g. d k ( where: P k is the percentage of material of size fraction k available in the bed; γ and γ s are the specific weights of fluid and sediment, respectively; g is the gravitational acceleration; d k is the diameter of the particles in size class k; and f is the dimensionless transport parameter for fractional bed load yields. φ b,k n' τ = n τ b c,k 2.2 in which: τ c,k = (ρ s ρ). g. d k.θ c. ξ k critical shear stress (N/m 2 ) θ c = 0.03 critical Shields parameter (-) 28

29 0.6 P = dk P e,k Pm m= 1 dk + dm total exposed probabilities of particles d k (-) = dm P h,k Pm m= 1 d + d total hidden probabilities of particles d k (-) e,k φ b,k = P hiding/exposure factor (-) h,k k m τ b = ρ. g. h. S e bed shear stress (N/m 2 ) 6 d50 n' = 20 Manning s roughness related to grains (s/m 1/3 ) The suspended load transport rate of the k-th fraction per unit width can be written as: q s,k = P. φ k s,k γ s γ 1.g. d 3 k in which: 1.74 V s,k τ φ = 1 τc,k ωk fractional transport parameter (-) τ = ρ. g. R h. S e bed shear stress (N/m 2 ) 2 ν γ s ω k = g.d k d + settling velocity (m/s) k γ d k 6 [ ] (Te 15) (Te 15). 10 ν = kinematic viscosity (m 2 /s) Te is the temperature (ºC) The fractional sediment transport capacity for total sediment load (m 3 /s) is the sum of the bed load transport rate and the suspended load transport rate. C k = (q b,q + q b,q ). W where: W is the cross section width Ashida and Michiue Ashida and Michiue (1972) proposed a fractional bed load transport equation, as written below: ν q τ 1 τ. c,k b,k = 17.Pk.u e.d k. τe,k 1 k τ τ c,k k in which: 29

30 2 u τ k = shear stress (N/m 2 ) γ s 1.g.d k γ 2 e u τ e,k = effective shear stress (N/m 2 ) γ s 1.g.d k γ 2 c,k u τ c,k = critical shear stress (N/m 2 ) γ s 1.g.d k γ u * = g.r. shear velocity (m/s) h S e V ue = effective shear velocity (m/s) R h / d log τ50 d k 0.85.u c,50 if < 0.4 d 50 u c,k = critical shear velocity (m/s) log19 d k.u if d c,50 k d 50 log( ) d 50 γ s u c,50 = τc,50.g.d critical shear velocity for d 50 (m/s) γ τ c,k = 0.05 critical shear stress for d 50 (m/s) The suspended load transport is adapted from the method of Ashida and Michiue (1970), as proposed by Yang (2002) in the GSTARS Model. q s,k = C* V(e p.a e p.h e ). p p.a in which: f ( Ψ0 ) C* = Pk.N. F( Ψ0 ) concentration at a reference level (a=0.05h) Ψ0 1 2 ( 0.5Ψ0 ) f ( Ψ 0 ) = e 2π 1 2 ( 0.5Ψ ) F( Ψ0 ) = e dψ 2π Ψ0 6ωk p = κ.u.h 30

31 ωk Ψ 0 = 0.75u N = k = von Kàrmàn constant (-) Tsinghua University Method The empirical Tsinghua University Method (IRTES, 1985) was developed to calculate the transport capacity of flushing flows in reservoirs. No distinction was made between bed load and suspended load. The method is based on observations flushing in reservoirs in China, where the predominant practice is annual flushing and so relatively little consolidation occurs between flushing operations. Extrapolation to other reservoirs and conditions should be done with caution. Q s 1.6 Q S = Ω W where: Qs is the sediment transport capacity at the current section (ton/s); Q is the water discharge (m 3 /s); S is the bed slope; W is the cross section width; and Ω is a constant set from the sediment type: 1600 for loess sediments; 650 for other sediments with median size finer than 0.1 mm; 300 for sediments with median size larger than 0.1 mm; and 180 for flushing with a low discharge. Ackers and White Ackers and White (1973) proposed a formula to estimate the total load transport. The fractional sediment transport for fraction k is defined as: q t,k n' k V = Pk.K k.v.d k. u F. Fc gr,k gr,k. ξ k m' k in which: F gr,k 1 n' k n' k u V =. sediment mobility number (-) γ h s 1.g.d 32.log 10. k d γ k The coefficients n, m, K and Fc gr are dimensionless. These coefficients depend on the dimensionless particle size, calculated as follows: 31

32 d * k = d k γ s 1.g γ 2 ν 1/ 3 Ackers and White make a distinction between particles with 1 < d* < 60 and particles with d* 60. Later revisions were made for the K and m coefficients (HR Wallingford, 1990), because there were uncertainties in the original formula in the sediment transport for relatively fine and coarse sediments. Nevertheless, Van der Scheer (2000) showed that the results of the Ackers and White Formula with the modified parameters are slightly worse than the predictions of the original formula. Therefore, the 1973 Ackers and White Formula was selected. The parameters of that formula can be found in Table 3-2. Table 3-2 Coefficients of the formula proposed by Ackers and White (1973) Coefficients 1 < d* < 60 D* 60 n log(d*) 0 m' d* 1.5 K logd* (logd*) Fc gr d* Reservoir bed elevation changes For the calculation of bed elevation changes, each cross section is divided into three conceptual layers above the original bed material as illustrated schematically in Figure 3-4. The lowest layer is used for storage. There, the sediment is compacted and protected against erosion. In the intermediate layer, the sediment can be deposited or resuspended. In the top layer, the sediment-laden flow occurs. The sediment routing at the top layer is based on the conservation of sediment mass. Therefore, the sediment balance equation can be expressed as: [ S] j,k = [S in + S eros (S out + S dep )] j,k (3.9) where: S is the variation of the amount of suspended sediment (m 3 ); S in is the amount of incoming sediment into the reach (m 3 ); S eros is the amount of sediment resuspended in the reach (m 3 ); S out is the amount of sediment transported out of the reach (m 3 ); and S dep is the 32

33 amount of sediment deposited in the reach (m 3 ). All sediment inputs and sediment outputs are computed for each grain size and cross section. (a) Sediment laden flow Sediment laden flow (from cross section 6) (to cross section 8) Deposition Erosion Compaction Top Layer Intermediate Layer Lowest Layer Original Bed Material Stage (m) (b) 350 Top Layer 345 Deposition Erosion Original bed material Compaction Intermediate Layer Lowest layer 350 Width (m) Figure 3-4 Longitudinal view (a) and cross section (b) with the sediment balance for the three conceptual layers. The amount of incoming sediment at the first cross section of the reservoir is obtained from the River Module of the WASA Model. Erosion of the bed takes place when the sediment transport capacity at the current cross section exceeds the load incoming into the reach, whereas deposition occurs when the sediment transport capacity is exceeded by sediment inflow. Finally, the amount of sediment transported out of the reach is constrained by the sediment transport capacity in the reach. The sediment transport capacity is calculated for each grain size from one of the four available sediment transport presented above, assuming that the entire bed is composed of that size fraction alone. According to the GSTARS Model (Yang, 2002), the fractional sediment transport capacity can be written as: C j,k = [r.p j,k + (1 - r)p* j,k ]. C j,k where: P* j,k is the percentage of material of size fraction k incoming into the reach; P j,k is the percentage of material of size fraction k available in the bed; r is a weighting factor that enables the inclusion of incoming sediment into the reach (0 r 1); and C j,k is obtained from the sediment transport equations presented above. 33

