MSc Dissertation by Jorge Rubiano

HYDROLOGICAL IMPACT OF LAND USE CHANGE IN TROPICAL HILLSIDES: THE IMPACT OF PATTERNS

JORGE ELIECER RUBIANO MEJIA

1998

THIS DISSERTATION IS SUBMITTED AS PART OF AN MSC DEGREE IN GEOGRAPHY AT KING'S COLLEGE LONDON

Resumen

El objetivo de esta investigación fue modelar escenarios de cambios en el uso de tierra utilizando un modelo empírico y evaluar las respuestas hidrológicas en una cuenca tropical utilizando un modelo distribuido de procesos físicos. Los escenarios fueron generados con un modelo celular autómata que usó las reglas básicas cambio a áreas deforestadas si hay cercanía a las carreteras y alrededor de áreas que fueron previamente deforestadas. La pendiente del terreno fue utilizada como limitante a los cambios. Estos escenarios fueron utilizados como parte de la información requerida por el modelo hidrológico con el fin de identificar el impacto potencial que los patrones de cambio de uso de tierra tienen en la escorrentía, infiltración y evaporación. Los resultados preliminares muestran las potencialidades del enfoque de modelos basados en celular autómata para generar patrones de uso/cobertura de la tierra, dependiendo de las restricciones físicas o socio-económicas. La aplicación de los diferentes escenarios de uso de tierra en un modelo hidrológico dieron una idea aproximada de los disturbios ocasionados sobre el balance hidrológico en las cuencas andinas.

Acknowledgements

I would like to thank CIAT for the time and support, as well with the Cabuyal database that they provided to me to carry out this research. I would especially like to thank Koulla Pallaris and Mauricio Rincon for their help and assistance in the field and for supplying the digital elevation model of Tambito. Thanks too to Mark Mulligan for his guidance in the use and development of the models. I would especially like to mention the support that Alvaro Jose Negret provided to the King's College team in the Tambito reserve and who left to future generations new paths to continue in the knowledge of our natural resources in Colombia.

Abstract

The objective of the current research was to model the potential impact of different scenarios of land use change generated by an empirically based model upon hydrological responses in a tropical watershed. The scenarios were generated with a cellular automata model that used basic rules concerning deforested areas along the roads and around previous deforested areas and constrained by terrain attributes. These scenarios were then used as input to a distributed hydrological model to identify the potential impacts of patterns of land use change upon hydrological responses as runoff, infiltration and evaporation.

Preliminary results show the

capabilities of the cellular automata model to generate patterns of land use/cover depending on physical or socio-economical constraints. Application of different scenarios of land use in an hydrological model gave an approximated idea of the potential impacts of disturbance in the hydrological balance of Andean watersheds.

LIST OF CONTENTS

LIST OF CONTENTS...................................................................................... 4 LIST OF FIGURES.......................................................................................... 5 LIST OF TABLES ........................................................................................... 6 1. INTRODUCTION......................................................................................... 7 2. OBJECTIVES.............................................................................................. 8 2.1 Main Objective.................................................................................................8 2.2 Secondary Objectives.....................................................................................8

3. LITERATURE REVIEW............................................................................... 9 3.1 Land use/cover change (LUCC) .....................................................................9 3.1.1 Land Use/Cover change Modelling ...................................................................10

3.2 Cellular Automata (CA) .................................................................................14 3.3 Modelling hydrological processes...............................................................15 3.3.1 Water Balance Modelling, A Review of Historical Approaches........................16 3.3.1.1 Empirical Models.............................................................................................17 3.3.1.2. Source Area concept .....................................................................................17 3.3.1.3. Distributed Models and LUCC .......................................................................18 3.3.2 Hydrological impacts of land use change ........................................................18

3.4 The study area ..............................................................................................20

4. METHODOLOGY...................................................................................... 23 4.1 Development of cellular automata (CA) rules - Magnitude and patterns of land use change..................................................................................................24 4.3 Hydrological model.......................................................................................26 4.3.1 Parameterisation................................................................................................27 4.3.2 Calibration and Validation .................................................................................29

5. RESULTS.................................................................................................. 30 5.1 Cellular Automata Model ..............................................................................30 5.2 Hydrological Simulation ...............................................................................35

