Soil Erosion Assessment in the Dryland Areas of Bolivia Using the RUSLE 3D Model

Soil erosion assessment in the dryland areas of Bolivia using the RUSLE 3D model

MSc thesis by Annemieke de Kort September 2013

Soil Physics and Land Management Group

Soil erosion assessment in the dryland areas of Bolivia using the RUSLE 3D model

Master thesis Soil Physics and Land Management Group submitted in partial fulfillment of the degree of Master of Science in International Land and Water Management at Wageningen University, the Netherlands

Study program: MSc International Land and Water Management

Student registration number: 710610468100

LDD 80336

Supervisors: Dr.ir. A. Kessler Wageningen University, The Netherlands C.K. Ovando Crespo MSc CISTEL, Cochabamba, Bolivia R.J. Vargas Rojas MSc FAO, Rome, Italy

Examinator: Prof. Coen Ritsema Wageningen University, The Netherlands

Date: 17/09/2013

Soil Physics and Land Management Group, Wageningen University

Abstract

RUSLE 3D, a modification of the USLE model, was used in this thesis research to calculate soil loss in the dryland areas of Bolivia. Calculation results showed that annual soil loss amounts were less than five ton ha-1 in more than 50 per cent of the study area. This is not in accordance with literature where it is often mentioned that soil losses in Bolivia are severe. However, since the USLE model was originally developed for small scale use it might not be the best suitable model in this research and the reliability of the used data, used methods and the end result were therefore assessed. The reliability assessment showed that data were not always complete, did not always have the correct spatial resolution or map scale, or were outdated. Moreover, calculation methods sometimes overestimated or underestimated calculation results. Nevertheless, soil loss amounts in 99,1 per cent of the study area fell within the range of soil loss amounts obtained from other researches. However, RUSLE 3D calculation might provide a fake reality whereby the end result looks correct while that is in fact caused by compensation of an overestimated parameter with an underestimated parameter and vice versa and not because the calculation of each separate parameter gave correct results. In general, it can be said that the reliability of the used data and used methods is too low and too many uncertainties exist for a reliable calculation of soil loss amounts in the dryland areas of Bolivia using the RUSLE 3D model.

Keywords: Bolivia, dryland area, soil erosion, RUSLE 3D

Table of contents

Abstract ...... 5 1 Introduction ...... 9 1.1 General introduction ...... 9 1.2 Soil degradation in the dryland areas of Bolivia ...... 10 1.3 Problem definition ...... 12 1.4 Research objective and research questions ...... 13 1.5 Context of the research ...... 14 2 Materials and methods ...... 15 2.1 Study area description ...... 15 2.2 The RUSLE 3D model ...... 21 2.2.1 Concept of the RUSLE 3D model ...... 21 2.2.2 Rainfall-runoff erosivity (R) ...... 22 2.2.3 Slope length and slope steepness (LS) ...... 23 2.2.4 Soil erodibility (K) ...... 25 2.2.5 Cover and management (C) ...... 26 2.2.6 Conservation practices (P) ...... 27 3 Results ...... 28 3.1 Rainfall-runoff erosivity (R) ...... 28 3.2 Slope length and slope steepness (LS) ...... 28 3.3 Soil erodibility (K) ...... 29 3.4 Cover and management (C) ...... 30 3.5 Conservation practices (P) ...... 31 3.6 Annual soil loss by water in the dryland areas of Bolivia...... 31 4 Discussion ...... 33 4.1 Data availability and reliability ...... 33 4.2 Method reliability ...... 39 4.3 Model reliability ...... 46 5 Conclusion and recommendations ...... 49 References ...... 51 Appendix A: Overview of C factor vegetation classes ...... 59 Appendix B: Soil loss classes according to Morgan (2005) ...... 61

Introduction

1 Introduction

Land degradation is a common problem in the world. If not tackled properly, it can lead to food insecurity, decreases in livelihood situations and poverty. Suitable measures need to be taken to decrease land degradation, but for that one needs to know where and how much land degradation takes place. Soil degradation, one of the major forms of land degradation, can be calculated with the help of models. In this thesis research, the RUSLE 3D model is used to calculate the amount of soil loss by water in the dryland areas of Bolivia, areas that are susceptible to soil degradation due to their natural landscape form and location.

In Chapter 1, a general introduction about land and soil degradation is given (1.1), followed by a more detailed description of soil degradation in Bolivia (1.2). Then, successively, the problem definition in the framework of this research (1.3) and the research objective and research questions (1.4) will be defined. Finally, the context in which this research takes place will be explained (1.5).

1.1 General introduction

In 2005, the FAO defined land degradation as “the reduction in the capacity of the land to perform ecosystem functions and services (including those of agro-ecosystems and urban systems) that support society and development” (Nachtergaele and Licona-Manzur, 2008). In this era of high population growth and climate change, land degradation has become an increasing stress factor in the environment and in human life. Population growth increases pressure on land. On the one hand more food and fodder needs to be produced for the increasing population and its livestock, while on the other hand more land is needed to expand urban areas and infrastructure. To fulfil these needs, deforestation and expansion to marginal lands get more common nowadays, since land in more suitable areas is already in production.

Marginal lands are often situated in dryland areas, areas that are defined by UNESCO as semi-arid and sub-humid areas with an average annual P/ETP ratio between 0,20 and 0,75 (Dietz and Veldhuizen, 2004). Main agricultural production in dryland areas exists of pastoralism and rain fed subsistence crop production. Risk of crop failures is high because precipitation is unreliable and droughts, and occasionally flooding, are common in these areas. However, despite the risk factors, a large part of the world population lives in dryland areas. In Africa, Asia and South America, respectively 40, 39 and 30 per cent of the population lives in dryland areas (UNEP 1997, in Dietz and Veldhuizen, 2004, p.106).

Many dryland areas have poor, unfertile soils and sparse vegetation covers that make them vulnerable to land degradation. Natural land degradation can be accelerated by climate change and anthropogenic factors such as unsustainable land use management. Unsustainable land use management practices such as overgrazing and deforestation can lead to bare soils that are vulnerable to water and wind erosion. Climate change, often leading to more intense rainfall events in shorter time periods and longer periods of droughts, increases the vulnerability of soils to land degradation. During rainfall periods, the intensity of the raindrops causes soil aggregates to breakdown and detach after which they are transported by water that is flowing over the soil or, in dryer periods, through the air by wind, and deposited elsewhere (Dregne, 2002). As a result, soils become shallower and land degradation increases. According to DESIRE, an international project

Introduction

funded by the European Union that studies and tries to find solutions for land degradation problems all over the world, land degradation in dryland areas has a negative effect on more than 250 million people worldwide (Van den Elzen, 2007).

Land degradation can become visible in several forms. One of the major forms of land degradation is soil degradation. Figure 1 shows that soil degradation can appear in a physical, a chemical or a biological form, but all forms will eventually lead to a reduction in biomass productivity, contamination of air and water, and emission of trace gases into the atmosphere (Lal, 2001). If not tackled properly, soil degradation can become irreversible and eventually lead to food insecurity, decreases in livelihood situations and poverty.

Figure 1: Appearance forms of soil degradation (Lal, 2001)

1.2 Soil degradation in the dryland areas of Bolivia

Large parts of the dryland areas in Bolivia are situated in the Central Andes, a highland area with altitudes that can rise to more than six kilometres above sea level, where climatic conditions can be extreme. Slope steepness in the Central Andes ranges from zero per cent to near vertical, making the area vulnerable to natural soil degradation processes such as land sliding and gully formation (Blodgett and Isacks, 2007). According to Barnes et al. (2006) relief is often the driving force of natural soil degradation processes, which are further triggered by earthquakes and climatic conditions (Blodgett and Isacks, 2007). Research executed by Dosseto et al. (2006) showed that present-day soil degradation rates are higher than steady-state soil degradation rates, indicating that soil degradation has increased over the years and that soils are faster being destroyed than generated nowadays. Based on research executed by Tapia et al. (2003), Servant and Servant-Vildary (2003) and Rigsby et al. (2003) who all suggested that precipitation amounts in the Central Andes have increased, and based on their own findings, Dosseto et al. (2006) claim that soil degradation processes are to a large degree triggered by climatic conditions. However, human induced soil

Introduction

degradation in the Central Andes is also witnessed, especially in forested areas, on agricultural fields and on rangelands (Blodgett and Isacks, 2007).

Overgrazing of rangelands by sheep and goats is one of the main causes of human induced soil degradation in the dryland areas of Bolivia (LeBaron et al., 1979; Lauer, 1993; Zimmerer, 1993; Paulson, 2003). As in many dryland areas, main agricultural production in the Central Andes exists of rain fed subsistence crop production and pastoralism (Preston et al., 2003; Brandt and Townsend, 2006). Main livestock exists of oxen, cows, sheep, goats, pigs, rabbits and chicken (LeBaron et al., 1979). In high situated areas, sheep and goats are replaced by llamas. Sheep and goats, introduced during the Spanish colonization in the 16th century, deliver meat, wool, milk, manure, emergency food and cash income during bad crop years (LeBaron et al., 1979; Preston et al., 2003) and are therefore important animals for farmers. Although most animals obtain their fodder from harvested crops, sheep and goats obtain their fodder from grazing only. Overgrazing of soils by sheep and goats, however, results in the removal of vegetation from the soil, leaving the soil bare and vulnerable for raindrop impact and soil degradation processes (LeBaron et al., 1979).

Increased population growth is another cause of human induced soil degradation in the dryland areas of Bolivia (Ellis-Jones and Mason, 1999). As in many areas in the world, population in the Central Andes increases. Increased population growth, however, leads to pressure on agricultural fields because more food and fodder needs to be produced and because more land is needed to expand urban areas and develop infrastructure (Brandt and Townsend, 2006). The pressure on agricultural fields results in a more intensive use of those fields, conversion of sustainable land use practices to less sustainable land use practices, and reduction or abandoning of fallow periods (Ellis- Jones and Mason, 1999; Coppus et al., 2003; Brandt and Townsend, 2006; Kessler and Stroosnijder, 2006).

Migration can also cause human induced soil degradation in the dryland areas of Bolivia (Zimmerer, 1993). In the Central Andes, agricultural production is often not enough to fulfil the consumption needs of households. Therefore, many farmers migrate temporarily to cities or even to other countries to earn an additional income (Clark et al., 1999; Paulson, 2003; Preston et al., 2003). For the household members that stay behind, this means an increase in agricultural tasks like ploughing, threshing, mounding and harvesting (Zimmerer, 1993). Due to lack of time caused by the increase in agricultural tasks, less energy and time can be invested in maintaining the vitality, biomass, and biodiversity of agricultural fields and rangelands (Paulson, 2003). Moreover, agricultural fields are often abandoned and existing terraces and irrigation networks are no longer maintained (Lauer, 1993).

Soil degradation processes can be reduced by the implementation of soil and water conservation (SWC) measures. Although the implementation of SWC measures has advantages, disadvantages like the amount of labour or the amount of money that is needed to implement the measures (Zimmerer, 1993; Clark et al., 1999; Ellis-Jones and Mason, 1999) and the fact that many effects are only visible on the long term (Ellis-Jones and Mason, 1999) can outweigh the advantages. For the implementation of traditional SWC measures, like the building of stone boundary bunds around agricultural fields for example, it can take 23 to 32 labour days to build the bunds on a field with a size of 0,12 to 1,13 hectares (Clark et al., 1999). Especially in areas with high migration rates, this amount of labour might not be available. Moreover, many farmers in the Central Andes region are

Introduction

subsistence farmers who lack the amount of money to implement SWC measures and who are more focused on short term effects like having good yields so they can feed their family (Ellis-Jones and Mason, 1999). These farmers might not be willing to invest in often expensive SWC measures that only have benefits on the long term. Other causes that can prevent the implementation of SWC measures are (1) lack of tenure rights (Ellis-Jones and Mason, 1999), (2) lack of sufficient knowledge about soil degradation processes (Kessler, 2008), and (3) the fact that some farmers believe that they cannot control soil degradation processes because they are a punishment of the angry ‘Pachamama’ (Mother Earth) for neglecting ritual customs towards her (Zimmerer, 1993).