34 The cross section bed is revised when deposition or entrainment at the intermediate layer take places. The area of deposited material or resuspended material is computed for each grain size as: [ ] A = dep [ ] j,k A = ero j,k [ S ] dep L j [ S ] ero L j j,k j,k in which: L j L* = j 1 + L 2 j length of the reach (m) where: L j is the distance between the current cross section and the downstream cross section (m) Finally, the sediment area at the intermediate layer varies with the incremented volume by deposition or the removed volume by entrainment. In the case of deposition, the cross section bed changes proportional to the area available for deposition, constrained by the water level, whereas for entrainment the cross section bed changes proportional to the area available at the intermediate layer, constrained by the inactive layer. Within the SESAM project it is planned to parameterise and validate sedimentation models for the Bengue Reservoir in the Brazilian study area and the Barasona Reservoir in the Spanish study area. 3.4 Summary The extension of the WASA model to include sediment transport processes at the hillslope, river and reservoir scale involved and continues to involve challenging programming efforts. Besides the technical implementation, a large amount of data is required for the parameterisation and testing phase of the model. The monitoring schemes on suspended load, bedload and turbidity as summarised in Chapter 2 are going to provide valuable data sources to test the model at different spatial scales. Monitoring schemes related to climate data will provide important input parameters for the hydrological model components. For the parameterisation of the model components, field data are yet required that describe the hydrological, soil-hydraulic and geomorphological characteristics of the selected dryland environments in Spain and Brazil. It therefore became necessary to carry out various fieldwork campaigns that investigated the hillslope-river-reservoir system from a mulitparameter angle, as is described in detail in the following Chapter. 34

35 References Abrahart, R. J. and S. M. White, Modelling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 26 (2001): Ackers, P. and White, W.R. (1973). Sediment transport: a new approach and analysis, Proc. ASCE, Journal of the Hydraulics Division, Vol. 99, HY11, pp Ashida, K. and Michiue, M. (1973). Studies on bed load transport rate in alluvial streams, Trans. Japan Society of Civil Engineers, Vol. 4. Chow, V. T., D. R. Maidment, L.W. Mays (1988) Applied Hydrology. McGraw-Hill International Editions. Civil Engineering Series. Singapore de Araújo, J.C. (2005, submitted): Entropy-based equation to assess hillslope sediment production Govers, G. (1987) Empirical relationships on the transport capacity of overland flow, Proceedings of the Jerusalem Workshop, Erosion, Transport and Deposition Processes, March April 1987, 25 IAHS Publ. No. 189, 45 63, Graf, W.H. and Altinakar, M.S. (1998). Fluvial Hydraulics Flow and transport processes in channels of simple geometry. John Wiley & Sons LTDA. ISBN Güntner, A. (2002). Large-scale hydrological modelling in the semi-arid North-East of Brazil. PIK-Report, Potsdam Institute for Climate Research, Germany. No. 77. Güntner,A. and Bronstert,A. (2003). Large-scale hydrological modelling in the semiarid Northeast of Brazil: aspects of model sensitivity and uncertainty, In E.Servat, W.Najem, C.Leduc, and A.Shakeel, editors, Hydrology of the Mediterranean and Semi-Arid Regions. IAHS-Publication 278 Güntner,A. and Bronstert,A. (2004). Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas, Journal of Hydrology 297: Güntner, A., Krol, M., Araujo, J.C., and Bronstert, A. (2004). Simple water balance modelling of surface reservoir systems in a large data-scarce semiarid region, Hydrological Sciences Journal 49: HEC-6 User Manual, US Army Corps of Engineers. Internet resource (accessed on ): IRTCES (1985). Lecture notes of the training course on reservoir sedimentation. International Research of Training Center on Erosion and Sedimentation, Sediment Research Laboratory of Tsinghua University, Beijing, China. 35

36 Krysanova,V., F. Wechsung (2000) SWIM (Soil and Water Integrated Model) User Manual, Version: SWIM-8. Internet resource (accessed on ): Morgan, R. P. C., Quinton, J. N., Smith, R. E., Govers, G., Poesen, J. W. A., Auerswald, K., Chisci, G., Torri, D., Styczen, M. E., Folly, A. J. V. (1998): The European Soil Erosion Model (EUROSEM) Documentation and user guide, Cranfield University Müller, E.N. (May 2005) Water and sediment fluxes through the river system: modelling and measurement. SESAM Project Working Paper, University of Potsdam Neitsch, S.L., J.G. Arnold, J.R. Kiniry, J.R. Williams, K.W. King (2002): Soil and Water Assessment Tool. Theoretical Documentation, Version Published by Texas Water Resources Institute, TWRI Report TR-191 Williams, J.R. (1995). Chapter 25. The EPIC Model. p In Computer Models of Watershed Hydrology. Water Resources Publications. Highlands Ranch, CO. Wu, W., Wang, S.S.Y. and Jia, Y. (2000), Nonuniform sediment transport in alluvial rivers, Journal of Hydraulic Research, Vol. 38, No. 6, pp Yang, T.C. and Simoes, F.J.M. (2002) User s Manual for GSTARS3 (Generalized Sediment Transport model for Alluvial River Simulation version 3.0). U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, Colorado Yitian, L. and R. R. Gu. Modeling flow and sediment transport in a river system using an artificial neural network. Environmental Management 31 (2003):

37 4 Fieldwork campaigns within the SESAM project The previous chapter showed that data on hydrological, geomorphological and vegetationrelated properties are required firstly, for the parameterisation of the models, and secondly, for the testing of the modelling results. Besides the monitoring schemes that were installed at the various study sites and were already described in detail in Chapter 2, the existing water, soil and vegetation database proved insufficient for immediate model application, and thus made additional data collection necessary. The sections in this chapter contain brief descriptions on the fieldwork campaigns that were carried out by researcher and MSc students within the SESAM project in The articles describe the scope of the field study, the precise locations at one of the SESAM study sites in Spain or Brazil and the employed methodology. Furthermore, they contain some preliminary results of the field studies, such as a statistical analysis of numeric field data. A thorough analysis and discussion of the studies is still in progress and will be presented at a later stage of the SESAM project. Field campaigns in Spain comprised studies on hydrological and geomorphological characteristics on hillslopes and a study on badlands within the Esera and the Isabena Catchments, a detailed river survey along the Isabena River, and a sedimentological characterisation of the Barasona Reservoir. In Brazil, two field campaigns were carried out: one on the sedimentation characteristics of the Bengue Reservoir and the other on the characterisation of land-cover and terrain properties in the Bengue Experimental Basin. 37