6. CONCLUSIONS........................................................................................ 40 BIBLIOGRAPHY........................................................................................... 42 APPENDIX 1. CABUYAL WATERSHED MAPS .......................................... 48 APPENDIX 2. PATTERNS CHANGE ANALYSIS ........................................ 51 APPENDIX 3. CROSSTABULATION TABLES............................................ 60 APPENDIX 4. CELLULAR AUTOMATA RULES AND MODEL................... 69 APPENDIX 5. HYDROLOGICAL MODEL CODE......................................... 73 APPENDIX 6. SOIL DATA............................................................................ 77 APPENDIX 7. VEGETATION FIELD MEASUREMENTS ............................. 78 4

LIST OF FIGURES

Figure 3.1. Location of Cabuyal and Tambito catchment in Cauca - Colombia..............22 Figure 4.1 Methodological framework ..............................................................................23 Figure 5.1 Changes in LUC in four time steps generated by the CA model....................32 Figure 5.2 a and b (next page) LUC patterns generated in different time steps by the CA model. .........................................................................................................................33 Figure 5.4 Hourly infiltration in Tambito watershed for January 1998. ...........................37 Figure 5.5 Hourly bulk density at the water fron in the outlet of Tambito watershed (January 1998) ............................................................................................................38 Figure 5.6 Total fluxes of evaporation and infiltration in Tambito watershed (January 1998)............................................................................................................................39 Figure A.1 Land use series for 1946, 1970 and 1989 in the Cabuyal Watershed - Cauca - Colombia...................................................................................................................48 Figure A.2 Aspect, altitudinal ranges and slope in the Cabuyal Watershed - Cauca Colombia.....................................................................................................................49 Figure A.3 Proximity to roads and rivers in the Cabuyal Watershed - Cauca - Colombia .....................................................................................................................................50 Figure A2.1. Forest LUC conversion in the higher zone. Dashed ovals = new land uses and pointed oval = new forest....................................................................................52 Figure A2.2. Forest LUC conversion in the lower zone. Dashed ovals = new land uses and pointed ovals = new forest..................................................................................53 Figure A2.3. Forest LUC conversion in the middle zone. Dashed ovals = new land uses and pointed ovals = new forest..................................................................................54 Figure A2.4. Forest LUC conversion in the higher zone between 1970 - 1989. Dashed ovals = new land uses and pointed ovals = new forest ............................................55 Figure A2.5. Forest LUC conversion in the lower zone between 1970 - 1989. Dashed ovals = new land uses and pointed ovals = new forest ............................................56 Figure A2.6. Scrub LUC conversion in the higher zone between 1946 - 1970. Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover .................................................................................................................57 Figure A2.7. Scrub LUC conversion in the lower zone between 1946 - 1970. Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover .58 Figure A2.8. Scrub LUC conversion in the middle zone between 1946 - 1970. Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover .................................................................................................................59 Figure A7.1. Primary forest leaves scanned from pictures taken in Tambito, Cauca Colombia.....................................................................................................................81 Figure A7.2. Secondary forest leaves scanned from pictures taken in Tambito, Cauca Colombia.....................................................................................................................82 Figure A7.3. Pasture leaves scanned from pictures taken in Tambito, Cauca - Colombia .....................................................................................................................................82 Figure A7.4. Canopy Forest cover scanned from pictures taken in Tambito, Cauca Colombia.....................................................................................................................83

LIST OF TABLES D.

Table 5.1 Conversion from land cover in 1946 towards different land covers in 1970 considering the frequency of the distance to roads (n=40727 pixels). ...................... 31 Table 5.2 Conversion from land cover in 1946 towards different land covers in 1970 considering the frequency of the distance to rivers (n=48573 pixels). ...................... 31 Table A3.1. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 0 to 45 degrees..................................................................... 60 Table A3.2. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 45 to 90 degrees................................................................... 61 Table A3.3. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 90 to 135 degrees................................................................. 62 Table A3.4. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 135 to 180 degrees............................................................... 63 Table A3.5. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 180 to 215 degrees............................................................... 64 Table A3.6. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 215 to 270 degrees............................................................... 65 Table A3.7. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 270 to 315 degrees............................................................... 66 Table A3.8. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 315 to 359 degrees............................................................... 67 Table A3.9 Conversion from Forest in 1946 towards different land covers in 1970 considering the frequency of the neighbours of the same class (n=8814 pixels). .. 68 Table A5.10 Conversion from Pasture in 1946 towards different land covers in 1970 considering the frequency of the neighbours of the same class (n=23116 pixels). 68 Table A3.11 Conversion from Scrub in 1946 towards different land covers in 1970 considering the frequency of the neighbours of the same class (n=21047 pixels). 68 Table A.1 Soil properties corresponding with the 25 classes of the sampling scheme. 77 Table A7.1 Leave measurements in Primary Forest Plot. Tambito, Cauca - Colombia .. 78 Table A7.2. Leave measurements in scanned images from Primary Forest in Tambito, Cauca - Colombia................................................................................................................. 79 Table A7.3. Leave measurements in Secondary Forest Plot. Tambito, Cauca - Colombia ................................................................................................................................................. 79 Table A7.4. Leave measurements in scanned images from Secondary Forest in Tambito, Cauca - Colombia................................................................................................ 79 Table A7.5. Pasture leave measurements in Tambito, Cauca - Colombia........................ 80 Table A7.7 Vegetation Parameters for three different Land Use in Cauca - Colombia ... 80