1.3 Problem definition

In 1995, a land degradation assessment, executed by PRONALDES (Programa Nacional de Lucha contra la Desertificación), showed that 42 per cent of Bolivian soils is affected by desertification (CISTEL, 2010). Moreover, between 1985 and 2003, Brandt and Townsend (2006) tried to quantify the amount of land use and land cover change in the Altiplano and the Intermediate Valley (Mountain) zones in the South eastern part of the Bolivian Andes with the help of remote sensing techniques. They found that both the Mountain zones and the Altiplano faced extensive deforestation, desertification, agricultural expansion and communal rangeland degradation.

The assessments executed by PRONALDES and by Brandt and Townsend showed that land degradation in the dryland areas of Bolivia is severe. Since 42 per cent of Bolivia exists of dryland areas and 62 per cent of the Bolivian population lives in these areas (Murray et al., 1999), sustainable solutions need to be found to decrease land degradation processes and increase livelihood situations. However, quantitative information about land degradation in Bolivia is scarce and incomplete (Dregne, 2002) and more research about the present status of land degradation in this country is needed to be able to implement the best suitable measures and improve the situation.

Models can be useful to assess land degradation, especially when the area to be studied is situated in a remote or difficult accessible area. It should however be taken into account that most models are developed for use in a predefined area or at a specific spatial scale, and calibration and validation is needed when a model is applied in other areas or at other spatial scales than it was originally developed for. The model type should therefore be chosen with care and meet the demands of the research.

One of the most used models worldwide to study land degradation is the Universal Soil Loss Equation (USLE) model, an empirical model developed by Wischmeier and Smith (1978) that calculates annual soil loss by water. Since this easy to use model does not require large amounts of data input, it can be used in areas, such as the dryland areas of Bolivia, where only limited data are available. In this research, a quantitative assessment of soil loss by water in the dryland areas of Bolivia is executed with the help of the RUSLE 3D model, a modification of the USLE model.

The USLE model was originally developed for use at small hill slope scale in the United States only (Merritt et al., 2003) and is only valid for areas smaller than one hectare (Saavedra, 2005). This research, however, focuses on the dryland areas of Bolivia, areas that are together much larger than one hectare. Moreover, the USLE model ignores gully erosion, mass movement and sediment deposition (Zhang et al. 1995 in Merritt et al., 2003), important factors when soil erosion at large

Introduction

scale needs to be assessed. Since the RUSLE 3D model is a modification of the original USLE model, the model might therefore not be the best suitable model to use in this research.

1.4 Research objective and research questions

In 2005, Carlos Saavedra did a PhD research into soil erosion in the Cochabamba department in Bolivia (Saavedra, 2005). He calculated the amount of soil loss in the department with the help of different models, among which the RUSLE 3D model, in combination with remote sensing data. In this research, the RUSLE 3D method used by Saavedra is followed as much as possible to calculate the amount of soil loss in the dryland areas of Bolivia. However, as already mentioned in the previous paragraph, the RUSLE 3D model might not be the best suitable model to use in this research since it is extrapolated to an area that is much larger than it was originally developed for. The research objective of this MSc thesis is therefore to evaluate if the RUSLE 3D model is a suitable model to assess soil loss by water in the dryland areas of Bolivia. To reach the research objective, the following research questions and their sub questions need to be answered:

1. How reliable are the used data?  What kind of data is used to execute the RUSLE 3D model and are these data easy to obtain?  Do gaps in the data availability exist and if so, how can these gaps be filled?  Can other data be used for the RUSLE 3D model and are these data more reliable than the data currently used? 2. How reliable are the used methods?  What methods are used to execute the RUSLE 3D model?  What other methods can be used for the RUSLE 3D model and are these methods more reliable than the methods currently used? 3. How reliable is the end result of the RUSLE 3D model calculation?  What is the end result of the RUSLE 3D model calculation?  Is the end result in accordance with previous obtained results from the same study area?

The first research question and its sub questions will be answered by searching for data in existing field databases and on the internet. Data that are found will be assessed to see if they are suitable to use and to see where data gaps exist and where assumptions need to be made to be able to execute the RUSLE 3D model. Data are also compared with other data, if available, to see if they are the best suitable data to use in the RUSLE 3D model. A literature study will be used to answer the last two sub questions.

The second research question and its sub questions will also be answered by means of a literature study.

The third research question and its sub questions will be answered by executing the RUSLE 3D model with the available data for the whole study area with the help of ArcGIS 9.3 software. The model will then be validated by comparing its end result with previous obtained results from the same study area to see if the model result differs significantly from these results.

Introduction

1.5 Context of the research

This MSc thesis takes place in the framework of the project “Evaluacion de la degradación de tierras en las tierras secas de Bolivia” (Evaluating land degradation in the arid areas of Bolivia). The project is set up by the Bolivian organization CISTEL (Centro de Investigación y de Servicios en Teledetección) in order to decrease the lack of knowledge about land degradation in the dryland areas of Bolivia and to be able to implement suitable measures to combat land degradation in these areas in the future. The general objective of the CISTEL project is “to evaluate the current status of the degradation process in arid, semi-arid and sub-humid areas in Bolivia and to design a systematic monitoring system of this process using the LADA approach of the FAO” (CISTEL, 2010). The LADA (Land Degradation Assessment in Drylands) approach of the FAO aims at an assessment of land degradation in dryland areas at national, regional and local level, thereby including its nature, extent, severity, impact and root causes, so that sustainable measures can be implemented to combat degradation processes. In its assessment, the FAO takes both bio-physical factors and socio-economic factors into account (FAO, 2002).

Materials and methods

2 Materials and methods

In this Chapter, the materials and methods that are needed to execute the RUSLE 3D model will be explained. Chapter 2 starts with a description of the study area (2.1), followed by a description of the concept of the RUSLE 3D model (2.2.1). Finally, each parameter of the RUSLE 3D model and how to create the maps for each parameter will be explained separately (2.2.2-2.2.6). The RUSLE 3D model calculation follows as much as possible the approach used by Saavedra (2005) during his thesis research about soil erosion in the Cochabamba department in Bolivia.

2.1 Study area description

Location

The research is executed in the dryland areas of Bolivia, situated in the Southern part of the country. The dryland areas exist of the hyper-arid, arid, semi-arid and sub-humid zones as indicated by the Water Center for Arid and Semi-Arid Zones in Latin America and the Carribean (CAZALAC, 2010). In Figure 2, the study area is shown.

Figure 2: Overview of the study area (climate zones according to CAZALAC, 2010)

A large part of the study area is situated in the Central Andes, a tectonic active highland area with altitudes that can rise to more than six kilometres above sea level. From the West to the East, the Central Andes can be divided into five zones: the Western Cordillera (a volcanic arc), the Altiplano (a high-elevation internally drained low relief basin), the Eastern Cordillera (a bivergent portion of the Andean fold-thrust belt), the Interandean Zone (IAZ) and the Subandes (a zone of ridges and valleys striking from Northwest to Southeast parallel to the plateau boundary) (McQuarrie et al., 2005; Blodgett and Isacks, 2007). The tectonic structure of the Central Andes in Bolivia and an impression of the study area are shown in Figure 3. A small part in the South eastern part of the study area is

Materials and methods

situated in the Lowlands of Bolivia, an area also known as the Chaco Plain (a low-elevation foreland basin underlain by the Brazilian shield) (McQuarrie et al., 2005).

Figure 3: Tectonic structure of the Central Andes in Bolivia (left) and an impression of the study area (above) (adapted from McQuarrie et al., 2005)

Elevation and slope level

Many differences in elevation occur in the study area. Elevations range from 82 m above sea level in the East where the Chaco Plain is situated to more than 6.000 m above sea level in the West where the Central Andes is situated (Figure 4).

Figure 4: Elevation [m ASL] in the study area

Materials and methods

Slope levels in the study area range from very flat (less than two per cent) to very steep (more than 45 per cent) (Figure 5). Steepest slope levels are found in the Eastern Cordillera, the Interandean Zone and the Subandes. In these three areas, most slopes are steeper than 16 per cent. In the Altiplano and the Chaco Plain, most slopes are not steeper than 16 per cent.

Figure 5: Slope level [%] in the study area

Climate

Two seasons occur in the study area: a long winter season from April to October/November and a short summer season from October/November to April. Average precipitation amounts range from 243 mm per year in the Western Cordillera and the Altiplano to more than 1.100 mm per year in the Interandean Zone, the Subandes and the Chaco Plain, calculated over a period of 35 years (1976- 2010) (Figure 6).

Figure 6: Average precipitation distribution [mm yr-1] over the years 1976-2010 in the study area

Materials and methods

Average precipitation distribution (Figure 7) shows that most precipitation falls in the summer season. In most parts of the study area, precipitation falls in the form of rain but in higher situated areas, hail showers can also occur throughout the whole year.

Figure 7: Average precipitation distribution in the study area over the years 1976-2010

According to the Köppen-Geiger climate classification system (Kottek et al., 2006), the climate in the study area changes from an arid cold desert (BWk) or steppe (BSk) climate with average annual temperatures below 18 degrees in the Western Cordillera, Altiplano and Eastern Cordillera to a more temperate climate with dry winters and warm summers (CWb, at least four months with temperatures above 10 degrees) or with dry winters and hot summers (CWa, maximum temperature above 22 degrees) in parts of the Eastern Cordillera, the Interandean Zone and Subandes. In the Chaco Plain, an equatorial savannah climate with minimum temperatures of 18 degrees and dry winters (Aw) is found. Moreover, an arid hot steppe climate with average annual temperatures above 18 degrees (BSh) is found in the central part of the Interandean Zone and the North western part of the Subandes (Figure 8).

Figure 8: Climate zones in the study area according to the Köppen-Geiger classification system (Kottek et al., 2006)

Materials and methods

Soils

Most occurring soil types in the study area are Leptosols (43,0%), Cambisols (24,1%) and Regosols (15,5%) (Figure 9). Leptosols are shallow soils that have continuous rock at or close to the surface or they are extremely gravelly or stony soils. They mainly occur at high or medium altitudes, especially in strong eroding areas. Population pressure, overexploitation and increasing environmental pollution are the main causes of erosion in Leptosol areas (FAO, 2006). In the study area, Leptosols mainly occur in the Eastern Cordillera (bare rock), the Interandean Zone (bare rock or afforested) and the Subandes (afforested).

Figure 9: Soil map of the study area (ISRIC, 2011b)

Cambisols exist of medium and fine-textured parent materials that are derived from a wide range of rocks. They are characterized by slight or moderate weathering and by the absence of illuviated clay, organic matter and aluminium (Al) or Iron (Fe) compounds. The beginning of soil formation (changes in structure, colour, clay content or carbonate content) is visible in the subsoil. Cambisols are often used for agricultural purposes, such as mixed arable farming, grazing or (on steep slopes) forest (FAO, 2006). In the study area, Cambisols mainly occur in the flatter areas of the Altiplano and in the Chaco Plain.

Regosols form a group of soils that cannot be classified into one of the Reference Soil Groups defined by the FAO (FAO, 2006). They are weakly developed mineral soils existing of unconsolidated fine grained parent materials that do not have a clearly developed horizon, that are not very shallow or rich in gravels, that are not sandy, or that do not contain fluvic materials. Regosols occur in eroding areas at all elevations, particularly in arid, semi-arid and mountainous areas. Due to their low moisture holding capacity, crop production often needs additional irrigation and therefore Regosols are often used for extensive grazing instead. In the study area, Regosols mainly occur in the Western Cordillera, the less flat areas of the Altiplano and the Northern parts of the Eastern Cordillera and Interandean Zone.