38 4.1 Measurement of hillslope parameters related to water- and sedimentexport in the Esera Watershed, NE Spain by Till Francke and Sascha Wichmann Period of time: Scope of the field study A fieldwork campaign was carried out to gain an enhanced understanding of the hydrological and geomorphological parameters in the Esera Watershed that are required for the parameterisation and validation of the hillslope components of the WASA model. The objective of the field campaign was twofold: the first focus laid on the collection of soil hydraulic parameters and related properties, especially soil hydraulic conductivity, as discussed in the following section. The second focus was laid on the application of Cs radioisotopic methods to derive validation data for the testing of simulated sediment budgets of the WASA model, as described in more detail in Section 3.1. Background In contrast to parameters like elevation or vegetation cover, which can be acquired for a whole area using remote sensing techniques, such methods are still unavailable for soil hydraulic parameters. Since extended literature data on this subject is also scarce, field measurements have to be conducted. The WASA model employs a semi-distributed representation of the modelling domain, i.e. that parameters do not have to be supplied for every single location of the catchment, but for representative subregions within it. This concept should allow restricting field measurements to characteristic and dominant formations of the catchment. For a first attempt to determine representative landscape units (LUs), the LUMP algorithm (Francke, 2005) was applied to the entire Esera catchment using topography and geology as attributes. The number of LUs to produce was limited to 3. For each LU, the 3 most representative catenas (i.e. those closest to the cluster centre, see Figure 4-1 and Figure 4-3 b) were selected. The easiest accessible of the 3 of each class was visited and considered representative for the delineated parts of the catchment. Additional sample sites (Figure 4-1) were chosen with respect to local lithology to cover the major lithological formations. Due to time constraints it was also not possible to conduct infiltration experiments in all lithological formations. Study area As it is planned to simulate the entire Esera and Isabena Catchments, parameterisation data are required for the entire watershed. Figure 4-1 shows a map of the watersheds with the sampling locations of the soil-hydraulic parameters (blue dots and crosses) and of the Cs sampling (red square). 38

39 Figure 4-1 Map of the Esera Catchment with the sampling locations of the infiltration study and the radiotracer study A. Measurement of soil hydraulic parameters for the hydrological parameterisation of WASA model Methodology For soil hydraulic measurements, a hood infiltrometer (UGT Müncheberg) was used. The choice of the method measuring device was done under the following premises. Most important was to ensure a minimal disturbance of the soil matrix, therefore only in situ experiments seemed to be practicable. Furthermore, water consumption should be held at a minimum due to scarce water availability in the field. Using the hood-infiltrometer, the infiltration takes place from a closed hood that is filled with water. The hood is connected to a Mariotte apparatus. This kind of water-supply allows a steady state flow while a constant hydraulic potential at the boundary layer between the soil and the water in the hood can be ensured. The potential applied should always be equal or less than zero. Relevant parameters to be recorded are infiltration period (time), volume of the infiltrated water, hydraulic potential at the boundary layer and area of the boundary layer, i.e. size of the hood. To be able to calculate saturated soil hydraulic conductivity, measurements under at least two different potentials are necessary. Ideally, the two potentials are very close whereas one is equal zero. The saturated soil hydraulic conductivity can be calculated by using equations proposed by Gardener (1958) and Wooding (1968). 39

40 Figure 4-2: Hood infiltrometer measurement at sample site in the north-eastern part of the Isabena catchment Besides hood infiltrometer measurements also soil samples of a defined volume (100 cm 3 ) were collected with a ring cylinder at most sample sites for bulk density and soil texture analysis. For soil texture analysis, two methods were applied wet sieving and laser diffractometer analysis. It should be noted that the sieves used for analyzing grain size distribution have had a mesh size according to the German standard (mesh sizes of 2000, 630, 200 and 64 µm). Knowing grain size distribution and bulk density, soil hydraulic conductivity can be estimated using transfer functions. The model applied here is the ROSETTA model, which is a based on the UNSODA soil data base and equations by van Genuchten (1980) and Mualem (1976). Since the model requires soil texure classes according to the USDA convention, the measured values had to be adjusted. This was done by linear interpolation between the known pairs of the sum function of the grain size distribution. For comparison the model was supplied with soil l texture according to both, the USDA and the German convention. During the field campaign, in total 40 measurement series were conducted on 22 different sites whereas 11 series were conducted at depths between 21 and 40 cm. 36 measurements were conducted at the soil surface. In three cases, it was necessary to level the surface by carefully removing the topmost soil (2-3 cm), due to the deep slope of the sample sites. Soil samples were collected at 38 of the measurement sites. Due to inaccessibility in situ measurements were only possible at two out of three catenas. For the inaccessible catena in situ measurements at an alternative sample site were conducted. 40

41 Esera Badlands Isabena B A Barasona Reservoir (a) Figure 4-3 (a) l Overview of the Esera and Isabena catchments (b) landscape units computed with LUMP and sample sites (red) A,B: Catenas investigated (b) Catena A Catena B Figure 4-4 Infiltrometer measurements at catena A and B 41

42 Figure 4-5 Infiltrometer measurements on a characteristical badland region in the Esera Catchment Results In the following, the estimations of saturated soil hydraulic conductivity are based on both, hood infiltrometer measurements and the ROSETTA model. Furthermore, we will have a look on soil texture, since it gives a first general impression at what magnitude soil hydraulic conductivity should be expected to vary and, by including this, a general idea about soil properties of the study area. Soil texture classes for the soil sample analysed reach from sandy loam to silt loam (according to the United States Department of Agriculture convention). However, some are also classified as loamy sand and one with more than 30 % clay can be classified as silty clay loam. In Figure 4-6, it can be seen that soil samples collected from deeper regions have a slightly higher clay percentage than the samples take from the ground surface. However, all in all do both graphs look very similar and do not really reflect any dependency between soil properties and soil depth. Figure 4-6 Soil texture triangle for (a) depth between 0 and 5 cm and (b) depth between 21 and 45 cm 42

43 For numeric values of the statistical properties see table 1. measured: estimated values from all hood infiltrometer measurements, ROSETTA: estimated values from the ROSETTA model, d= 0-5 cm: estimated values from hood infiltrometer measurements conducted in depth between 0 and 5 cm, d=20-45 cm: estimated values from hood infiltrometer measurements conducted in depth between 20 and 45 cm Figure 4-7 (a) box plots of saturated soil hydraulic conductivity in mm/s (range=1,5), (b) scatter plot for the decade logarithm of soil hydraulic conductivity Taking a closer look at saturated conductivity, Figure 4-7 and Table 4-1 gives a different impression. There a decrease of values for k f where greater depths can be observed as usually expected and already noticed during the field work. Soil hydraulic conductivity seems to be generally very low. Values reach from magnitude 10-4 to 10-6 m s -1, but most samples lie at a magnitude of 10-5 m s -1. Interestingly, the values predicted with ROSETTA are significant lower than those conducted from infiltration measurements. This might be attributed to the fact that models usually work under the presumption of a homogeneous soil matrix. Assuming a homogeneous matrix, however, neglects that the absence or existence of coarser pores (i.e. pores are coarser than a mean size) is of greater relevance for the magnitude of hydraulic conductivity than the absence or existence of finer pores. Furthermore, channels connecting pores as they are usually created by bioturbation or cracks due to shrinking are disregarded under this assumption. Furthermore, although a positive relationship between the results of the two methods can be observed (see Figure 4-2b) with R 2 = 0.36 (Pearson Correlation), it is not very strong. On one hand this may just reflect that there is not only a heterogeneity in pore sizes but also in pore size distributions, grade of bioturbation and/or shrinking processes, i.e. heterogeneity on several spatial scales. On the other hand, one might argue that this is a result of a too small sample size. When investigating the samples of different catenas separately, it must be noted that this can only be done on a base of low sample sizes. Comparing catena A and catena B we can not notice a significant difference, although the soils on catena A seem to have of slightly lower hydraulic conductivities. However, in comparison to badlands the hydraulic conductivity at the sample catenas are significant lower. 43