1. INTRODUCTION

There is no doubt that the change produced by human action on the rural landscape can have a strong impact upon water resources both in terms of their quantity and their quality. These hydrological changes may influence overland flow, soil erosion, streamflow and sediment transport. A lot of recent research in these hydrological processes has shown that it is now possible to model the process change resulting from this land uses impacts. The results of these models indicate that some parts of the watershed are more sensitive to a particular type of land use change than others. In particular it is thought that the 'contributing' areas closest to fluvial zones are extremely sensitive and that, if left undisturbed, these areas can act as a barrier to hydrological impact. These buffer zones can be important, but not in all cases. The size of buffer zone required for protection of hydrological resources against land use change impacts will vary across the units of sensitivity in the catchment. Indeed, a buffer zone may not always be necessary. Different patterns of land use change may lead to different requirements for buffer zones.

The spatial configuration of change is also important because flow paths link landscape units. Net runoff or erosion is the sum of water and sediment from sinks as well as sources, and the spatial configuration of land use determines the location of these sinks relative to the sources. When sources and sinks occupy different flow paths, net flows of water and sediments may be high, where they occupy the same flow path re-infiltration and re-sedimentation reduce net losses at the catchment scale.

2. OBJECTIVES

I. II.

2.1 Main Objective

- To model the potential hydrological impact of scenarios for land use change generated by empirically based cellular modelling.

III.

2.2 Secondary Objectives

- To analyse patterns of past land use change and their relationship with environmental parameters. - To attempt to develop a set of cellular automata rules for land use changes (LUC) in the hillsides of Colombia to be applied under different environmental and infrastructure constraints. - To parameterise an existing PC-raster hydrological model with field data collected from a tropical catchment in Cauca - Colombia. - To use the model to identify the potential impact of the different scenarios of LUC generated with the cellular automata rules over the parameterised tropical catchment in Cauca - Colombia.

3. LITERATURE REVIEW

IV.

3.1 Land use/cover change (LUCC)

"Land use/cover has been recognised by a variety of National and international bodies as a critical factor mediating between socio-economic, political, and cultural behaviour and global environmental changes, especially changes in atmospheric chemistry and potential climatic change" (IGBP, 1988; NRC, 1990; ISSC, 1990, cited by Rainier et al, 1994 in Turner 1994).

In the past few decades, we have seen rapid land use/cover changes in the form of afforestation, cropland abandonment and clearance for agriculture in many parts of the developing world. A global view of this process is not enough to understand its effect occurring locally. The diversity of socio-economic and biophysical conditions makes it difficult to find similar changes between one region and another.

The

human driving forces involved in the change are clustered in a complex way and their operation is strongly influenced by the environmental context. The fragility or robustness of the physical environment mediates the impact of human activities upon it; similar levels of human pressure may affect different environments to different degrees. The ways in which social factors define the selection of land use are evident but are little understood. Changes in land use/cover occurring in tropical hillsides are associated with many factors and processes ranging from the socio-economical to the physical characteristics of the landscape. Although the driving forces are more related with political and socio economical aspects, an understanding of the physical factors is fundamental to the development of potential scenarios and should therefore be the key component in future socio-economic models.

The effort of many scientists in identifying these key physically based or social factors has encouraged the development of different approaches to the problem. It has been necessary to define the terminology used in these studies, to enable comparisons between studies of different locations and to avoid misinterpretations. Land use denotes the human employment of the land. Some uses include settlement, cultivation, pasture, rangeland, and recreation, amongst others. Land use change at any location may involve either a shift to a different use or an intensification of the existing one.