Other soils that occur in the study area are Solonetz (5,2%), Solonchaks (3,8%), Phaeozems (3,3%), Ferralsols (2,2%), Arenosols (1,7%), Luvisols (1,1%) and Kastanozems (0,04%) (Figure 9). While

Materials and methods

Solonetz mainly occur in the Chaco Plain, Solonchaks are found in the Altiplano. Both soils are salty soils. Solonetz have a dense, strongly structured, clayey subsurface horizon with a high proportion of adsorbed sodium (Na) or magnesium (Mg) ions. They can be strongly alkaline. Solonchaks have a high concentration of soluble salts at some time in the year (FAO, 2006).

Phaeozems, Ferralsols, Luvisols and Arenosols are all found in the Chaco Plain. Phaeozems are dark fertile soils with a high organic matter content and a high base saturation in the upper meter of the soil. Ferralsols are deeply weathered, well drained, red or yellow tropical soils with a stable microstructure, but with poor chemical properties. Luvisols are fertile soils that have a higher clay content in the subsoil than in the topsoil due to clay migration. All three soils are suitable for agricultural purposes, although erosion control measures might be needed for Phaeozems and Luvisols due to their susceptibility to erosion, and good soil fertility management is needed for Ferralsols due to their poor chemical properties and fast depletion of nutrients. On the other hand, Ferralsols are less susceptible to erosion because of their great soil depth, good permeability and stable microstructure. Arenosols are sandy soils with a coarse texture, high permeability, and low water and nutrient storage capacity. In dry areas, soil development is less than (or can even be absent) in humid areas. Although Arenosols are less suitable for agricultural purposes, they can be used for the cultivation of root and tuber crops (FAO, 2006).

Kastanozems are found in a small area in the Southern part of the Interandean Zone. These chestnut- brown coloured soils are rich in organic matter and show accumulation of secondary carbonates in the subsoil. Kastanozems can have a lack of soil moisture and additional irrigation might be needed when the soils are used for crop production. Often, extensive grazing is practiced but care must be taken not to overgraze the soils, because wind and water erosion can cause problems on overgrazed fallow soils (FAO, 2006).

Vegetation and land use

Although a large heterogeneity in vegetation cover is shown, especially in the Western part of the study area, rangelands (57,5%) and forests (32,7%) are the dominating vegetation types (Figure 10). While most rangelands are found in the Western part of the study area, forests are mainly found in the Eastern part. In wetter areas, such as in the Interandean Zone, the Subandes and the Chaco Plain, forests consist of deciduous and semi-deciduous trees, while in dryer areas, such as in the Eastern Cordillera, forests consist of coniferous trees. Rangelands, consisting of shrublands, savannahs and steppes, are found in the Western Cordillera, the Altiplano and the Eastern Cordillera in areas where precipitation amounts are low. In the Northern parts of the Altiplano and the Eastern Cordillera, natural vegetation types (0,5%) also occur. Agriculture covers 2,4 per cent of the study area in the form of extensive (1,9%) and intensive (0,4%) agriculture. Other land use forms, such as salt lakes, water bodies, snow fields and urban areas are found in 4,0 per cent of the study area. Moreover, 2,3 per cent of the study area is covered with gullies, bare soils or deserts. These erosion features are mainly found in the Southern parts of the Western Cordillera, the Altiplano, the Eastern Cordillera and the Interandean Zone.

Materials and methods

Figure 10: Vegetation map of the study area (CDRNB, 2013)

2.2 The RUSLE 3D model

2.2.1 Concept of the RUSLE 3D model

The RUSLE (Revised Universal Soil Loss Equation) 3D model is a modified version of the USLE model, an empirical model that was developed by Wischmeier and Smith in the 1950s. Since most empirical models require less input data than more complex models, such as conceptual models and physical based models, they are often used in areas where little data are available (Saavedra, 2005). The USLE model was originally developed to estimate the average annual soil loss caused by sheet and rill erosion from small hill slopes in croplands in the United States (Renard et al., 1991; Merritt et al., 2003). From the 1970s however, the model was also used for rangelands and disturbed forested areas. It became a tool that was not only used by soil conservationists in farm planning but also by policy makers to estimate the amount of sheet and rill erosion in national inventories and assessments in order to formulate and implement soil conservation policies (Renard et al., 1991).

In the 1990s, the USLE model was revised whereby its database was updated with data that were acquired after the first release of the model (Renard et al., 1991; Lal, 2001; Merritt et al., 2003). After the revision, the name RUSLE became common. Nowadays, the RUSLE model is the most used model worldwide to calculate soil loss by water.

Materials and methods

The formula used in the RUSLE model is (Renard et al., 1996; Saavedra, 2005):

A = R * LS * K * C * P (Formula 1)

Where A = annual soil loss by water [ton ha-1 yr-1] R = rainfall-runoff erosivity factor [MJ mm ha-1 hr-1] LS = slope length and slope steepness factor [-] K = soil erodibility factor [ton ha-1 per unit R] C = cover and management factor [-] P = conservation practices factor [-]

The RUSLE model can be divided in two parts: the potential soil loss part and the management part (Vrieling, 2007; Van Hulst, 2011). The potential soil loss part indicates the susceptibility of the soil to erosion and is calculated by multiplying the R factor, the LS factor and the K factor. These three factors are fixed parameters that depend on location and that cannot be changed by human intervention. The management part is calculated by multiplying the C factor and the P factor. Management factors can be changed by human intervention and can therefore have a positive or negative effect on the amount of soil loss. For example, the execution of SWC measures (changing the P factor) can decrease soil loss amounts, while cutting down forests (changing the C factor) can increase soil loss amounts. The actual soil loss is calculated by multiplying the potential soil loss part and the management part.

In both the USLE and the RUSLE model, hill slopes are assumed to be spatially homogeneous uniform (Jetten et al., 2003). Erosion in these models is only considered along flow lines without including the influence of flow convergence and divergence, making them only suitable for areas that experience erosion (Moore and Burch, 1986; Mitasova et al., 1996; Garcia Rodriguez and Gimenez Suarez, 2012). To include flow convergence and divergence, the RUSLE 3D model was developed whereby the slope length was replaced by the upslope contributing area per unit contour width (see also Chapter 2.2.3), making it possible to apply the model to more complex and large areas, such as the dryland areas of Bolivia, where both erosion and deposition takes place.

2.2.2 Rainfall-runoff erosivity (R)

The rainfall-runoff erosivity factor is the climatic factor in the RUSLE 3D model. It represents the input that drives the rill and sheet erosion process. Differences in R values represent differences in erosivity (Renard et al., 1991). When continuous rainfall data at a time interval equal to or less than 30 minutes are available, the R factor can be calculated with the formula (Saavedra, 2005):

j (EI 30)i R  i1 (Formula 2) N

Where E = total storm kinetic energy [MJ ha-1 mm-1] -1 I30 = maximum 30 minutes rainfall intensity [mm hr ] j =number of storms in the N year period N = year

Materials and methods

When continuous rainfall data at a time interval equal to or less than 30 minutes are not available, the R factor can be calculated with the help of the Modified Fournier Index (Arnoldus 1977 in Renard and Freimund, 1994) and the formulas (Saavedra, 2005):

R = 0,07397 * MFI1,847 (when F < 55 mm) (Formula 3) R = 95,77 – 6,081*MFI + 0,477 *MFI2 (when F > 55 mm) (Formula 4)

Where MFI = Modified Fournier Index [mm]

The Modified Fournier Index (MFI) can be calculated with the formula (Saavedra, 2005):

12 2  Pi MFI  i1 (Formula 5) Pa

Where Pi = average monthly precipitation [mm]

Pa = average annual precipitation [mm]

For this research only monthly rainfall data were available and therefore Formulas 3, 4 and 5 were used to calculate the R factor.

Obtaining the data and calculating MFI and R

Monthly rainfall data were obtained from the website of the ‘Servicio Nacional de Meteorología e Hidrología’ (National Meteorological and Hydrological Service) of Bolivia (SENAMHI, 2003). Years with missing data were excluded. The Modified Fournier Index (MFI) and the runoff-rainfall erosivity factor (R) were calculated with the help of Microsoft Excel and Formulas 3, 4 and 5 and imported in ArcGIS 9.3.

Creating the R factor map

After importing the Microsoft Excel data in ArcGIS 9.3, annual R factor point maps were created. Only years between 1976 and 2010 were used since these years showed the most uniform spreading of the weather stations over the study area. The maps were then interpolated and extrapolated to the boundaries of the study area to create continuous maps that were suitable for use in the RUSLE 3D model. The spherical ordinary kriging method was used as interpolation method. The average of all the maps was then calculated to create the final R factor map.

2.2.3 Slope length and slope steepness (LS)

The slope length and slope steepness factor is the topographical factor in the RUSLE 3D model. The slope length factor (L) represents the ratio of soil loss from the field slope length to soil loss from a 22,1 m slope length under otherwise identical conditions, and the slope steepness factor (S) represents the ratio of soil loss from the field slope gradient to soil loss from a nine per cent slope under otherwise identical conditions (Renard et al., 1996). The original formula to calculate the LS factor in the USLE model is (Wischmeier and Smith, 1978):

Materials and methods

m    LS =    65,41sin 2   4,56sin  0,065 (Formula 6)  22,13

Where λ = slope length [m] m = parameter depending on the value of the slope [-] θ = slope angle [°]

However, as already mentioned before in this Chapter, this formula does not include the influence of flow convergence and divergence and only considers erosion along flow lines, and it is therefore not considered suitable for use in complex areas where not only erosion but also deposition takes place. To calculate the LS factor in complex areas, new formulas were developed by Moore and Burch (1986) and Mitasova et al. (1996). These formulas, based on the unit stream power theory, include the influence of flow convergence and divergence and can therefore be used in areas where both erosion and deposition takes place. In this research, the formula of Mitasova et al. (1996) was used:

m n  A   sin  LS = m 1      (Formula 7) 22,13 0,0896

Where A = upslope contributing area per unit contour width [m] θ = slope angle [°] m = empirical exponent n = empirical exponent

Moore and Wilson (1992) have shown that the values of m = 0,6 and n = 1,3 give satisfying results for slope lengths less than 100 m and slope angles less than 14 degrees.

Obtaining and preparing the data

To calculate the LS factor, a Digital Elevation Model (DEM) of the study area was needed. The 54 DEMs that were needed to cover the whole study area were downloaded from the ASTER GDEM website (Japan Space Systems, 2009), merged into one single DEM with the help of the ‘mosaic to new raster’ tool and clipped to the boundaries of the study area with the help of the ‘extract by mask’ tool in ArcGIS 9.3. The ASTER GDEMs, developed by the Japanese Ministry of Economy, Trade and Industry (METI) and NASA, are in GeoTIFF format with geographic latitude and longitude coordinates and a one arc-second (30 m) grid. Estimated accuracies are 30 m at 95 per cent confidence for horizontal data and 20 m at 95 per cent confidence for vertical data (ASTER GDEM Validation Team, 2009). Cloudy pixels, residual bad values and outliers had already been removed, and residual anomalies had already been corrected by METI and NASA before the data were downloaded from the ASTER GDEM website.

Creating the upslope contributing area per unit contour width (A) map

The upslope contributing area per unit contour width is the area from which the water flows into a given grid cell (Mitasova et al., 1996). To calculate the upslope contributing area, a flow accumulation map needed to be created first. This map then needed to be multiplied with resolution of the DEM (Mutua et al., 2006; Terranova et al., 2009).

Materials and methods

To create the flow accumulation map, the ‘hydrology’ tool in ArcGIS 9.3 was used. First, the sinks in the DEM were filled to create a surface without depressions. After filling the sinks, a flow direction map was created and based on this flow direction map, the flow accumulation map was created. Finally, the flow accumulation map was multiplied with the resolution of the DEM to create the upslope contributing area per unit contour width map.