44 Table 4-1 Statistical properties for saturated soil hydraulic conductivity Statistical parameter measured [mm s -1 ], n=39 ROSETTA [mm s -1 ], n=38 d = 0-5cm [mm s -1 ], n=28 d= 20-45cm [mm s -1 ], n=11 Minimum st Quartile Median Mean nd Quartile Maximum lower limit of conf. interval upper limit of conf. interval n: sample size, measured: estimated values from hood infiltrometer measurements, ROSETTA: estimated values from the ROSETTA model, n: sample size, d= 0-5 cm: estimated values from hood infiltrometer measurements conducted in depth between 0 and 5 cm, d=20-45 cm: estimated values from hood infiltrometer measurements conducted in depth between 20 and 45 cm Table 4-2 Statistical properties for saturated soil hydraulic conductivity for some sample sites - estimated values from hood infiltrometer measurements, n: sample size Statistical parameter Catena A, n=4 Catena B, n=6 Badlands, n=3 Mean 0,0024 0,0040 0,0139 Standard deviation 0,0016 0,0022 0,0059 n: sample size, *Only one measurement was conducted on actual badlands, i.e. bare soil (k f = m s -1 ). The other two measurements were conducted on nearby sites that had been still covered by vegetation (k f = and 0.157m s -1 ) Discussion and conclusion The results of the infiltration measurements with respect to the relatively small sample size give a good idea about hydraulic properties of the soils within the study area. However, in order to give a more substantiated view over spatial differences, a much higher amount of measurements would be necessary. This applies especially, if one wants to quantify the parameters at locations with extreme conditions relatively to the average of the study area. Regarding the estimations of soil hydraulic properties by ROSETTA, the prediction could not be improved by adjusting the results obtained from soil analyses, i.e. soil texture, to the USDA convention. As it was shown, the measurement results are generally underestimated by ROSETTA. A prediction from soil texture and bulkdensity is however possible, using a linear correlation of the measured and predicted values. Nevertheless, it also clearly shows that predictions of modells can not replace infiltration experiments. For future field campaigns dealing with hood infiltrometer measurements, it might be advisable to do several measurements at one sample site or sample sites close to each other in one day, due to the time intensive installation of the devices. 44

45 B. Pilot study for the usage of radiotracers to asses soil erosion rates In WASA, the calibration and validation of water- and sediment-production and -transport in the three main modules hillslope, river and reservoir should be done independently in order to locate deficiencies in the module that produces them. But whereas the measurement of discharge and sediment concentration in rivers or reservoirs is rather straightforward, the relevant variables in the hillslope-processes are difficult to obtain. This applies to both the hydrological (e.g. surface and subsurface runoff, groundwater recharge) and the sediment transport variables (sediment fluxes). Firstly, difficulties arise because of the areal extent of the modelling domain (e.g. in contrast to the river network which can be considered a linear structure). Secondly, the intermittent nature and their high spatial and temporal variability complicate the measurement of the above mentioned variables. In order to acquire data of the erosive processes, it was sought to determine mean erosion rates at selected sites. They can provide a meaningful number that allows model calibration or validation. Being mean rates, these values are less susceptible to single events which may display extreme behaviour in erosion events. This also means, however, that these average erosion rates do not allow a direct assessment of erosion rates on a shorter timescale, which may differ significantly. Major focus during this campaign consisted of checking the applicability of radioisotopic methods for the measurement of erosion rates under the given conditions with the available equipment. Methodology Long-term (i.e. ~ a) erosion rates can be assessed using anthropogenic Cs-137 or natural Pb-210 fallout as a tracer. The computation of the erosion rates essentially bases on a comparison between the sampling site and a (supposedly) non-eroded reference site. At an undisturbed site, the concentration usually declines exponentially with depth. Sites that experienced soil loss show a shortened concentration profile and a lower inventory (integral of concentration along depth), whereas deposition sites feature an augmented Cs-137 (and Pb- 210) inventory compared to the reference sites (e.g. He & Walling, 1997). The locations of the sampling points for the radiotracer study were already given in Figure 4-1. For each soil profile, the concentration of the respective radioisotopes in different layers of the soil had to be determined. Such soil samples are commonly collected with soil corers (e.g. Basher, 2000; Panin et al., 2001). Lacking a soil corer and due to the partially high stone content in the soils the sampling was done similar to Bujan et al (2000) or Bonniwell et al. (1999) by cutting blocks with a shovel and a spatula. The depth increment increased from 2 cm at the surface to 10 cm in the lower layers. Soil profiles were sampled at 14 locations (5 reference sites, 5 erosion profiles, 4 deposition profiles) with 4-8 depth layers each resulting in 98 soil samples overall. All samples were oven dried and sieved to 2 mm (Pennock & Appleby, 2002). At least 21 days in a sealed container ensured equilibrium of any in-situ produced daughters of Ra-226. The Gamma Spectrometry using a HPGe-detector at the department of Physics, University of Potsdam is still in progress. Counting times are in the order of 24 h. Alternatively, the application of a scintillation counter was tested to reduce counting times. 45

46 Results and discussion So far, 20 samples have been processed, resulting in two completely analysed soil profiles and some additional values. After energy-calibration of the detector with a Cs-137 standard, differences in at the Cs-137 peak (662 kev) were clearly visible among the samples (Fig. 2). The running mean (over 100 channels) is used for noise reduction. A second peak in the differences appears near 60 kev. Whether this peak can be credited to the difference in Pb- 210 (actually at 47 kev) must still be verified. Therefore, the preliminary interpretation is limited to Cs-activities. No absolute activity-calibration has been performed yet, so relative activities are used. Integrating the counting rates under the Cs-137-peak yielded a relative Cs- 137 activity, as depicted in Figure 4-9. difference in counting rates (surface sample - deep sample) 10,0 8,0 6,0 difference in counting rates running mean (100) difference counting rate 4,0 2,0 0,0-2,0-4,0-6,0-8,0-10,0 0,0 200,0 400,0 600,0 800,0 energy [kev] Figure 4-8 Differences in counting rates between a sample at the surface and another at a depth of 23 cm relative Cs-137-activity depth [cm] I. Cs, 016 Cs, 210 I-S-II, Badlands Z2-S K06 Figure 4-9 Relative Cs-137 activity of soil samples at selected sites and depths 46