Land cover denotes the physical state of the land. It embraces, for example, the quantity and type of surface vegetation, water, and earth materials. Changes in land cover driven by land use can occur in two ways: 1) When the land cover changes completely, e.g. from pasture to crops, a process referred to as conversion; 2) When the change involves changing the conditions in land use without changing the cover class; a process referred to as modification. The majority of studies in this field have been related with the process of conversion, because it is more evident and easy to identify and does not require the collection of data on land use practices and economic characteristics of each class. The current study is specifically related with land cover changes but does not include any socio-economical factors.

IV.1.

3.1.1 Land Use/Cover change Modelling

The approach to study and modelling LUCC, has followed the development of socioeconomical conceptual models operationalised into computer simulations and in some cases, with the incorporation of biophysical models. The objective in some cases has been to build descriptive classifications. Others have concentrated on

prescription (recommendations) or restoration within planning projects and others still on projection and/or prediction of future changes given different scenarios. Conceptual models Conceptual models have offered a starting point to addressing the problems. Biophysical and socio-economical factors are included in these models but their full operation has not been carried out. The reason for this is based specifically in the complexity of the problem. The Global Change Institute (1991) statement emphasises the importance of human induced changes: "Human actions rather than natural forces are the source of most contemporary changes in the states and flows of the biosphere. Understanding these actions and the social forces that drive them is thus of crucial importance for understanding, modelling, and predicting global environmental change and for managing and responding to such change"(p. 24).

When human forces are included in a model, the uncertainty is increased to the point at which it becomes impossible to work with. It is generally accepted that socioeconomic factors have more influence in the land use/cover configuration than the biophysical factors. Unfortunately, as Riebsame et al (1994) state: "A recurrent problem with land use/cover modelling (and modelling in general) is that the assumptions and goals of a given study are often neglected in interpreting the results".

Socio-economical modelling at the macro scale has been dominated by the identification of "social driven forces" such as population and consumption pressures. Other important factors that were considered in this study were technological change, affluence/poverty, political and economical structure, beliefs and attitudes. The

general consensus is that the environmental impact of LUCC is directly related to human numbers and wealth and is either amplified or diminished by technology.

At a meso -scale level the prevailing paradigm is that of optimum economic utility. In theory, any piece of land, given its physical attributes and spatial location, will be used in the way that earns the highest rent. The driving forces of change operate through their integrated effect on the net return of alternative uses of land. Ravnborg (1998) emphasises that a farmer tends to concern himself, not with the suitability of soil conditions with respect to the planting of a particular crop but rather with its marketability and input demands (such as labour, fertilisers, etc).

Land use/cover modelling is one of the most challenging tasks in hillside environments.

The complex pattern of land uses, forming a heterogeneous

landscape, makes the process difficult even for basic descriptive studies, (Langford, 1997). The task is further complicated by the lack of accurate census and historical information.

Biophysical models The biophysical approach emerges from general classifications of the vegetation distribution around the world. Descriptive studies were incorporating normative rules about the relationship between microclimate, soils, topography and vegetation. The Holdridge classification is one example of that (Holdridge, 19xx). Biophysical characteristics, such as the temperature, elevation and latitudinal location, are used to define an expected vegetation cover. A new procedure incorporates the theory of probability of transitions. It appears to be a useful way to deal with uncertainty and complexity in landscape change. The technique is used to simulate future land use structures considering previous conditions of biophysical and socio-economic characteristics. This, in turn, is 12

matched to ecological knowledge to simulate the effects of the new use pattern. The main application has been in the assessment of biodiversity impacts of LUCC.

Although the weight of agent/decision making in comparison with biophysical factors is greater, there is no doubt that some land use/cover types are restricted by the landscape. However, biophysical constraints can, to some extent, be overcome with technological advance and in such circumstances, socio-economic factors became more significant. A firm understanding of natural resource availability of an area must be the first step in any framework conducted to model processes like LUCC.

The relations between land use and its driving factors is also dependent on the scale of observation, (Veldkamp, 1996). Hall et al (1995 in Verburg et al 1997) found that, at detailed scales, land use in tropical rainforest areas is strongly correlated with topography. At a coarse scale other factors emerge.

In the application of the CLUE model (Conversion of Land Use and its Effects, Verburg, 1997) to China and Costa Rica, it was found that, both biophysical and socio-economical factors are needed to explain the land use structure. Population density and agricultural labour force were the most important factors, explaining the land distribution in those areas. However, biophysical conditions, especially soils and topography, also had an important influence on the distribution of land use. Cellular automata (CA) modelling approach has been suggested as a new method for dealing with the complexity of interacting terrestrial and social systems such as land use/cover change (Waldrup, 1992 in Riebsame, 1994).