Creating the slope angle (θ) map

To include the slope angle in the formula, a slope map in degrees was created with the help of ArcGIS 9.3. First, the geographic coordinate system of the DEM was changed to a projected coordinate system with the help of the ‘project raster’ tool. The UTM coordinate system was used. Since the study area is situated in two different UTM zones, two different UTM coordinate systems were used, namely UTM19S and UTM20S. After projection of the DEM, the slope maps for each zone were calculated with the ‘slope’ tool and the two separate zones were merged together again with the ‘mosaic to raster’ tool.

Creating the LS factor map

After creating the upslope contributing area per unit contour width map and the slope angle map, Formula 7 was used to calculate the LS factor map.

2.2.4 Soil erodibility (K)

The soil erodibility factor is the soil factor in the RUSLE 3D model. It represents the soil loss rate per erosion index unit for a specified soil as measured on a standard plot, which is defined as a plot with a length of 22,1 m and a uniform nine per cent slope in continuous clean-tilled fallow (Renard et al., 1996). The K factor can be calculated with the formula (Römkens et al., 1986 in Saavedra 2005):

 2   log Dg 1,519  K = 0,0035  0,0388 exp  0,5   (Formula 8)  0,7584     

Where Dg = average geometric soil particle diameter [mm]

The average geometric soil particle diameter can be estimated with the formula (Shirazi and Boersma, 1984):

  Dg = exp 0,01 fi  lnmi  (Formula 9)  i 

Where fi = particle size fraction [%]

mi = arithmetic mean of the particle size i [mm]

Obtaining and preparing the data

At the time of this research, no information about soil structure for the whole study area nor a detailed soil map was available and therefore the soil map of the Soil and Terrain Database for Latin

Materials and methods

America and the Caribbean (SOTERLAC) version 2 was used. The map with a scale of 1 : 5.000.000 was downloaded from the website of ISRIC (ISRIC, 2011b) and clipped to the boundaries of the study area with the help of the ‘clip’ tool in ArcGIS 9.3. Soil characteristics of the soil types in the study area (clay percentage, sand percentage and silt percentage) were obtained from the World Inventory of Soil Emission Potentials (WISE) soil profiles database version 2.2 that was also downloaded from the website of ISRIC (ISRIC, 2011a). Only soil characteristics from the topsoil (0-30 cm) were used since the topsoil is most vulnerable to water erosion.

Creating the K factor map

To create the K factor map, the average geometric soil particle diameter (Dg) and the soil erodibility factor (K) for every soil type in the study area were calculated with the help of Microsoft Excel and Formulas 8 and 9. To determine the boundaries of the clay, silt and sand particle sizes, the classification made by the United States Department of Agriculture (USDA) (Shirazi and Boersma, 1984) was used:

Clay boundaries 0,000 – 0,002 mm Silt boundaries 0,002 – 0,050 mm Sand boundaries 0,050 – 2,000 mm

The calculated K factor values were then added to the table of the SOTERLAC map in ArcGIS 9.3 after which the SOTERLAC map was converted from a vector map to a raster map.

2.2.5 Cover and management (C)

The C factor is the vegetation cover and crop management factor in the RUSLE 3D model. It represents the ratio of soil loss from an area with specified cover and management to soil loss from an identical area in tilled continuous fallow (Renard et al., 1996). In easy accessible areas, the C factor can be calculated from vegetation data obtained during field work. In difficult accessible areas, remote sensing techniques can be used whereby the C factor is calculated with the help of a Normalized Difference Vegetation Index (NDVI) map, created from satellite images. Another method is to assign predefined C factor values to an existing vegetation map. In this research, the latter method was used to create the C factor map.

The ‘Mapa de Cobertura y Uso Actual de la Tierra’ (Current Land Cover and Land Use Map) of Bolivia version 2001 was used to determine the vegetation pattern in the study area. The map with a scale of 1 : 1.000.000 was downloaded from the website of ‘El Centro Digital de Recursos Naturales de Bolivia’ (Digital Centre of Natural Resources in Bolivia) (CDRNB, 2013) and clipped to the boundaries of the study area with the help of the ‘clip’ tool in ArcGIS 9.3. The different vegetation types were divided in vegetation classes (see Appendix A for an overview) after which predefined C factor values were added to each class in the table of the Current Land Cover and Land Use Map. The same C factor values as used by Saavedra during his thesis research in the Cochabamba department were used (Saavedra, 2005, p.81, Table 4.2). In Table 1, an overview of the C factor values for the vegetation classes is given. Finally, the map was converted from a vector map to a raster map.

Materials and methods

Table 1: C factor values per vegetation class (adapted from Saavedra, 2005)

vegetation class C factor value vegetation class C factor value closed evergreen forest 0,001 natural vegetation 0,1 open evergreen forest 0,01 agriculture extensive 0,09 closed semi-deciduous forest 0,03 agriculture intensive 0,1 open semi-deciduous forest 0,05 bare soil 1,0 open deciduous forest 0,07 desert 1,0 closed shrubland 0,04 gully 1,0 open shrubland 0,14 salt lake 1,0 shrub savannah 0,125 snow field 1,0 grass savannah 0,011 urban area 0 closed steppe grassland 0,06 waterbody 0 open steppe grassland 0,15 unclassified 1,0

2.2.6 Conservation practices (P)

The P factor is the erosion control practice factor in the RUSLE 3D model. It represents the ratio of soil loss with a conservation practice such as contouring, strip cropping or terracing to soil loss with straight-row farming up and down the slope (Renard et al., 1996). A low P factor (approaching zero) indicates that conservation practices are effective, while a high P factor (approaching 1,0) indicates the opposite. Predefined P factor values can be obtained from tables such as Table 6.3 from the book of Morgan (Morgan, 2005, p.123).

Results

3 Results

The RUSLE 3D model was executed with the help of ArcGIS 9.3 and Formula 1. The calculation resulted in a map where the annual soil loss by water in tons per hectare in the dryland areas of Bolivia is presented. In this Chapter, the results of the RUSLE 3D model calculation are discussed. First, the results of the different parameters are discussed separately (3.1-3.5). Then, the final map presenting the annual soil loss amounts by water in the dryland areas of Bolivia is discussed (3.6).

3.1 Rainfall-runoff erosivity (R)

The values of the rainfall-runoff erosivity factor range from 2.790,9 to 17.648,5 MJ mm ha-1 hr-1 (Figure 11). High values are found to be in the Interandean Zone, the Subandes and the Chaco Plain, while low values are found to be in the Altiplano and the Western Cordillera. The Eastern Cordillera seems to be an intermediate zone where both higher and lower values are found to be.

Figure 11: Rainfall-runoff erosivity (R) distribution [MJ mm ha-1 hr-1] in the study area

3.2 Slope length and slope steepness (LS)

The values of the slope length and slope steepness factor range from zero to 3.274,8 (Figure 12). Most slope length and slope steepness values in the study area are found to be close to zero and are scattered over the study area. Higher slope length and slope steepness values are found to be in stream patterns in all zones where flow accumulation occurs.

Results

Figure 12: Slope length and slope steepness (LS) distribution [-] in the study area

3.3 Soil erodibility (K)

In Table 2, an overview of the soil erodibility values per occurring soil type in the study area that were calculated with Formula 8, is shown.

Table 2: K factor values [ton ha-1 per unit R] per occurring soil type in the study area

soil code soil group clay % silt % sand % K factor ARh haplic arenosol 4,0 4,0 92,0 0,011542 CMd dystric cambisol 20,5 34,0 43,0 0,037953 CMe eutric cambisol 22,0 29,5 45,0 0,037350 CMx chromic cambisol 29,5 23,5 44,5 0,039980 FRh haplic ferralsol 40,0 12,0 44,0 0,041703 KSh haplic kastanozem 28,0 36,5 29,0 0,041940 KSl luvic kastanozem 22,0 44,0 42,0 0,041381 LPd dystric leptosol 17,0 21,5 59,0 0,029625 LPe eutric leptosol 19,0 29,0 53,0 0,034856 LPq lithic leptosol 27,0 29,0 42,0 0,040186 LVh haplic luvisol 18,0 19,0 57,0 0,029336 LVx chromic luvisol 23,0 17,0 55,0 0,032885 PHl luvic phaeozem 29,0 34,0 30,0 0,041869 RGd dystric regosol 14,0 20,5 63,5 0,026336 RGe eutric regosol 8,0 14,0 78,0 0,017963 SCg gleyic solonchak 27,5 30,0 35,0 0,040669 SNh haplic solonetz 24,5 21,0 53,5 0,035861 SNm mollic solonetz 26,0 42,5 26,0 0,042144

Results

Table 2 shows that the calculated soil erodibility factors are lower when the soil contains more sand particles. This is due to the fact that the large sand particles are more resistant to transport than the smaller clay and silt particles, making the soil less erodible (Morgan, 2005, p.50).

The values of the soil erodibility factor range from 0,011 to 0,042 ton ha-1 per unit R (Figure 13). Lowest soil erodibility values are found to be in the Western Cordillera, the Altiplano and in a small part of the Chaco Plain. In some parts of the Altiplano and the Chaco Plain, however, high values are also found to be. Due to the homogeneity of the soils in the Eastern Cordillera, the Interandean Zone and the Subandes, soil erodibility values do not differ much in these zones. In the other zones, where the heterogeneity of the soils is higher, the variation in soil erodibility is also higher. In these zones, both low and high K factor values occur.

Figure 13: Soil erodibility (K) distribution [ton ha-1 per unit R] in the study area

3.4 Cover and management (C)

The values of the cover and management factor range from 0,001 for areas with a high vegetation cover to 1,0 for areas with a low vegetation cover (Figure 14). Urban areas and water bodies were not taken into account in the soil erosion assessment and were therefore given a C factor of zero. Lowest cover and management values are found to be in the Chaco Plain, an area that is mainly covered with forest making it less vulnerable to soil loss by water. In the Western Cordillera, the Altiplano and the Eastern Cordillera, higher cover and management values are found to be, mainly due to the presence of (overgrazed) rangelands. Highest cover and management values are found to be in the Altiplano and the Western Cordillera due to the presence of bare soils, deserts and salt lakes. The area in the centre of the Subandes was not classified and was given a high cover and management factor value to be sure it was not classified too low.

Results

Figure 14: Cover and management (C) distribution [-] in the study area

3.5 Conservation practices (P)

No information about conservation practices in the study area was available and therefore a fixed P value of 1,0 was used in the RUSLE 3D model, indicating that no soil erosion conservation practices were implemented in the study area, making the soils most vulnerable to soil erosion.

3.6 Annual soil loss by water in the dryland areas of Bolivia

Annual soil loss by water in the dryland areas of Bolivia, calculated with the RUSLE 3D model, ranges from zero to 7.053,9 ton ha-1. In Figure 15 and Figure 16, the severity of the soil loss is shown. The soil loss classes are divided according to the classification system used by Morgan (Morgan, 2005, p.88). Soil loss amounts and indicators belonging to each class are given in Appendix B.

Figure 15 and Figure 16 show that in 60 per cent of the study area, soil loss by water is found to be low and does not reach amounts higher than five ton ha-1 yr-1. Moderate and high soil loss with amounts up to 50 ton ha-1 yr-1 are calculated for 33,7 per cent of the study area. In 6,3 per cent of the study area, soil losses are found to be very high. In these areas, soil loss can reach amounts higher than 50 ton ha-1 yr-1. While soil loss amounts up to 50 ton ha-1 yr-1 are found to be scattered over the study area, soil loss amounts higher than 50 ton ha-1 yr-1 are mainly found to be concentrated in streams. One of the main streams with high soil loss amounts is the Desaguadero River, a river flowing through the Altiplano that connects Lake Titicaca (situated North of the Altiplano just outside the boundaries of the study area) with Lake Poopó, Lake Coipasa and the Salt Lake of Uyuni, all situated in the Altiplano. In the Altiplano, high soil loss amounts are also found to be close to and in the Salt Lake of Uyuni. It is likely that the Salt Lake of Uyuni is highly vulnerable to erosion due to its erodible soil and lack of vegetation. In other zones, streams with high soil loss amounts are also found. In the Chaco Plain, high soil loss amounts are likely caused by high rainfall amounts in combination with erodible soils that contain high amounts of silt and clay. In the Interandean Zone

Results

and in the Subandes, where soils are less vulnerable to erosion than in the Chaco Plain due to their higher amount of sand particles, high soil loss amounts are likely caused by the combination of high rainfall amounts and steep slopes.