47 The Cs-activity profile of site Z2-S decreases exponentially with depth. This meets expectations because Z2-S was collected as a reference site. It must be concluded that approx. 120 relative units mark the background noise, indicating a Cs-concentration below detection limits. The sample I-S-II, collected from an active badland surface, shows an activity only slightly above that limit. Presumably, erosion rates on the badlands are so high that the entire Cs-137-marked layer has been removed already, which renders the Cs-technique useless for badland areas. The profile of K06 (arable site) suggests little variance with depth, as a result of the mixing effects of ploughing. No drop to background Cs-level with depth was recorded, which indicates that sampling did not extend below the ploughing horizon. The measurements using the scintillation counter yielded a high-noise signal. Neither the resolution energy nor activity of the HPGe-detector was matched, proving the scintillation counter unsuitable for the purpose. The preliminary results indicate the applicability of the Cs-technique for erosion assessment. However, various problems were encountered. Especially digging the sample pits and retrieving the soil columns took considerable time because of the hard soil and stoniness. Bulk-sampling and / or using a corer would be favourable. No Cs-137 could be detected in the sample from the badland area. The technique seems unsuitable for these parts of the catchment. Although at eroded and undisturbed sites the Cs-concentrations decrease to background level within the upper 20 cm, sampling at ploughed sites or where deposition is to be suspected must include deeper layers. Erosion patterns seem highly heterogeneous in the field. It remains questionable to what extent a limited number of sampled sites can capture the overall erosion characteristics within highly variable slopes. Choosing appropriate reference sites remains challenging, considering the extensive land use changes within the study area during the past decades. Thus, an apparently undisturbed site may have been arable land in the not so distant past. This must be validated, e.g. with the available orthophotos of References AG BODEN (1994), Bodenkundliche Kartieranleitung, 4th edition, Hannover (B.-Anst. Geowiss. u. Rohstoffe u. Geol. L.-Ämter B.-Rep. Dtld.). Allué, J. L. (1990), Atlas Fitoclimático de España, Taxonomías, Instituto Nacional de Investgaciones Agrarias, scale 1: , Ministerio de Agricultura, Pesca y Alimentación, Madrid. (01/09/2005) Basher, L.R. (2000). Surface erosion assessment using 137Cs: examples from New Zealand. ACTA GEOLOGICA HISPANICA, v. 35 (2000), n 3-4,p Bonniwell, E. C., Matisoff, G., Whiting, P. J. (1999). Determining the times and distances of particle transit in a mountain stream using fallout radionuclides. Geomorphology, 27,

48 Buján, A., Santanatoglia, O. J., Chagas, C., Massobrio, m., Castiglioni, M., Yáñez, M. S., Ciallella, H., Fernández, J. (2000). Preliminary study on the use of the 137Cs method for soil erosion investigation in the pampean region of Argentina. ACTA GEOLOGICA HISPANICA, v. 35 (2000), nº 3-4,p Francke, T. (2005). Spatial discretisation in semi-distributed hydrological modelling with WASA using the Landscape Unit Mapping Program (LUMP). Internal working document, SESAM-project Gardener, W.R. (1958), Some steady state solutions of unsaturated moisture flow equations with application to evaporation from a water table, published in Soil Science 85, , 51 Güntner, A. (2002). Large-scale hydrological modelling in the semi-arid North-East of Brazil. PIK-Report, Potsdam Institute for Climate Research, Germany. No. 77. He, Q., Walling, D.E. (1997). The Distribution of Fallout 137 Cs and 210 Pb in Undisturbed and Cultivated Soils. Appl. Radiat. Isot. Vol. 48, No. 5, pp Mualem, Y. (1976), A new model predicting the hydraulic conductivity of unsaturated porous media, Water Resources Research 12, Nemes, A. et. al. (1999), UNSODA, Version 2.0, U.S. Salinity Laboratory, Panin, A.V., Walling, D.E., Golosov, V.N. (2001). The role of soil erosion and fluvial processes in the post-fallout redistribution of Chernobyl-derived caesium-137: a case study of the Lapki catchment, Central Russia. Geomorphology 40, Pennock, D.J., Appleby, P.G. (2002). Sample processing. In: Handbook for the Soil Erosion and Sedimentation Using Environmental Radionuclides, Zapata F. (Ed.). pp Kluwer Academic Publishers, Dordrecht, The Netherlands. Schaap, M.G. (1999), ROSETTA Model vers. 1.0, U.S. Salinity Laboratory, < UGT (2004), Bedienanleitung Haubeninfiltrometer, Müncheberg (Germany) van Genuchten, M.Th. (1980), A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Am. J. 44, Wooding, R.A. (1968), Steady infiltration from a shallow circular pond, published in Water Resources Research 4,

49 4.2 Fieldwork campaign for the remote sensing based characterisation of land-cover and terrain properties of the Bengué watershed in semiarid northeast of Brazil by Benjamin Creutzfeldt Period of time: Scope of the field study The aim of the field study is the characterisation and classification of land-cover and terrain properties for the modelling of hydrological and erosion processes of the Bengue watershed in the semi-arid Northeast of Brazil. In developing countries, like Brazil and especially the North-east of Brazil, the existing data basis for environmental studies is generally poor and therefore it was necessary to collect data for the modelling of hydrological and erosion processes. Due to the medium scale of the catchment with an area of 1062 km 2, the appropriate approach to determine the land-cover and terrain properties is by remote sensing, whereby medium resolution satellite images such as ASTER data were considered to be suitable for the study. A preliminary quantitative description of the land-cover was done by a land-cover classification and by the determination of different vegetation indices of the satellite images. The quantitative description of terrain properties such as slope and profile was done by derivatives of a digital elevation model. The classification of the landscape followed the approach of landscape units as defined by Güntner (2002), a spatial discretisation concept that is used for the parameterisation of the WASA model. For remote sensing studies, it is nearly always necessary to support the derived classification with ground sampling data. Therefore, the field campaign was conducted to support and validate the derivation of the terrain and land-cover properties, of the landscape units and of the vegetation indices. Methodology For all mapping purposes in the field, a mobile GIS tool kit was used, existing out of a Trimble GeoXM GPS receiver and a field computer combined with the software ArcPad 6.0 from ESRI. For each theme, a shape file with an input-form were created using ArcGIS 9.0 and the ArcPad 6.0 Application Builder. A mobile GIS kit has several advantages, as it allows easily an orientation in an unknown area, a very fast mapping of different themes with a predefined input-form and the direct use of satellite images during the mapping process. All mapping were done using the projected coordinate system UTM Zone 24S. The first task of field campaign was to gain an overview of the Bengue catchment, whereby a few Ground Control Points (GCPs) were collected to roughly georeference satellite images to support the mapping. Based on the overview and a literature review, a classification of different land-cover types was carried out. Based on the preliminary classification, Ground Truth Data (GTD) were mapped for the supervised land-cover classification, whereby for each class a rough estimation of some vegetation indices were done (like vegetation height or fractional vegetation coverage). For the exact georeferencing process and for the Digital 49

50 Terrain Model (DTM) creation more GCPs were collected. A qualitative classification of the Bengue catchment into different landscape units were performed, whereby for each landscape unit, representative hillslope profiles were selected and mapped and soil samples were taken. The particle size distribution of the soil samples were estimated by the soil laboratory of the Universidade Federal do Ceará (UFC). The soil texture had to be converted from the Brazilian grain size distribution to the German one, using a simple, linear interpolation method. Results During the field campaign around 650 georeferenced picture were taken, 67 Ground Control Points and around 180 Ground Truth Data were gathered. In addition, for 15 slope profiles the terrain and the land-cover attributes were mapped. For six different hillslopes profile, 32 soil samples were collected and the soil texture was analyzed. The map in Figure 4-10 depicts the locations of the GCPs, GTDs, the profiles and the soil sampling locations. The derivation of a land-cover classification for the entire Bengue Watershed on the basis of the results of this field campaign in combination with remotely sensed imageries is in progress. Figure 4-10 Location of sampling collection in the Bengue watershed from the field campaign April- May