3.2 Cellular Automata (CA) 13

Cellular automata are mathematical models applied to a finite set of elements in a discontinuous space. When these models are applied to a landscape, they consist of fixed arrays in which each cell represents an area of the land surface. The scale is defined by the cell size and the time step is set up depending on the process being simulated. Each cell can be in one of n different states at a given time step. At the next time step, each cell may change its state, in a way determined by the set of predefined rules. These rules describe precisely how a given cell should change states, depending on its current state and the states of its neighbours. Which cells are in the neighbourhood of a given cell must be specified explicitly (Espericueta, 1997). The rules are generally simple and can be expressed as algebraic statements, which minimise the need for more complex mathematical operations that are associated with other modelling approaches involving differential equations. These algebraic statements are easily translated into command syntax of many raster GIS packages.

In summary, CA models consist of an array of cells (one or two-dimensional), a neighbourhood defined for each cell and a set of rules, which specify how the dynamics of the CA operates both in space and time.

The theory about CA was first introduced in the 1940's by the Hungarian-American mathematician John von Neumann (1948) whose work in 'self-reproduction' and Ulam's work on 'cellular auxology', were the first steps in computer development (Hogeweg, 1988). They were looking for simple mathematical models of biological systems. The concept was popularised three decades later through John Conway's work in the Game of Life, which is an infinite class of mathematical systems. CA has been used to model phenomena from diverse disciplines. Any system can be analysed from the point of view of large numbers of discrete elements with local interactions, is posible to being modelled as a CA. Examples of its use include the 14

study of fluid dynamics, plasma physics, chemical systems, growth of dendritic crystals, economics, two directional traffic flow, image processing and pattern recognition and geomorphological and ecological modelling (Espericueta 1997 and White 1993). In spatial and environmental research CA has been used to study the connectivity and complexity of ecosystems (Green, 1994), spatial urban development (Camara, 1996), vegetation succession (Hogeweg, 1988), forest fire simulations (Goncalves, 1994) and rainforest dynamic (Solé, 1995).

VI.

3.3 Modelling hydrological processes

“The study of the water balance is the application in hydrology of the principle of conservation of mass, often referred to as the continuity equation. This states that, for any arbitrary volume and during any period of time, the difference between total input and output will be balanced by the change of water storage within the volume.” (UNESCO, 1971 in Sokolov et al, 1974). Water is continuously flowing and distributed in the hydrological cycle. It takes water from the ocean or land surfaces by evaporation and is transported by winds across the earth during which condensation occurs, and deposits the water on the Earth's surfaces in the form of precipitation. Once here, the water runs by gravitational forces towards the oceans or it is returned to the atmosphere by evaporation and transpiration (Oki, 1995). “The Watershed is a natural unit of land which collects precipitation and delivers runoff to a common outlet” (Black, 1970 in Newson, 1992). First reports considering the watershed as a unit comes since the 1700's. Philippe Buache (1752) presented a memoir to the French Academy of Sciences in which he outlined the concept of the general topographical unity of the drainage basin. In his study of the human geography of France, Jean Brunhes based his major divisions of the country on the drainage basins of Geronne, Loire, Seine, and Rhône-Saône and their major towns. 15

His argument for using this method is based partly on convenience and partly on recognition of water as a link between the earth and man’s activities. ‘Water is the sovereign wealth of a state and its people. It is nourishment; it is fertiliser; it is power; it is transport" (Brunhes, 1920 cited in Smith, 1969).

In a variety of ways the drainage basin has formed a framework for human activity: in guiding the direction of primary settlement, in river navigation and the growth of trade and towns, in the provision of water-power for industrial concentrations, and in providing a logical context for irrigation works. (Smith, 1969). "On the basis of the water balance approach it is possible to make a quantitative evaluation of water resources and their change under the influence of man’s activities" (Sokolov et al, 1974). Water balance studies are the first step in the design of projects for the rational use, control and redistribution of water resources in time and space. To improve the knowledge of the water balance is usefull to assits the prediction of the consequences of artificial changes in the regime of streams, lakes, and ground-water basins.

VI.1.