Figure 15: Soil loss severity in the study area divided in classes

Figure 16: Soil loss severity in the study area divided in percentages

Discussion

4 Discussion

As discussed in Chapter 3.6, the RUSLE 3D model results show that soil loss by water in the dryland areas of Bolivia is not severe. In more than 50 per cent of the study area, annual soil loss by water is found to be less than five ton ha-1, and where high amounts of soil loss by water do occur, they are mainly found to be concentrated in streams and in and around the Salt Lake of Uyuni. This seems not to be in accordance with literature, where it is often mentioned that soil losses in Bolivia are severe (e.g. LeBaron et al., 1979; Lauer, 1993; Zimmerer, 1993; Paulson, 2003; Kessler and Stroosnijder, 2006; Blodgett and Isacks, 2007) and it can therefore be questioned if the RUSLE 3D model is a reliable model to use in this research. In Chapter 4, the reliability of the RUSLE 3D model is assessed. First, the availability and reliability of the used data are assessed (4.1). Then, the reliability of the used methods is assessed (4.2). Finally, the reliability of the model is assessed by comparing the model results with previous obtained results from the same study area (4.3).

4.1 Data availability and reliability

Rainfall-runoff erosivity (R)

Although rainfall satellite data become more available nowadays (Ceccato and Dinku, 2010) and some researchers already use these data (e.g. Vrieling et al., 2008), in general the R factor is calculated with the help of rainfall data measured at weather stations. Since weather stations only provide point data, interpolation techniques are needed to create continuous maps that can be used in the RUSLE 3D model. Interpolation techniques make it possible to predict the values of cells where sampling is not possible. The techniques are based on the principle of spatial autocorrelation or spatial dependence that measures degree of relationship or dependence between near and distant objects (Childs, 2004). Different interpolation techniques, based on stochastic or deterministic models, were compared by Khorsandi et al. (2012). They found that co-kriging, defined as “a form of kriging in which the distribution of a second, highly correlated variable (covariate) is used along with the primary variable to provide interpolation estimates” (ESRI, 2013) was the most suitable method to create R factor maps, followed by kriging, an “interpolation technique in which the surrounding measured values are weighted to derive a predicted value for an unmeasured location” (ESRI, 2013). In this research ordinary kriging was used, what is assumed to be a suitable interpolation technique when the research of Khorsandi et al. (2012) is taken into account.

Interpolation results are most reliable when the sample points are evenly distributed over the study area, preferably at small distances from each other. In the dryland areas of Bolivia, however, weather stations are unevenly distributed over the study area. Most weather stations are situated in the Eastern Cordillera, the Interandean Zone and the Subandes, with a cluster of weather stations in the Southern part of the Interandean Zone and in the Northern part of the Eastern Cordillera (Figure 17). In these areas, interpolation results are assumed to be more reliable than in the Altiplano, the Western Cordillera and the Chaco Plain, where fewer weather stations are situated and where the distance between the weather stations can be high. This assumption is strengthened by Lu and Yu (2002), who compared R factor values (predicted by an erosivity model and 20-year daily rainfall data) with R factor values (calculated with rainfall data) at several sites in Australia. Lu and Yu found that large differences existed between predicted and calculated R factor values. These differences

Discussion

were partially caused by large interpolation errors due to a sparse weather station density in these areas.

Figure 17: Distribution of weather stations over the study area

For a reliable calculation of the R factor, Wischmeier and Smith (1978) recommended to use rainfall data from a period of at least 20 years so that natural climatic variations would not lead to calculation errors. However, from the 663 weather stations that are present in the study area, only 34 weather stations could provide data over the last 20 years (from 1991 to 2010) without data gaps. These weather stations are indicated with a black dot in Figure 17. Figure 17 shows that weather stations with at least 20 years of continuous data storage are only situated in the Eastern Cordillera, the Interandean Zone and the Subandes. Only one weather station with more than 20 years of continuous data storage was situated in the Chaco Plain and no weather stations with more than 20 years of continuous data storage were situated in the Western Cordillera and the Altiplano. To be able to interpolate the data anyway, data from weather stations with less than 20 years of continuous data storage were also included in the interpolation. However, this can lead to a distorted map due to calculation errors caused by natural climatic variations.

Slope length and slope steepness (LS)

With the increase in remote sensing techniques and availability of satellite data, it has become easier to assess soil erosion at a large scale. DEMs are often used to represent the LS factor in soil erosion modelling. The correct choice of the DEM resolution is hereby an important aspect because resolution influences the topographical model parameters and thus the final model results (Datta and Schack-Kirchner, 2010). According to Rojas et al. (2008), for USLE based soil erosion models it is best to choose a DEM resolution that is close to the 22,1 m standard plot size of the USLE. Several studies (e.g. Cotter et al., 2003; Chaubey et al., 2005; Rojas et al., 2008) have shown that low DEM resolutions result in low topographical details, making the representation of stream networks less accurate. When DEM resolutions decrease, average watershed areas and slope steepness’s also decrease while average slope lengths increase, leading to a change in runoff volume and soil erosion

Discussion

estimates (Cotter et al., 2003; Chaubey et al., 2005; Rojas et al., 2008). At DEM resolutions lower than 150 m, soil erosion is no longer calculated in a correct way (Rojas et al., 2008).

In the past, DEMs were mainly created by interpolating vectorized contour lines that were digitized from topographical maps (Datta and Schack-Kirchner, 2010) or from aerial photographs. Improved remote sensing techniques, however, have made it possible to extract DEMs directly from optical satellite images. Two general used DEMs that were created by the latter technique are SRTM DEM (Shuttle Radar Topography Mission DEM) and ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM). Both DEMs cover almost the entire continent and can be easy and freely downloaded from the internet. In this research, ASTER GDEMs were used to calculate the LS factor. ASTER GDEMs were chosen over SRTM DEMs because of their higher spatial resolution (ASTER GDEM 30 m resolution versus SRTM DEM 90 m resolution), what was assumed to give better results.

Several validation studies have been executed in the past to assess the accuracy of among others ASTER GDEMs and SRTM DEMs (e.g. ASTER GDEM Validation Team, 2009; Datta and Schack-Kirchner, 2010; Forkuor and Maathuis, 2012; Wang et al., 2012). In general, these studies have shown that, although being within predefined accuracy specifications, ASTER GDEM underestimates elevations while SRTM DEM, on the other hand, overestimates elevations. For both DEMs, errors in elevation calculation were higher in mountainous areas than in flat areas. Datta and Schack-Kirchner (2010) have also calculated slope steepness’s and slope lengths with the help of DEMs. They found that slope steepness’s calculated with DEMs were more gentle compared with slope steepness’s measured in the field, and slope lengths calculated with ASTER DEMs were lower than those calculated with SRTM DEMs, whereby slope length values calculated with ASTER DEMs were closer to field measurements than SRTM DEM slope length values. The latter, however, was probably caused by artifacts in the ASTER DEMs and may therefore not be considered as an accurate representation of reality (Datta and Schack-Kirchner, 2010).

Kamp et al. (2005) have assessed the accuracy of an ASTER DEM, generated from satellite data, in the Chilean and Bolivian Andes. They compared the elevation of the Cerra Sillajhuay, a 5.982 m ASL situated volcano, obtained from an ASTER DEM with field measurements and found that “the overall quality of the ASTER DEM was outstanding, with only few artifacts mostly representing lakes”. Until an altitude of 5.000 m ASL, elevations were of good accuracy, while above 5.000 m ASL, elevations were calculated slightly too low. Slope lengths were also calculated slightly too low. The general conclusion of Kamp et al. (2005) was that “ASTER data might be useful for geomorphological mapping especially at medium scales (1 : 100.000 and 1 : 50.000)”.

Although above mentioned validation studies have shown that both ASTER GDEM and SRTM DEM do not fully represent reality, both DEMs can be considered useful and good replacements for DEMs obtained from contour lines (Forkuor and Maathuis, 2012). The accuracy of SRTM DEM however, is better than the accuracy of ASTER GDEM, despite its lower resolution, and does therefore better represent reality (ASTER GDEM Validation Team, 2009; Datta and Schack-Kirchner, 2010; Forkuor and Maathuis, 2012; Wang et al., 2012).

Discussion

Soil erodibility (K)

The most suitable way to calculate the K factor is by using data obtained during field work. In the dryland areas of Bolivia, many soil samples have been gathered over the years during field work. However, soil sample data that were available for this research could not be used because they were not geo-referenced and therefore sample locations were unknown (pers. comm. K. Ovando Crespo, 2011). Instead, to calculate the K factor, soil types were obtained from a soil map and soil properties were obtained from an existing database. Several soil maps can be used. While Saavedra (2005) used the USDA Soil Taxonomy Map, in this research the soil map of the Soil and Terrain database for Latin America and the Caribbean (SOTERLAC) was used. Although both maps were created in a different way and both generated different outputs (Figure 18), one map was not considered to give a better representation of the reality than the other map. The use of the SOTERLAC map in this research was basically chosen on the fact that it was available in a digital format and therefore easier to work with in ArcGIS 9.3. The large scale of the map (1 : 5.000.000), however, does not make it possible to go into detail and display small scale soil units and can therefore deviate from the reality, but this is equal true for the USDA Soil Taxonomy Map.

Figure 18: USDA Soil Taxonomy map versus SOTERLAC map

For many soil types, K factor values have been calculated and stored in databases throughout the years. The USDA Natural Resources Conservation Service database, for example, contains K factor values for major soil types and soil textures from the United States (Wang et al., 2001). In this research, the World Inventory of Soil Emission Potentials (WISE) database was used. The data stored in this database are based on global data and not on physical soil measurements in the study area

Discussion

itself (Claessens et al., 2008). However, since soils are affected by amongst others climatic factors, temperature, rainfall and vegetation (Wang et al., 2001), their properties depend highly on location. Soil properties taken from existing databases might therefore deviate from the actual soil properties as measured in the field and may lead to miscalculation of K factor values. This was confirmed by Wang et al. (2001) who calculated K factor values from existing databases for several soil types in the United States. They found that only for a few soil types in their study area, correct K factors could be calculated. However, for the most soil types, calculated K factor values were underestimated. On the other hand, both Romero et al. (2007) and Vaezi and Sadeghi (2011), who compared measured and calculated K factor values in the Northern Andean Cordillera in Peru and in a semi-arid region in Iran respectively, found that calculated K factor values were higher than measured K factor values.

Cover and management (C)

Nowadays, remote sensing techniques are the most common way to calculate the C factor. For this research, it was decided to calculate the C factor with the help of an NDVI map since this map is highly correlated with vegetation cover and biomass (Saavedra, 2005) and it was therefore considered to be an easy and reliable tool to calculate the C factor for such a large area as the dryland areas of Bolivia. To create the NDVI map, several data were needed. Satellite images were needed to calculate the NDVI map, actual ground coordinates were needed to geometric correct the satellite images, and C values obtained from field samples were needed to calibrate the NDVI map.

Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and AVHRR (Advanced Very High Resolution Radiometer) images are often used to calculate NDVI maps. In this research, Landsat images were chosen over MODIS and AVHRR images because of their higher spatial resolution (30 m for Landsat images versus 250 m and 8.000 m for MODIS and AVHRR images respectively) what makes it possible to calculate a more detailed NDVI map. Currently, two Landsat satellites are still in orbit: Landsat 5 and Landsat 7. Of these two satellites, the availability of Landsat 7 images is higher than the availability of Landsat 5 images (Kovalskyy and Roy, 2013). In May 2003, however, the scan line corrector from the Landsat 7 satellite failed what resulted in data gaps on the images (USGS, 2012). The failure led to a reduction of the usable data of each Landsat 7 image by about 22 per cent (Markham et al. 2004 in Kovalskyy and Roy, 2013; Ju and Roy, 2008) and since it was not possible to repair the defective scan line corrector, this data reduction still continues nowadays. For this research, it was therefore decided to use Landsat 5 images instead of Landsat 7 images, despite the higher availability of the latter. Landsat 5 images can easy and freely be downloaded from the Landsat database at the United States Geological Survey (USGS) Centre website.

To not disturb the classification process and to obtain reliable results it is important that satellite images contain no or only few clouds. However, not only cloud cover influences the reliability of the results, the time period from which images are obtained can also influence their reliability. Since the amount of vegetation is correlated to time of year, only images taken in the same time period, preferably in the same year, should be used. Images taken at the beginning of the wet season could be used best since soils are most vulnerable to soil erosion at that time period due to lack of vegetation cover (especially at agricultural fields) in combination with the start of (high intensity) rainfall events. When images cannot be obtained from the same time period, they have to be obtained from different time periods, when seasonal and atmospheric circumstances are likely to be different what can have negative effects on the end result (Hansen and Loveland, 2012).

Discussion

The Landsat 5 satellite has a repeat cycle of 16 days, what means that the satellite crosses every same point on the earth once every 16 days (USGS, 2012). This relatively low repeat cycle can limit the ability of image acquisition from the same time period. When looking at the available data in the Landsat database, it was shown that in October 2009 of the 34 Landsat 5 images that were needed to cover the total study area, 28 images with less than 10 per cent cloud cover could be obtained and six images needed to be obtained from another time period or with a higher amount of cloud cover. For other months or other years, the situation was worse: images contained more cloud cover or could not all be obtained from the same month. Especially in the vicinity of lakes, clouds occurred in a higher frequency (more than 10 per cent) and images with few or no clouds could not be obtained from the same time period.

Different processing stages for downloadable Landsat images are available: an unprocessed stage, a level 1T stage, and a level 1G stage. At a Level 1T stage, systematic radiometric, geometric and topographic corrections are executed with the help of ground control points and a DEM. At a Level 1G stage, systematic radiometric and geometric corrections are also executed, but this time with the help of data collected by the sensor. Radiometric and geometric correction are important steps to obtain reliable results, especially when multiple images are used in one research (Hansen and Loveland, 2012). Looking again at the available data of October 2009, it was shown that of the 28 available cloud poor images, 17 images were corrected at a Level 1T stage, two images were corrected at a Level 1G stage and the other nine images still needed to be corrected. However, actual ground control points were not available in this research and therefore these corrections could not be performed for the nine unprocessed images. C values obtained from field samples were not available either, so even if it would have been possible to calculate an NDVI map from reliable Landsat images, this map could not be calibrated.

Without geometric correction and calibration of the NDVI map, it was assumed that the final result would be too unreliable and it was therefore decided to use another technique to calculate the C factor whereby predefined C factor values were assigned to an existing vegetation map. The Current Land Cover and Land Use Map of Bolivia was used for this goal. This map, based on Landsat 7 images obtained between May and September 1999-2000, was radiometric and geometric corrected with the help of topographic maps (Rico et al., 2002). Due to the large scale of the map (1 : 1.000.000), however, it is not possible to show detailed vegetation patterns and only an overall overview of the vegetation types can be given what might deviate from the actual situation. Also, since vegetation patterns are highly dynamic and can change from year to year or even within one season, the current situation might be changed and the map, which was produced in 2001, might be outdated.

Conservation practices (P)

Information about conservation practices was not available and therefore the highest P factor value, indicating that no conservation practices are executed, was used. However, since conservation practices are mainly executed on agricultural fields and agricultural fields only represent a minor part of the study area (2,4 per cent of the total study area), the lack of information about conservation practices was not considered to be important in this research.

Discussion

4.2 Method reliability

Rainfall-runoff erosivity (R)

The original way to calculate the R factor is by using Formula 2 developed by Wischmeier and Smith (1978). However, using this formula requires detailed rainfall information that is not always available. When detailed rainfall data is not available, the R factor needs to be calculated with the help of other formulas. In this research, the Modified Fournier Index was used. While some researchers found a good correlation between the R factor and the Modified Fournier Index (e.g. Munka et al., 2007; Shamshad et al., 2008), other researchers (e.g. Angulo-Martínez and Beguería, 2009) found that using the Modified Fournier Index resulted in severe underestimation of the R factor. Moreover, Renard and Freimund (2004) found that using the Modified Fournier Index can lead to R factor estimation errors that can have large effects on predicted soil loss amounts, although these effects tend to decrease with increasing R factor values. According to Renard and Freimund, large estimation errors mainly occur when R factor values are lower than 3.000 MJ mm ha-1 hr-1.

In this research, calculated R factor values ranged from 2.791 to 17.649 MJ mm ha-1 hr-1 (see also Chapter 3.1). Since rainfall-runoff erosivity depends highly on the amount of rainfall, high R factor values should be found in areas with high rainfall amounts, while low R factor values should be found in areas with low rainfall amounts. This pattern is also found back in this research. However, although the pattern seems to be correct, the calculated R factor values seem to be overestimated when compared with other researches. Da Silva (2004) for example, calculated R factor values for Brazil. In the Eastern part of Brazil, where rainfall amounts are similar to the rainfall amounts in this research, Da Silva calculated R factor values ranging from 2.000 to 10.000 MJ mm ha-1 hr-1. These values are slightly lower than the R factor values calculated in this research. Bonilla and Vidal (2011) however, calculated R factor values ranging from 206 to 1.635 MJ mm ha-1 hr-1 for rainfall amounts ranging from 320 to 1.066 mm yr-1 in Central Chile, values that are much lower than the R factor values calculated in this research. Finally, Ochoa-Cueva et al. (2013) calculated R factor values for a watershed in the Southern Andes of Ecuador. Although their calculated values are similar to the R factor values calculated in this research, the amount of rainfall in their study area is much higher than in the study area of this research.

A typographical error occurs in Formula 3, according to Yu and Rosewell (1996). It is not clear whereupon their statement is based, but according to Yu and Rosewell the correct formula must be:

R = 0,7397 * MFI1,847

When recalculating the R factor with this formula, the results increase with a factor 10, as is shown in Table 3. In this Table, an overview is given of the original R factor values and the recalculated R factor values of some of the weather stations in the study area. The first five R factor values (where MFI < 55 mm) were calculated with Formula 3 and its corrected version, while the last five R factor values (where MFI > 55 mm) were calculated with Formula 4. It is shown that a large gap exists between the original R factor values of weather stations #5 and #6. This gap has disappeared when the R factors were recalculated with the corrected formula, what might indicate that Yu and Rosewell were right about the incorrectness of the original formula.

Discussion

Table 3: Overview of original and recalculated R factor values with the formula as proposed by Yu and Rosewell (1996)

Modified Fournier R factor original R factor recalculated # weather station Index [mm] [MJ mm ha-1 hr-1] [MJ mm ha-1 hr-1] 1 Copacabana Taxara 54,95 120,99 1209,85 2 Punilla 54,95 121,00 1209,98 3 Guaqui 54,96 121,03 1210,31 4 Ayo Ayo 54,98 121,12 1211,22 5 Tarabuco 54,99 121,17 1211,66 6 Llica 55,00 1204,24 1204,24 7 Viacha 55,01 1204,52 1204,52 8 Uyuni 55,01 1204,89 1204,89 9 Rincon Cañas H 55,02 1204,96 1204,96 10 Vinto 55,02 1205,12 1205,12

Using the corrected formula increased the total R factor values in the study area from 2.791 - 17.649 MJ mm ha-1 hr-1 to 2.930 - 18.171 MJ mm ha-1 hr-1 (not shown). The increased R factor values, however, do hardly influence the amount of the annual soil loss by water, as is shown in Figure 19. While the lower boundary of the annual soil loss stays the same, the upper boundary only increases from 7.053,95 to 7.088,27 tons of soil loss per hectare per year.

Figure 19: Original RUSLE 3D model versus recalculated RUSLE 3D model

Discussion

Slope length and slope steepness (LS)

Different ways exist to calculate the LS factor. In this research, the formula of Mitasova et al. (1996) was used. Although this formula has the advantage of including flow convergence and flow divergence, what makes it suitable for areas where both erosion and deposition takes place, the formula has limitations too. First, the formula is best suitable for use with a DEM that has a resolution between two and 20 m (Mitasova et al., 1996). Second, the values of m = 0,6 and n = 1,3 that are used in the formula only give satisfying results for slope angles less than 14 degrees and slope lengths less than 100 m (Moore and Wilson, 1992). Slope lengths were not calculated in this research. However, many slope angles in the study area are steeper than 14 degrees, especially in the Eastern Cordillera, the Interandean Zone and the Subandes (Figure 20). Moreover, the DEM that was used in this research, has a resolution of 30 m what is slightly too low for use in the formula. Both factors might make results less reliable.

Figure 20: Overview of slope levels [degrees] in the study area

In this research, calculated LS factor values ranged from zero to 3.274,8 (see also Chapter 3.2). Compared with LS factor values that were calculated in other, comparable, study areas (Table 4), these values seem to be very high. However, when looking into detail (Figure 21), it is shown that only 0,05 per cent of all the calculated LS factor values in the dryland areas of Bolivia reaches values higher than 150 (what seems to be the upper boundary of the calculated LS factors in the other study areas, Table 4). It is most likely that values above 150 are calculated incorrect due to the presence of artifacts in the DEM (as already discussed in Chapter 4.1) or due to the presence of slope lengths longer than 100 m and slope steepness’s higher than 14 degrees.

Discussion

Table 4: Overview of LS factor values calculated for other (comparable) study areas

slope level [°] LS factor [-] methods used study area reference 2,9 - 28,8 0,8 - 23,0 unknown Kenya Angima et al. (2003) Mitasova et al. (1996) and 0 - 49 0,08 - 24,38 Ecuador Ochoa-Cueva et al. (2013) Nearing (1997) > 16,7 0 - 28,0 Moore and Burch (1986) Thailand Krishna Bahadur (2009) 0 - 54,6 0 - 43,1 Van Remortel et al. (2001) China Fu et al. (2005) > 24 0,1 - 53,1 McCool et al. (1987) India Dabral et al. (2008) 7,4 - 11,3 0 - 149,73 McCool et al. (1982) Colombia Kingston (1997) Wischmeier and Smith (1978) > 36,9 > 75 Colombia Hoyos (2005) and Nearing (1997)

Figure 21: Overview of LS factor values in the study area divided in percentages

The histogram in Figure 21 also shows that almost 96 per cent of all the LS factor values calculated for the dryland areas of Bolivia falls in the range from zero to one. This would mean that in almost 96 per cent of the study area, the LS factor will not contribute to an increase in soil loss, but to a decrease in soil loss instead. In an area where so many steep slopes occur, this situation seems unrealistic and it can therefore be questioned if the formula of Mitasova et al. (1996) is the correct formula to use in this research. For comparison, the LS factor is recalculated with the original formula of Wischmeier and Smith (Formula 6). The results in Figure 22 show that when using Formula 6, LS factor values range from zero to 48,20 which is more in accordance with the results from the other study areas mentioned in Table 4. Moreover, Figure 22 shows that only 2,5 per cent of the LS factor values calculated with Formula 6 is less than one and therefore most LS factor values will contribute to an increase of soil loss in the study area, what is more likely in an area where many steep slopes occur.