51 4.3 Derivation of representative river stretches: A field survey of a mountain river in the Pyrenees, the Isabena River in NE Spain by Eva Nora Mueller Period of time: Location: Isabena Catchment, Huesca, Aragon, Spain (ca. 80 km north of Lleida) Purpose of the field study: To enable the parameterisation of process-based river transport models, a spatial distributed data set on water flow and sediment model input data were collected for ten river stretches along the Isabena River ranging from very narrow, turbulent mountain to very wide, shallow lowland stretches. Background: The mountain area in the lower Pyrenees in north-east Spain may be characterised by a heterogeneous spatial distribution of river reach forms and properties. Steep, narrow, deep incised mountain streams with rocky, gravely riverbeds in the upper parts of the catchment alternate with shallow, plain and very wide riverbeds with silty riverbed materials in the lower catchment areas, as depicted in Figure Figure 4-11 Isabena river (right) in the upper part of the Pyrenees, (left) in the lower catchment area These spatial variations of river stretch characteristics have a significant impact on the modelling of water and sediment fluxes with the process-based and the lumped approaches and require consideration in the model parameterisation procedure. Quantitative estimations on the spatial distribution of these parameters are limited due to the lack of field measurements and spatial field survey. Although a few riverbed characteristics are available for a number of river stretches of the Isabena River by Verdu (2003), the existing information do not suffice to characterise all principal river stretches in the 1000 km 2 basin. For the parameterisation of the river module within the WASA modelling framework, large spatial variations of input parameters are expected, specifically for the cross-sectional profile, the gradation of the riverbed material, slope and potentially for roughness, deposition and erodibility properties. 51

52 Lately, field and modelling studies at the hillslope to catchment scale experienced a shift from single point measurements to spatial field data sets (Mueller 2004). This circumstance was derived from the need and the trend to collect data sets on hydrological and geomorphological characteristics that correctly describe a two dimensional space and that avoid artefacts and error sources introduced by spatial scaling effects. It appears then only appropriate to derive the input to river routing modelling from an equally distributed spatial field data sets on river stretch characteristics. To enable the spatial characterisation of river reaches, two state-of-theart river classification systems are employed, to characterise the spatial variability in a watershed: the descriptive classification scheme after Rosgen (1994) and the more processbased classification scheme after Montgomery and Buffington (1997). Prior to the field survey, ten river stretches were selected in the Isabena Catchment that showed distinctively different characteristics in regard to their geology, the slope as derived from a digital elevation model, the sinuosity and the river bed as derived from aerial photography. In this way, it was expected to identify representative river stretches, similar to the concept of the WASA model, whose spatial discretisation is based on representative toposequences (currently developed by Till Francke) and characteristic landscape units (Güntner 2002) at the hillslope scale. The sampling locations of the ten field sites are given in Figure The total number of field sites was to some extent restricted by financial and temporal constraints. Figure 4-12 Isabena and Esera Watershed with sampling locations of the river survey 52

53 Methodology: The river survey at each of the ten stretches comprised the collection of data that are required for the parameterisation of a process-based spatially distributed river transport model. Data for the following parameters were collected: Detailed cross-section and longitudinal slope profiles Flow velocity measurements for the determination of Manning s n Riverbed gradation Vegetation type and cover inside and surrounding the river stretch Water temperature Water samples for suspended sediment concentration Oxygen content The employed field methodology is summarised in Figure River field work methodology: Field survey for one river stretch 1. Survey with high-precision total station a) Cross-sections: include descriptions on current water depth, bankfull depth and floodprone area b) Longitudinal height profiles for slope estimates (ca channel widths in length) 2. Measurement of flow velocity with a OTT C2 small current meter (as described by Harrelson et al. 1994): a) At least measurements per stretch, ca. all cm b) Minimum of 40 seconds for each reading c) Measurement depth: 0.6 times the total depth 3. Riverbed gradation (as described by Harrelson et al and Kondolf et al. 2003) a) Photographic method at 1 3 points with scale in picture; collection of soil samples of finer sediments b) Pebble count: sampling of 100 locations, randomly, measurement of the intermediate axes (if distinctly different, homogenous facies exist, each facie was sampled individually) 4. Characterisations of river reach: a) GPS location b) Classification according to Montgomery & Buffington (1997) c) Classification according to Rosgen (1997) d) Determination of dominant vegetation, vegetation surface cover estimation in % e) Water temperature and oxygen content with a multi-sensor device f) Water samples of 0.5 l for suspended sediments Figure 4-13 Methodology of field work Results The following paragraphs summarise some of the results of a preliminary data analysis. Figure 4-14 compares the form and extent of the cross-sections from the upland mountains river stretches to the lowland, alluvial parts. Figure 4-15 displays the topographical properties (slope and altitude) at the ten river sampling locations along the Isabena River over a distance of approximately 60 km. Figure 4-16 shows the hydrodynamic properties; note that the discharge measurements were carried out at different dates with varying discharges due to 53

54 rainfall events during the sampling period. Figure 4-17 presents the data for water temperature and oxygen content; please note that some data are missing, as the equipment performed erroneous at times Relative river depth [m] River width [m] Station 2 Station 3 Station 4 Station 5 Station 6 Station 7 Station 10 Station 11 Station 12 Station 13 Figure 4-14 Comparison of cross sections from the mountains areas (Station 13) to the lowland parts (Station 2) 54

55 Slope (%) 25% 20% 15% 10% 5% 0% River length (km) Alltitude (m) Slope Alltitude Figure 4-15 Topographical properties (altitude and slope) along the Isabena River Manning's friction factor [-] River length (km) Discharge [m3/s] Manning's friction factor Discharge Figure 4-16 Hydrodynamic properties (discharge and Manning s friction factor) along the Isabena River 55

56 Water temperatue [ C] River length (km) Oxygen conent [mg/l] Water temperature Oxygen content Figure 4-17 Water temperature and oxygen content along the Isabena River Figure 4-18 and Figure 4-19 present two different visual presentations of the particle size distribution of the river bed at the ten sampling stretches. The first plot shows the cumulative grain size distribution, the second one gives a box plot representation with the statistical descriptors D 50, D 25 etc. Cummulative percent finer [%] 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Station 13 (5.3 km) Station 12 (8.3 km) Station 11 (60.1 km ) S ta tion 10 (21.6 km ) Station 7 (29.4 km) Station 6 (36.8 km) Station 5 (42.4 km) Station 4 (46.8 km) Station 3 (60.1 km) Station 2 (61.0 km) Grain size [mm] Figure 4-18 Cumulative grain size distribution for the ten river sampling locations 56

57 8, , , , Station_2 Station_3 Station_4 Station_5 The median diameter, D 50 is represented by a horizontal line through the box; the rectangle box encompasses the middle 50 % of the sample (between D 25 and D 75 ); the whiskers extend to D 10 and D 90 Station_6 Figure 4-19 Box plot presentation of particle size distribution for the ten river sampling locations Station_7 Station_10 Station_11 Station_12 Station_13 References Harrelson, C. C., C. L. Rawlins, J. P. Potyondy (1994) Stream channel reference sites: An illustrated guide to field technique. United States Department of Agriculture. Forest Service General Technical Report RM-245 Güntner, A., Large-scale hydrological modelling in the semi-arid North-East of Brazil, PhD thesis Potsdam Institute for Climate Research, Germany, PIK-Report No. 77, Kondolf, G. M., T. E. Lisle, G. M. Wolman (2003) Bed Sediment Measurement in: Tools in fluvial geomorphology edited by G. M. Kondolf and Herve Piegay, Wiley, Chichester Montgomery, D. R. and J. M. Buffington (1997) Channel-reach morphology in mountain drainage basins. GSA Bulletin 109 (5), p