3.3.1 Water Balance Modelling, A Review of Historical Approaches

"Models - either symbolic (mathematical) or material - are essential to understand and predict environmental phenomena on agricultural watersheds. The watershed is an appropriate area element to consider for hydrological models because all uncontrolled surface water flux out of the system is zero except at the stream draining it" (Woolhiser, 1975). Summaries of models used in the study of rainfall – runoff process has been reported in different sources (Woolhiser, 1973; Renard, 1982; Linsley, 1982; Todini, 1982; Boughton, 1988 and Wheater, 1993). There is an extensive literature of models that are currently used in hydrology for many different

purposes. Singh (1995) presents in detail the currently most used computer models in watershed hydrology.

VI.2.

3.3.1.1 Empirical Models

Hydrologic modelling originated in the latter part of the 19th century as a way to address design issues for urban sewers, land reclamation drainage systems, and reservoir spillways (Todini, 1988). Into the early 20th century, empirical formulas were the primary tool used to estimate runoff. Also during this time, the rational method, which is based on the concept of concentration time, was developed. The rational method is probably one of the oldest models used in the rainfall - runoff relation. Its origins are dated between 1851 to 1889 according to different authors. (Chow, 1964). VI.3. VI.4.

3.3.1.2. Source Area concept

Models of overland flow took a new direction in the early 1970â&#x20AC;&#x2122;s by the inclusion of the source area concept (for instance, Freeze 1971 cited by Engman and Rogowski (1974). "In order to accommodate the source area concept, Ishaq and Huff (1979) revised the continuity equation of overland flow and constructed a model, the result of which are promising and suggest that major portions of runoff are indeed generated by overland flow originating from small parts of a watershed." (Beven et al 1979 cited by Hugget, 1985). Troendle presents a detailed review of the variable source area concept in Anderson (1985). VI.5.

3.3.1.3. Distributed Models and LUCC

The use of computers in LUCC modelling has a short history. The division of the space covered by a catchment in discrete cells or polygons was possible only when 17

the hydrological distributed models appeared. This made it possible to assign the correspondent land qualities of each land use/cover class and to compute the physical relationships between the set of polygons. Hjelmfelt and Amerman (1980), cited by Woolhiser, (1996) reported a paper written by Merril Bernard and presented in 1937, in which he used a rectangular grid to represent the topography of a small watershed and used a routing scheme to represent overland flow. All the computations had to be done by hand, so his work had little impact and was forgotten for more than 40 years.

The search for a physically based distributed models was encouraged with the development of geographical information systems (GIS). Automated procedures are commonly used to delineate basin geometry and to derive flow pathways from digital maps of topography. Of more than 100 models reported in a study by the American Society of Civil Engineering (1985), 28 models quantified major land-use change effects in the absence of site calibration data. There were eleven models based on continuous process, eleven based on the soil conservation curve number (SCS) and six based on statistical regression equations.

VI.6.

3.3.2 Hydrological impacts of land use change

OIES Global Change Institute (1991) presents an overview of the land-cover landuse change in the environment. On water resources, the report mentions their impact on water quality and quantity produced by changes in river and groundwater regimes. The flooding is increased by destruction of vegetation because it promotes compaction and reduces the soil infiltration capacity. Overgrazing, burning, deforestation, some agricultural practices and urbanisation can destroy the vegetation and make less water available for groundwater recharge. â&#x20AC;&#x153;The base flow

of perennial rivers in turn could be seriously affected by such a reduction of groundwater returns to the river, with more of the flow being concentrated in the flood or peak periods and less during the dry periods. A further consequence of these impacts on water quantity is the addition or removal of material from the rivers, water bodies, and groundwater. For example, clearcutting of trees can lead to large increments of sediment reaching a nearby stream.â&#x20AC;? (OIES Global Change Institute, 1991).

Effects of Forest Change The energy balance is affected after clearcutting or afforestation. Processes and components of the surface system are changed dramatically: the albedo, canopy interception, the aerodynamic properties of the surface (roughness) and the radiation available at the ground level, all of these have major impacts upon the energy and water balances.