Discussion

Figure 22: LS factor values [-] calculated with the formula of Wischmeier and Smith (1978)

Soil erodibility (K)

The most reliable method to assess soil erodibility is by using field data obtained over a period of at least 20 years (Wischmeier and Smith, 1978). However, this method is expensive and labour intensive and therefore data are not always available. An alternative is to calculate the K factor with the help of formulas. In this research, two formulas were used to calculate the K factor. First, the average geometric soil particle diameter for each soil type was calculated which was then used in the formula of Römkens et al. (Formula 8) to calculate the K factor. Since Formula 8 is only based on soil texture (clay, silt and sand percentages), it can be used when only limited soil information is available. However, some researchers have found that calculated K factor values were overestimated when using the average geometric soil particle diameter (e.g. Vaezi and Sadeghi, 2011; Wang et al., 2012), although the latter found that the differences between calculated and measured K factor values were not significant. Not only soil texture, but also other soil properties, such as soil structure, soil permeability, aggregate stability, soil moisture and organic matter content, influence soil erodibility (Torri et al., 1997; Wang et al., 2001; Morgan, 2005; Vrieling, 2006, Vaezi and Sadeghi, 2011). When calculating K factor values, Torri et al. (1997) found no relationship between average geometric soil particle diameter and the K factor only. However, they did find a relationship when the average geometric soil particle diameter was combined with clay fraction by mass and organic matter content of the soil. Due to the two above mentioned drawbacks (overestimation and lack of relationship between average geometric soil particle diameter and K factor), Formula 8 might not the best suitable formula to calculate the K factor. However, since only information about soil texture was available in this research, Formula 8 was the only formula available for this research.

Calculated K factor values in the dryland areas of Bolivia ranged from 0,011 to 0,042 ton ha-1 per unit R (see also Chapter 3.3). To find out if these values are realistic, a literature study was executed whereby K factor values from other research areas were searched for. The result of the literature study is shown in Table 5. When comparing the calculated K factor values from the dryland areas of Bolivia with the results in Table 5 it is shown that the calculated K factor values of only three soil types in the dryland areas of Bolivia (Cambisols, Ferralsols and Leptosols) fall completely within the K factor value ranges found in the literature study, while calculated K factor values of the Luvisols and

Discussion

Regosols fall partly within these ranges. K factor values of the Phaeozems seem to be calculated too high, while K factor values of the Arenosols, Kastanozems and Solonetz seem to be calculated too low. For Solonchak, no information was found. Based on the results found in the literature study it is difficult to say if the K factor values calculated for the dryland areas of Bolivia are correct, although there seems to be a slight tendency of underestimation of the calculated K factor values in this research.

Cover and management (C)

Cover and management factors were not calculated but predefined values were used instead. The same C factor values as used by Saavedra (2005) during his thesis research were taken, since these values seemed to be suitable when they were used in the Cochabamba department in Bolivia. However, when assigning C factor values to the different vegetation classes, not all values were known and some assumptions had to be made. For a closed evergreen forest and a closed semi- deciduous forest, the predefined C factor values could be used. Open evergreen forests and open semi-deciduous forests, however, were assumed to be more vulnerable to soil erosion than the closed forests and were therefore given a higher C factor value than their closed equivalents. An open deciduous forest was assumed to be even more vulnerable to soil erosion than all the other forest types and was therefore given a slightly higher C factor value than those forests. Salt lakes and gullies were assumed to be highly vulnerable to soil erosion and were therefore given the highest C factor value available (1,0). Extensive agriculture was assumed to be less vulnerable to soil erosion than intensive agriculture and therefore, extensive agriculture was given a lower C factor value than intensive agriculture. For natural vegetation, the same C factor value as for intensive agriculture was chosen, since it was not clear what the natural vegetation existed of and the vegetation was therefore assumed to have similar characteristics as intensive agriculture. Finally, unclassified areas were given the highest C factor value, to be sure they were not classified too low. Since the assumptions were not based on actual measurements but on common sense instead, incorrect values might be assigned to some vegetation classes which increases the unreliability of the C factor values of these classes.

The largest assumption that was made, however, was that climatic and soil conditions at different locations are similar and that one particular vegetation type has the same C factor value at all the different locations where it occurs. In reality this is not true but it is almost impossible to take climatic and soil conditions into account when assigning C factor values. Different climatic and soil conditions also make it very difficult to compare C factor values calculated in this research with C factor values calculated in other researches and comparison was therefore not done. It is therefore likely that C factor values differ from reality, but it is not known if they should be higher or lower.

Conservation practices (P)

Since no information about conservation practices in the study area was available, a fixed P factor values was used instead and methods do not need to be discussed here.

Discussion

Table 5: Overview of K factor values found in other research areas

soil type K factor [ton ha h method used reference ha-1 MJ-1 mm-1] 0,020 unknown Shiferaw (2011) 0,035 literature study Da Silva and Alvares (2005) Arenosol 0,100 Wischmeier nomograph Sanroque et al. (1990) 0,194 ERFAC equation1 Ashiagbor et al. (2013) 0,036 lineair regression2 Da Silva et al. (2009) 0,050 Mulengara and Payton (1999) equation3 Vezina et al. (2006) 0,051 literature study Da Silva and Alvares (2005) Cambisol 0,130-0,220 unknown Shiferaw (2011) 0,200-0,500 Wischmeier and Smith (1978) equation4 Vopravil et al. (2007) 0,300 Wischmeier nomograph Sanroque et al. (1990) 0,400 Wischmeier and Smith (1978) equation4 Khormali et al. (2009) 0,014 literature study Da Silva et al. (2012) 0,016 literature study Da Silva and Alvares (2005) Ferralsol 0,050 Wischmeier nomograph Igwe (2003) 0,080 Wischmeier and Smith (1978) equation4 Smaling et al. (1993) 0,050 unknown Gelten (2010) Kastanozem 0,100 Wischmeier nomograph Sanroque et al. (1990) 0,220-0,350 Wischmeier and Smith (1978) equation4 Khormali et al. (2009) 0,028 Mulengara and Payton (1999) equation3 Vezina et al. (2006) 0,036 literature study Da Silva et al. (2012) Leptosol 0,275 ERFAC equation1 Ashiagbor et al. (2013) 0,300 unknown Shiferaw (2011) 0,032-0,042 literature study Da Silva et al. (2012) 0,250 Wischmeier nomograph Sanroque et al. (1990) 0,260-0,300 Wischmeier and Smith (1978) equation4 Khormali et al. (2009) Luvisol 0,295 ERFAC equation1 Ashiagbor et al. (2013) 0,310-0,600 Wischmeier and Smith (1978) equation4 Vopravil et al. (2007) 0,400 USLE equation Barber et al. (1979) 0,031 literature study Da Silva and Alvares (2005) 0,065 Mulengara and Payton (1999) equation3 Vezina et al. (2006) Phaeozem 0,100 Wischmeier nomograph Sanroque et al. (1990) 0,300-0,340 Wischmeier and Smith (1978) equation4 Vopravil et al. (2007) 0,025 Mulengara and Payton (1999) equation3 Vezina et al. (2006) 0,050 Wischmeier and Smith (1978) equation4 Torri et al. (2013) Regosol 0,070 unknown Shiferaw (2011) 0,170-0,220 Wischmeier and Smith (1978) equation4 Vopravil et al. (2007) 0,350 Wischmeier nomograph Sanroque et al. (1990) 0,170 unknown Gelten (2010) Solonetz 0,351 ERFAC equation1 Ashiagbor et al. (2013) 1 ERFAC equation: K = 0,32 * [% silt / (% sand + % clay)] 2 linear regression: soil loss = erodibility * erosivity 3 Mulengara and Payton (1999): K = 1,82247 * M *(10-5) + 0,0045 * Pe – 0,0097 4 Wischmeier and Smith (1978): 100K = 0,1317 * [2,1*(M1,14)*(10-4)*(12-a) + 3,25*(b-2) + 2,5*(c-3)]

Discussion

4.3 Model reliability

Due to spatial and temporal variability of input parameters and due to measurement errors caused by spatial interpolation techniques, accurate erosion prediction is difficult (Jetten et al., 2003). This is shown by the RUSLE model which seems to over predict small annual soil losses and under predict large annual soil losses (Risse et al., 1993). Validation of obtained results with independent data is therefore an important step in soil erosion assessments. However, since validation methods can be labour intensive or might require long term data they are often not executed in soil erosion assessments, making these assessments less reliable as soil erosion assessments that have been validated by independent data.

Different validation methods exist, such as the measurement of soil erosion, runoff or sediment accumulation, or quantitative surveys such as the repetitive measurement of rill volumes (Vrieling, 2006). Another option is to use high resolution images such as QuickBird or IKONOS images to visually detect erosion features such as gullies or landslides and compare these with the soil erosion assessment (Vrieling, 2007). In this research, it was decided to search for previous obtained results from the same study area and compare these with the results of the RUSLE 3D model calculation in this research to see if there are significant differences. However, since not many quantitative soil erosion assessments have been executed in the dryland areas of Bolivia, results from previous researches were difficult to obtain. Only eight soil erosion assessments were found in the literature. These assessments are listed in Table 6.

Table 6: Overview of soil erosion assessments that have been executed in the dryland areas of Bolivia

soil loss # location method time period source [ton ha-1 yr-1] field measurements 1985-1986 Bastian and Gräfe, 1 Tarija basin 10-230 (runoff plots) rainy season 1989 Rio Desaguadero basin (a) 0,59 (a) calculations based on 2 Rio Mauri basin (b) 1976-1982 6,40 (b) Guyot et al., 1990 field measurements Rio Desaguadero basin (c) 2,90 (c) 3 Calicanto River watershed modelling (USLE) ? 114-173 Zimmerer, 1993 qualitative Metternicht and 4 Sacaba Valley remote sensing 1994 assessment Zinck, 1998 field observations Camacho River near 12,4 ton ha-1 Coppus and Imeson, 5 during an extreme 28.11.1999 Tarija (for one event) 2002 rainfall event 6 Cochabamba department modelling (RUSLE 3D) ? > 128 Saavedra, 2005 Kayakas field observations + qualitative Kessler and 7 Tomoroco 1963-2003 farmer interviews assessment Stroosnijder, 2006 Sirichaca modelling (PSIAC 8 Tupiza basin April 2010 9,10 ± 7,52 Vezzoli et al., 2013 model)

Table 6 shows that soil loss amounts range from 0,59 ton ha-1 yr-1 in the Rio Desaguadero basin to 230 ton ha-1 yr-1 in the Tarija basin. These amounts are much lower than the soil loss amounts calculated in this research (0 - 7.053,9 ton ha-1 yr-1). However, reclassifying Figure 15 using the upper boundary of 230 ton ha-1 yr-1 of soil loss found in the Tarija basin (Table 6) shows that in only 0,9 per

Discussion

cent of the dryland areas of Bolivia soil loss amounts reach amounts higher than this upper boundary (Figure 23), indicating that in 99,1 per cent of the dryland areas of Bolivia, soil loss amounts fall within the range of the soil loss amounts found in the validation areas mentioned in Table 6.

Figure 23: soil loss rates in the dryland areas of Bolivia calculated with RUSLE 3D

It should be taken into account that the soil loss amounts in the validation areas from Table 6 were calculated for small areas only. Moreover, soil loss distribution in the validation areas is not shown. In this research, high soil loss amounts were mainly found in streams and in and around the Salt Lake of Uyuni and it is not known if such areas also occur in the validation areas. To look more into detail, the results from the research of Saavedra (2005) were compared with the results from this research whereby only the Cochabamba area was taken into account. The area of interest was clipped to the boundaries of the Cochabamba department so it could be better compared with the results obtained by Saavedra. Unfortunately, only the Southern part of the Cochabamba department could be compared because the Northern part was situated outside the boundaries of the study area in this research. In Figure 24, the results of this research and the research of Saavedra are shown.