58 Mueller, E. N., (2004) Scaling approaches to the modelling of water, sediment and nutrient fluxes within the Jornada Basin, New Mexico. unpublished PhD thesis, King s College London Rosgen, D. L., A classification of natural rivers. CATENA 22 (1994): Verdu, J. M., Analysis and modelling of the hydrological and fluvial response of a large mountainous Mediterranean catchment (Isabena River, Pre-Pyrenees), University of Lleida,

59 4.4 Sedimentological characterisation of the Barasona Reservoir, NE Spain by George Mamede Period of time: Scope of the field study A fieldwork campaign was carried out to gain an enhanced understanding of the sedimentation processes of the Barasona Reservoir at the outlet of the Esera Watershed, NE Spain. In particular, field data are required for the parameterisation and validation of the reservoir module of the WASA model. For this purpose, parameters associated with sedimentation processes were collected in and around the reservoir. The following three types of data were collected: firstly, soil samples of deposited material; second, water samples for the estimation of suspended sediment, and third, vertical profiles of water temperature. The sampling location, methods and results of the three data sets are described in turn below. Data collection was performed taking into account the incoming sediment from all large tributary streams and the deposition pattern along a longitudinal profile and several cross sections at the reservoir delta. This proceeding is essential to check the spatial variability of the physical properties of deposited sediment in order to support the development of the reservoir sedimentation module, particularly erosion and compaction processes. Values of water temperature are used in the calculation of fall velocity of particles. It is important to have values of water temperature from different seasons to enable the calculation of daily water temperature by interpolation. Collection of water samples at the upstream and downstream sites of a reservoir is very important to calibrate and validate the results on reservoir sediment balance. With measured values of suspend sediment concentration at the inlet point, it is possible to obtain a correlation between water input and sediment input into the reservoir, when there is no data available on incoming sediment. 1. Collection of sediment samples Forty-three sediment samples were collected immediately upstream of the Barasona Reservoir to characterize the deposited material. The locations of the sediment samples are depicted in Figure 4-20, emphasizing the eighteen sediment samples collected by means of cylinders to determine their physical properties. 59

60 A8 A9 A39 A40 A45 A46 A6 A38 A10 A37 A42 A24 A23 A5 A22 A21 A25 A26 A29 A4 A27 A28 A19 A18 A17 A30 A31 A32 A33 A15 A3 A34 A35 A36 A14 A13 A12 A11 A2 A7 Figure 4-20 Location of the sediment sampling points at the Barasona The results of the laboratory analysis of the eighteen sediment samples are summarized in Table 4-3. The sediment samples have a mean wet bulk density of 1.89 g/cm 3 and a mean dry bulk density of 1.52 g/cm 3. The particle size analysis of the soil samples is still in progress. Table 4-3 Physical properties of eighteen sediment samples collected upstream of the Barasona Reservoir ID Sample Wet sediment mass (g) Dry sediment mass (g) Soil moisture (%) Sediment density (g/cm3) Wet bulk density (g/cm3) Dry bulk density (g/cm3) 1 A % A % A % A % A % A % A % A % A % A % A % A % A % A % A % A % A % A % Mean value % Standard deviation %

61 2. Collection of water samples for suspended sediment concentration In order to determine the suspended sediment concentration from the inflow and outflow discharges and also along a longitudinal profile of the Barasona Reservoir, thirty-four water samples were collected. The locations of the sampling points are depicted in Figure Isábena River Sarron River P4a P4b Esera River Input P6 P7 P8 P9 P10 P11 P13 P12 P20 P24 P23 P19 P18 Barasona Dam P17 5 km Aragon and Catalunia Channel Figure 4-21 Location of the sampling points for suspended sediments at the Barasona Reservoir The sediment concentrations from daily outflow discharges were measured at the Aragon and Cataluna Channel and are presented in Table 4-4. The results show a very low mean sediment concentration of g/l at the outlet point downstream of the Barasona Reservoir in comparison to the mean suspended sediment concentration of g/l at the inlet point upstream the reservoir, reaching 13.5 g/l for water fluxes of about 14.7 m 3 /s. Table 4-4 Suspended sediment concentration measured downstream of the Barasona Reservoir during the field study (autumn, 2005) Sample Filtered volume (ml) Filter/plate weight (g) Gross weight (g) Concentration (g/l) HID HID HID HID HID HID HID Mean value Standard deviation

62 Ten of them were collected at the inlet point of the Barasona Reservoir to evaluate the amount of incoming sediment and to obtain a correlation between inflow discharge and sediment input. The amount of collected water samples are not representative to provide a good correlation between them, as presented in Figure Nevertheless, ongoing measurements on sediment concentration upstream the Barasona Reservoir on Isábena river are being continuously performed. Incoming sediment (ton/day) y = e x R 2 = Input discharge (m3/s) Figure 4-22 Relationship between the water inflow and the sediment inflow of the Barasona Reservoir For determination of the suspended sediment concentration along a longitudinal profile of the Barasona reservoir, eleven water samples were collected. The results, summarized in Figure 4-23, show explicitly the variation of sediment concentration from upstream to downstream, with circa 14 g/l at the inlet point approximately 5km away from the dam and scarce 0.32 g/l very close to the dam Sediment concentration (g/l) Distance from the Barasona Dam (m) Figure 4-23 Variation of suspended sediment concentration along a longitudinal profile of the Barasona Reservoir, measured during the field study (autumn, 2005) 62

63 3. Vertical profiles of water temperature Water temperature was measured from ten vertical profiles in the Barasona Reservoir, as presented in Figure 4-24, in order to assess its variation from the water surface to the reservoir bed Figure 4-24 Location of the sampling points for vertical profiles of water temperature at the Barasona Reservoir The results of five from them, presented in Figure 4-25, show a gradual variation from approximately 24 ºC at the water surface to circa 19 ºC at a depth of 23m very close to the dam. The other profiles showed some errors, and are therefore not presented here. Depth (m) Water Temperature ( 0 C) Profile Profile 7 Profile 8 Profile Profile Figure 4-25 Variation of water temperature for several vertical profiles at the Barasona Reservoir 63

64 4.5 Characterization of the Bengue Reservoir in the semi-arid northeastern part of Brazil by George Mamede Period of time: Scope of the field study In order to parameterize the reservoir sedimentation module of the WASA Model, a field study in the Bengue Reservoir was carried out during the rainy season. The plan of action for the field measurements in the Bengue Reservoir consisted of sediment collection from the not flooded area, water collection from inflow discharges, outflow discharges and along a longitudinal profile of the reservoir, measurements on water quality and installation of three suspended sediment samplers Description of the reservoir area The 19.2 Mm 3 Bengue Reservoir is located in the dry region of the Brazilian semiarid, with a mean annual evaporation of approximately 2,500 mm and a mean annual rainfall of around 560 mm. The dam impounds the ephemeral Umbuzeiro River, which dries out during the dry season from July to December. The Bengue Reservoir is able to supply 200 L/s for the cities downstream the dam, but only 15 L/s has been released for water supply in the Aiuaba city, with approximately 2,000 inhabitants. A view of the reservoir area is presented in Figure Figure 4-26 View of the Umbuzeiro River immediately upstream of the Bengue Reservoir during the field study (spring, 2005) 64