The use of water is reduced when forest is changed towards seasonal crops or pastures and the yield of water is increased and when forest is replaced thus changing the highest and lowest flow peaks to more extreme levels. Watershed research studies have empirically confirmed the property of forests to absorb heavy storms and transmit water to the soil by infiltration through forest litter (Pereira, 1973 in OIES Global Change Institute, 1991). A large number of researchers have concentrated their efforts on evaluating the impact of land use change on water resources, using the forest as their main focus of interest. In the Proceedings of an International symposium of the International Union of Geodesy and Geophysics held in Vancouver, British Columbia, a review is made of different studies in this topic (Swanson, 1987). Field research on clearcutting or where soil processes were measured (erosion, sediment, and nutrient fluxes) are summarised in Okunishi et al (1987), Williams et al (1987), Pearce et al (1987), and 19

Troendle (1987). Watershed simulations on clearcutting, reforestation, soil erosion between other aspects are presented in Hornbeck (1987), Schulze and George (1987), and Storm et al (1987).

Effects of Grassland Change â&#x20AC;&#x153;The hydrological effects of grasslands depend entirely upon their management. Improperly managed grazing and burning can lead to removal of vegetation cover and the trampling of soils. In many areas where uncontrolled burning and grazing have been practised, grazing management has a greater impact upon the hydrology of a watershed than does forest management.

Two opposing hydrological facts are at work with managed grasslands. In order to control flood flow and soil erosion, control of grazing is essential to preserve the grass cover, to prevent soil exposure, and to prevent excessive trampling. On the other hand, increasing crop density and productivity of grasslands, the total water yield decreases; the vegetation needs the water for evapotranspiration. This leads to the conclusion that in many cases, rather than looking to afforestation to reduce flood and erosion damage, maintaining grass cover may be more effective without reducing the water yield of the watershed as much as forestâ&#x20AC;? (Ives and Messerli, 1989 in OIES Global Change Institute, 1991).

VII.

3.4 The study area

Two similar areas were selected for the current study. Cabuyal watershed in the municipality of Caldono in Cauca - Colombia and Tambito catchment in the municipality of Tambo in the same department. Figure 1 shows the location of both watersheds in Cauca, Colombia. The first one was used to identify land use/cover change rules. Land use/cover changes studies are very scarce with regards to Tambito. Its selection was based on the assumption of being affected by similar 20

socio-economical processes as Cabuyal. Cabuyal is the pilot area of the Hillsides Project at the International Center for Tropical Agriculture - CIAT and has been the subject of several agricultural and natural resource researches (CIAT, 1997). Tambito was selected to take advantage of instrumentation facilities and because it is preceded for a long fallow period of almost 35 years. Details of the characteristics of the catchment can be found in Museo de Historia Natural (1996). Tambito catchment comprises an area of 3000 ha located in altitudes between 1500 and 2900 MASL irrigated by two rivers: Palo Verde and Tambito. The land cover is represented by CABUYAL

Primary forest (62%), Secondary forest (36%) and Pasture (2%). COLOMBIA

CALDONO TAMBO

CAUCA DEPARTMENT

Figure 3.1. Location of Cabuyal and Tambito catchment in Cauca - Colombia.

4. METHODOLOGY

Figure 4.1 shows the sequence of steps followed to identify the effects of LUC

LUC 1946

LUC 1970

DIGITAL MAPS

GIS SURFACE ANALYSIS

CELLULAR AUTOMATA RULES

DIGITAL MAPS

GIS SURFACE ANALYSIS

'TAMBITO' CATCHMENT

SCENA RIO 1

SCENA RIO 2

LUC 1989

SCENA RIO 3

CA NOPY INTERCEPTION RIV ER DISCHA RGE (SEDIMENTS)

PC-RASTER HY DROLOGICAL MODEL

SOIL PROPERTIES

SIMULATION AND ANALYSIS OF RESULTS

HYDROLOGICAL MODEL PARAMETERISATION (FIELD WORK)

MODELLING LAND USE/COVER SCENARIOS

DEVELOPMENT OF CELLULAR AUTOMATA RULES

patterns upon the water resources. Each step is explained with detail in this chapter.