Figure 24: Comparison of the results from this research with the results from the research of Saavedra (2005)

Discussion

Figure 24 shows that although high soil loss amounts of more than 128 ton ha-1 yr-1 are found in both study areas, differences also occur between both study areas. While a clear stream pattern with soil loss amounts higher than 128 ton ha-1 yr-1 is found along the Southern border and in the Eastern part of this research, this stream pattern is not found back in the research of Saavedra. Moreover, soil loss amounts calculated in this research seem to be slightly higher than soil loss amounts calculated in the research of Saavedra. This can best be seen in the centre of the study area where the majority of the soil loss amounts in the research of Saavedra is less than two ton ha-1 yr-1, while the majority of the soil loss amounts reaches values up to eight ton ha-1 yr-1 in the same area of this research. Compared with the research of Saavedra, soil loss amounts calculated in this research seem to be more in accordance with literature where it is often mentioned that soil erosion in Bolivia is severe, but it should be taken into account that in this comparison only (part of) the Cochabamba department was taken into account and not the whole study area. The situation can be different in other parts of the study area.

Conclusion and recommendations

5 Conclusion and recommendations

In this research, the RUSLE 3D model was used to calculate soil loss by water in the dryland areas of Bolivia. The results showed that calculated soil loss amounts range from zero to 7.053,9 ton ha-1 yr-1. These values seem to be very high, but when looking more into detail, it was found that in more than 50 per cent of the study area, soil loss amounts were low and did not reach values higher than five ton ha-1 yr-1 and when soil loss amounts were high, they were mainly concentrated in streams and in and around the Salt Lake of Uyuni. This seems not to be in accordance with literature where it is often mentioned that soil losses in Bolivia are severe. It can therefore be questioned if the calculated results are reliable and if the RUSLE 3D model is a suitable model to use in this research.

When assessing the reliability of the RUSLE 3D model, several things were noticed. First, when using maps, spatial resolution and map scale are important because higher spatial resolutions and smaller map scales will result in more detailed end results. In this research, ASTER GDEMs were used to calculate the LS factor while soil and vegetation maps were used to calculate the K factor and the C factor. Map scale for both the soil and the vegetation map were large making it difficult to go into detail and display small scale soil and vegetation units. The maps can therefore deviate from reality and results might be less reliable. ASTER GDEM on the other hand, had a spatial resolution of 30 m what was considered a suitable spatial resolution for LS factor calculation. This was confirmed by researchers, who found that models using ASTER GDEM performed very well, especially at altitudes lower than 5.000 m.

Second, data that is used can increase unreliability for several reasons. The WISE database that was used to obtain soil properties was based on global data and not on data from the study area itself. Since soil properties depend highly on location, calculated results can deviate from the reality and make results less reliable. This was confirmed by researchers who found both underestimation and overestimation when calculating K factors based on global databases. Moreover, the vegetation map that was used dated from 2001 and was probably outdated because vegetation patterns are highly dynamic and can change from year to year and even within one season.

Third, interpolation techniques can increase unreliability when they are not performed in a correct way. In this research, interpolation was needed to create the R factor map. The ordinary kriging interpolation method that was used in this research was considered a good technique according to researchers. However, weather stations were unevenly distributed over the study area and most of them lacked long term continuous rainfall data, making the interpolation results less reliable, especially in the Western Cordillera, the Altiplano and the Chaco Plain.

Fourth, different formulas can be used in the RUSLE 3D model and they should be chosen with care. The Modified Fournier Index formula that was used to calculate the R factor was found to give good results by some researchers, while other researchers found underestimation of the calculated values. The formula that was used to calculate the K factor seems to overestimate calculated values, according to researchers, but since no alternative formulas were available for this research it was used anyway. The formula of Mitasova et al. that was used to calculate the LS factor could best be used in areas where slope lengths are less than 100 m and slope angles are less than 14 degrees. In this study area, however, slope angles are often more than 14 degrees, especially in the Eastern Cordillera, the Interandean Zone and the Subandes, making results in these areas less reliable.

Conclusion and recommendations

Not only the reliability of the data and methods used was assessed, the availability of data was assessed too. It was found that data is in general easy and freely accessible on the internet. ASTER GDEMs could be downloaded from the ASTER GDEM website, rainfall data could be obtained from the SENAMHI website and digital soil and vegetation maps could be downloaded from respectively the ISRIC and CDRNB websites.

However, although rainfall data could easily be obtained, it contained a lot of missing data and not many long term data were available, making interpolation results and therefore end results less reliable. Field data to calculate the K factor were considered to be the best data to use but unfortunately, field data for this study area were not available. A digital soil map and a global soil properties database were used instead. Both map and database could be freely downloaded from the ISRIC website. To determine the P factor, no information about conservation practices in the study area was available at all. This seems to be a common problem in many researches. In this research, however, lack of information about conservation practices was not considered to be important because conservation practices are mainly applied in agricultural areas and agricultural areas represented only a minor part in this study area. A fixed P factor value could be used instead and was considered appropriate in this research.

The largest problems occurred when data for C factor calculation needed to be obtained. In first instance, it was decided to calculate the C factor with the help of an NDVI map. For this, Landsat data were needed. Although Landsat data are easy and freely available on the internet, they often contain clouds or are not all available for the same time period (34 Landsat images were needed to cover the whole study area). Moreover, sometimes data still needs to be geometric and radiometric corrected but this was not possible since no actual ground control points were available to perform the correction. It was therefore decided to assign predefined C values to an existing vegetation map instead. The vegetation map could be easy obtained from the CDRNB website and most C factor values were available from previous research in the Cochabamba department. However, for missing C factor values assumptions had to be made what might lead to misclassification of C factor values to some of the vegetation types, making results less reliable.

Comparing the results from this research with results from other researches showed that in only 0,9 per cent of the dryland areas of Bolivia, soil loss amounts fell outside the ranges of results from other researches, indicating that in 99,1 per cent of the study area, soil loss amounts were calculated correct. However, when looking at the RUSLE parameters separately, it was shown that some parameters had overestimated results while for other parameters results were underestimated. This might lead to a fake reality whereby the RUSLE 3D end result looks correct, while that is in fact only caused by compensation of an overestimated parameter with an underestimated parameter and vice versa and not because the calculation of all separate parameters gave correct results.

Based on the above, it can be concluded that although most data are easy to obtain and methods are easy to execute, their reliability is in general low and too many uncertainties exist for a reliable calculation of soil loss amounts in the dryland areas of Bolivia using the RUSLE 3D model. It is therefore better not to use this model in this research and other methods to obtain reliable information about soil erosion in the dryland areas of Bolivia need to be considered.

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Appendix A: Overview of C factor vegetation classes

Vegetation type (in Spanish) Vegetation class Acuatica arraigada en aguas cristalinas fluyentes natural vegetation Acuatil arraigada en aguas cristalinas no fluyentes natural vegetation Bosque denso siempre verde lluvioso no inundado closed evergreen forest Bosque denso semidecíduo estacional no inundado closed semi-deciduous forest Bosque denso semidecíduo estacional xeromorfico esclerófilo closed semi-deciduous forest Bosque denso semidecíduo lluvioso nublado closed semi-deciduous forest Bosque denso semidecíduo xeromórfico micrófilo closed semi-deciduous forest Bosque denso siempre verde lluvioso inundable closed evergreen forest Bosque denso siempre verde lluvioso nublado closed evergreen forest Bosque ralo decíduo espinoso microfilo open deciduous forest Bosque ralo deciduo xeromorfico espinoso open deciduous forest Bosque ralo semideciduo xeromorfico con suculentas open semi-deciduous forest Bosque ralo semidecíduo xeromórfico esclerófilo open semi-deciduous forest Bosque ralo siempre verde nublado esclerófilo open evergreen forest Campos de nieve permanente snow field Campos de nieve temporal snow field Ciudades principales urban area Cuerpos de agua lagos y lagunas waterbody Cultivos de plurianuales extensivos agriculture extensive Cultivos en rotacion y produccion extensiva agriculture extensive Cultivos en rotación y producción intensiva agriculture intensive Dispersa de arbustos en sustrato arenoso matas y cojines open shrubland Dispersa de arbustos en sustrato rocoso gramineas y forbias efímeras open steppe grassland Dispersa de arbustos en sustrato rocoso suculentas gramineas y forbias efímeras open steppe grassland Dispersa de arbustos en sustrato salino matas y cojines open shrubland Dispersa de herbaceas vivaces en sustrato salino matas y cojines natural vegetation Herbacea graminoide amacollada vivaz sinusia arbustiva micrófila closed steppe grassland Herbacea graminoide amacollada viváz con sinusia arbustiva esclerófila closed steppe grassland Herbacea graminoide amacollada vivaz sinusia arbórea esclerófila closed steppe grassland Herbacea graminoide amacollada vivaz sinusia arborea espinosa closed steppe grassland Herbacea graminoide amacollada vivaz sinusia arborea inundable closed steppe grassland Herbacea graminoide cesped vivaz turboso saturado plantas pulvinadas grass savannah Herbaceo graminoide amacollada con sinusia arbustiva xeromórfica closed steppe grassland Matorral decíduo xeromórfico espinoso open shrubland Matorral semidecíduo xeromórfico esclerófilo open shrubland Matorral siempre verde microfilo closed shrubland Matorral siempre verde y herbacea graminoide amacollada vivaz neblina y garua esclerófilo shrub savannah Nieve temporal snow field Plantaciones forestales extensivas agriculture extensive Superficie descubierta inestable evaporitas bare soil Superficie descubierta estable (Salares) salt lake Superficie descubierta inestable (Cieno) bare soil Superficie descubierta inestable cárcavas gully Superficie descubierta inestable-depósitos de arena desert Vegetacion acuatil arraigada en aguas cristalinas no fluyentes natural vegetation

Appendix B: Soil loss classes according to Morgan (2005)

soil loss rate class Indicators [ton ha-1] No evidence of compaction or crusting of the soil; no wash marks or scour very slight <2 features; no splash pedestals or exposure of tree roots; over 70 per cent plant cover (ground and canopy)

Some crusting of soil surface; localized wash but no or minor scouring; rills every 50-100 m; small splash pedestals, 1-5 mm depth, where stones of slight 2-5 exposed trees protect underlying soil, occupying not more than 10 per cent of the area; soil level slightly higher on upslope or windward sides of plants and boulders; 30-70 per cent plant cover

Wash marks; discontinuous rills spaced every 20-50 m; splash pedestals and exposed tree roots mark level of former surface, soil mounds protected by vegetation, all to depths of 5-10 mm and occupying not more than 10 moderate 5-10 per cent of the area; slight to moderate surface crusting; 30-70 per cent plant cover; slight risk of pollution problems downstream if slopes discharge straight into water courses

Connected and continuous network of rills every 5-10 m of gullies spaced every 50-100 m; tree root exposure, splash pedestals and soil mounds to high 10-50 depths of 10-50 mm occupying not more than 10 per cent of the area; crusting of the surface over large areas; less than 30 per cent plant cover; danger of pollution and sedimentation problems downstream

Continuous network of rills every 2-5 m or gullies every 20 m; tree root exposure, splash pedestals and soil mounds to depths of 50-100 mm severe 50-100 covering more than 10 per cent of the area; splays of coarse material; bare soil; siltation of water bodies; damage to roads by erosion and sedimentation

Continuous network of channels with gullies every 5-10 m; surrounding soil very severe 100-500 heavily crusted; severe siltation, pollution and eutrophication problems; bare soil Extensive network of rills and gullies; large gullies (>100 m2) every 20 m; catastrophic >500 most of original soil surface removed; severe damage from erosion and sedimentation on-site and downstream