65 Figure 4-27 View of the reservoir area during the field study (spring, 2005) Installation of the three suspended sediment samplers It was planned to install three suspended sediment samplers, two of them upstream of the Bengue Reservoir and the third one at the outlet point of the 11.5 km 2 Aiuaba experimental catchment. The location for installation of the two samplers for the Bengue Reservoir was defined after considering some selection criteria: (1) the locations should be easily accessible; (2) a local person had to be found, who lives close to the place and could take charge of the equipment and carry out the data collection and maintenance; (3) they should be located on the river stretches, where a representative runoff and sediment from the contributing basin flow through. In Figure 4-28, the selected points upstream of the Bengue reservoir for installation of the suspended sediment samplers are presented. Unfortunately, the samplers could not be installed during this field campaign because of some to get the equipment through the customs at Fortaleza Airport. However, they have been installed later that year in August PS2 PS2 Figure 4-28 Location of the installation points of the two sediment concentration sampler upstream of the Bengue Reservoir (spring, 2005) 65

66 Collection of sediment samples Twenty-nine samples of the deposited sediment were collected from each tributary stream at the open-air exposed area of the reservoir to determine grain size distribution. Eight from them were collected to characterize the deposited material in terms of their physical properties, such as, soil permeability, wet and dry bulk density and dry bulk density of compacted sediment. The location of the collected samples is presented in Figure 4-28, emphasizing the eight samples collected by means of the cylinders. C09 C11 C01 C08 C05 C04 C16 C17 C18 C15 C13 C14 C20 C19 C21 C10 C07 C06 C03 C12 C02 Figure 4-29 Location of the twenty-one sediment samples collected upstream of the Bengue Reservoir (spring, 2005) The results on grain size distribution analyse show that the collected material is predominantly sand, with an average percentage of about 87%, as presented in Figure Percent finer (%) Grain size (mm) Figure 4-30 Grain size distribution of the deposited sediment in the Bengue Reservoir (spring, 2005) The results on soil physical properties, summarized in Table 4-5, show a mean wet bulk density of 1.67 and a mean dry bulk density of The unsaturated hydraulic conductivity 66

67 of the collected samples is very different in magnitude, varying from 3.6 x 10-7 cm/s to 0.28 cm/s. Table 4-5 Physical properties of eight sediment samples collected upstream of the Bengue Reservoir during the field study (spring, 2005) ID Sample Wet sediment mass (g) Dry sediment mass (g) Soil moisture (%) Unsatured hydraulic condutivity (cm/s) Sediment density (g/cm3) Wet bulk density (g/cm3) Dry bulk density (g/cm3) 1 A E A % 5.720E A % 4.928E A % 3.646E A % A % A % A % Mean value % Standard deviation % Table 4-6 shows the soil properties after compaction in the Soil Laboratory at the Federal University of Ceara. This information will be used in the reservoir sedimentation modelling module to relate volume and weight of the compacted sediment at the inactive layer. Table 4-6 Physical properties of the sediment samples collected upstream of the Bengue Reservoir during the field study after compaction in laboratory (spring, 2005) ID Sample Soil moisture after compaction (%) Dry bulk density after compaction (g/cm3) 1 A A A A A A A A A A A A A A A A A A Mean value Standard deviation

68 Collection of water samples Measurements of suspended sediment concentration of eight vertical profiles along the Bengue reservoir were carried out by sampling the water-sediment mixture. The location of the vertical profiles can be seen in Figure A Van Dorn bottle was used to collect water samples from several depths in the vertical profiles. P03 P06 P04 P05 P01 P02 P07 P08 P09 Figure 4-31 Location of the eight vertical profiles of suspended sediment concentration in the Bengue Reservoir (spring, 2005) The results, summarized in Figure 4-32, show the variation of sediment concentration from the water surface to the reservoir bed and also from the entrance of the Bengue reservoir to the dam. The blue marks belong to the five profiles located in the upstream part of the reservoir (P01, P02, P07, P08 and P09), whereas the red ones belongs to the four profiles located in the downstream part of the reservoir (P3, P4, P5 and P6). The low sediment concentration can be explained by the fact that no sediment fluxes into the reservoir existed at the time of measurements. Suspended sediment concentration (mg/l) Depth (m) Upstream subreach Downstream subreach Figure 4-32 Suspended sediment concentration in the Bengue Reservoir (spring, 2005) 68

69 Measurements on water quality A multi-parameter water quality meter was used to measure some physical properties of water, but only the temperature sensor worked well. A ph meter was used to calibrate the phsensor of the multi-parameter water quality meter. The results, summarized in Figure 4-33 and Figure 4-34, show the very low variation of temperature and ph, respectively, from the water surface to the reservoir bed for the four vertical profiles. Water Temperature ( 0 C) Depth (m) Profile 1 Profile 2 Profile 3 Profile Figure 4-33 Variation of water temperature from several vertical profiles in the Bengue Reservoir, measured during the field study (spring, 2005) ph Depth (m) Profile 1 Profile 2 Profile 3 Profile Figure 4-34 Variation of ph values from several vertical profiles in the Bengue Reservoir, measured during the field study (spring, 2005) Problems during data collection The field study was successful in the sense that several sediment samples and water samples could be collected and the most planned measurements were performed. Nevertheless, it was 69

70 a dry year and some relevant measurements could not be carried out, such as suspended sediment concentration from the inflow discharges and its influence in the variation of sediment concentration along a longitudinal profile of the Bengue Reservoir. Although the field campaign was carried out at the height of the rainy season, no inflow of water occurred at the inlet points of the Bengue Reservoir during the entire sampling period. The year 2005 has been a particular dry year. It was very difficult to access the collection points of sediment samples because of several trees and bushes, which were not removed from the reservoir area, as shows in Figure Figure 4-35 Presence of tree and bushes in the Bengue Reservoir (spring, 2005) Additional activities during the field campaign to Ceara A workshop about the WASA model for members of the SESAM project in Brazil was carried out at the University of Ceara, Fortaleza, with descriptions of the relevant processes considered in the WASA model and some explanations about the Fortran code and input files of the WASA Program. Detailed reports about the Bengue reservoir, data on reservoir level, input and output discharges and some satellite imageries of the area were provided by COGEHR (Water Resources Management Agency) and FUNCEME (Water Resources and Meteorology Agency). 70

71 4.6 Classification of badlands regarding their morphological characteristics in the Isabena Catchment by Katharina Appel, Eva Nora Mueller & Till Francke Period of time: Location: Arroyo de Villacarli, central Isabena Catchment, Huesca, Aragon, Spain (ca. 80 km north of Lleida) Background of the study Badlands are hillslopes with unconsolidated sediments with no or little vegetation cover that cannot be used for agriculture. They are characterised by their desert like appearance and their very high soil erosion rates. This fieldwork work studies the specific characteristics of badlands in the Isabena Watershed in the upper Ebro Basin, NE Spain (see Section 2.1.1). Studies of various authors (e.g. Fargas et al., 1996; Martínez-Casasnova and Poch, 1997) indicate that the major amount of sediments reaching the Barasona reservoir originates from the Internal Ranges and Intermediate Depression, as shown in Figure 2-3. Within these areas, the major sediment sources can be attributed to badland areas. They abound on Mesozoic marls which are highly erodible. The main concentration of badlands can be found in the subcatchment of the Arroyo de Villacarli (see Figure 4-36) where they can cover up to one third of the valley slopes. According to Verdú (2003), the sub-catchment of Villacarli also generates a quarter to a third of the runoff of the entire catchment. Badland 1 Badland 2 Badland 3 Badland Meters Figure 4-36 Map of the Arroyo de Villacarli showing the shapes of the four badlands investigated during the field trip. Arrows indicate the main orientation of the badlands 71