RIVER DISCHARGE ANALYSIS

EROSION RUNOFF

Figure 4.1 Methodological framework

VIII. 4.1 Development of cellular automata (CA) rules Magnitude and patterns of land use change Three LUC time series maps (1946, 1970 and 1989) from the Cabuyal watershed in Cauca, Colombia were analysed looking for the kind of LUCC patterns present in the landscape. Appendix 1 contents the maps of this area. Although there are basic differences between the environment of Cabuyal watershed and the Tambito catchment, which is the area where the model was applied, this is the closest area with land use history data available. The analysis consisted of: 1. Identification of shape patterns of land use change, such as linearity or clustering, between land uses of different series: The coverage of LUC of 1946 was overlaid with the LUC of 1970 and LUC of 1970 with the LUC of 1989 using the crosstabulation command in IDRISI. The area was then divided in three altitudinal zones: 1200 - 1500, 1500 - 1800 and 1800 - 2200 MASL. In each altitudinal zone the shape and patterns of the main changes were defined. Appendix 1 contains the basic maps and Appendix 2 the description of crosstabulation map of each zone for the 1946 1970 series. Main conclusions derived from this analyses are included in the results chapter. 2. Identification of neighbourhood relations: To understand whether certain types of LUC depend on frequency distribution of precedent neighbourhood land uses, or proximity to rivers and roads, the procedure to obtain this information was as follows: 1. Reclassification of each of the maps from 1946, 1970 and 1989: Preliminary analyses were carried out with all the land use/cover classes originally available in the maps but with the purpose of simplifying the data management, similar land uses were joined. Pine was merged with Forest, Bare Soil with Scrubs and Crops with Pasture to define three classes of cover: Forest, Pasture and Scrubs.

2. Slope was grouped in six classes: 0 - 3 %, 3 - 12 %, 12 - 30 %, 30 - 50 %, 50 - 75 % and > 75 %. 3. Aspect was organised into eight classes of 45 degrees each, the first class being between 0 and 45ยบ. 4. Altitude was divided in two ranges: lower and higher than 1650 MASL. This value was selected considering previous field observations made about the altitudinal level of LUC differentiation. 5. A 3 by 3 neighbourhood analysis was carried out for each of the LUC classes in the 1946 series to obtain the frequency of pixels of the same class. The produced image was then used to make crosstabulation tables with the 1970 series of LUC. This enabled identification of the new land use/cover depending on the frequency distribution of neighbours of each LUC in the preceding time step (in this case 1946 series). 6. Preparation of rivers and roads: Roads and rivers vector files were rasterised and the Euclidean distance to pixels signalling the road was calculated using the DISTANCE command in IDRISI. The produced images were reclassified in four evenly distributed classes, with each road class covering 200 metres and each river class 100 metres. Cross-tabulation frequency tables were produced between the variables of LUC, slope, altitude and aspect. The same was done for the neighbourhood images and between LUC and distance to rivers and roads. 7. Logical rules were written following the frequency distribution of the three different kinds of tables. Land use changes between series according to landscapes attributes, neighbourhood relations in 3 by 3 pixels, and proximity to rivers and roads.

In defining the rules, values in tables describing a

change occurring in more than 50 % of the cases were considered with a weight of 100 %. Those cases where all the options had values below 50 %

were solved analysing the 1979/1989 series. Maps are displayed in Appendix 2; with tables in Appendix 3 and the set of rules in Appendix 4.

4.2 CA-model application - Modelling LUC scenarios. The CA model was then applied to the Tambito catchment under the general statements representing the physical constraints or river location and infrastructure investments (location of roads). As a result, LUC scenarios maps were generated and used as input for the hydrological model.

IX.

4.3 Hydrological model

A PC-Raster model was used (Mulligan, 1998), which three different modules: atmospheric, vegetation and soil module. The first calculates evapotranspiration based on solar net radiation, leaf area index (LAI) and terrain aspect. The vegetation module calculates the interception rate of vegetation based on the vegetation cover and leaf area index. The soil module calculates saturated hydraulic conductivity (Ksat), recharge, bulk density at the water front (BdatWF), infiltration, runoff and erosion, based on rainfall, soil density, soil texture, depth, stone density and terrain slope. The three scenarios generated with the CA-model were used as a land use cover input. The hydrological model was run for every scenario for January 1998 in 300 time steps (1 step = 1 hour). A series of maps were produced and displayed as a movie to follow the changes occurring in each variable. The main focus of attention was on recharge, erosion and runoff. Time series for recharge and evapotranspiration were plotted to identify differences in the hydrological response. contents The code is presented in Appendix 5.

IX.1.

4.3.1 Parameterisation

The model required the following list of parameters: Climatic parameters: Rainfall: Hourly precipitation was downloaded from the data-loggers currently installed in the catchment. Net radiation: The net radiation was computed as the difference between the measured incoming solar radiation and reflected energy by the surface (Jetten, 1994; Mulligan, 1996 cited by Rincon, 1998). The equation used to calculate the net radiation (Rn) in the model was: Rn = 0.8683 Rt - 8.5931 (MJ) Where Rt is the terrestrial solar radiation in Meg Jules per day.

Soil parameters: Soil samples were collected from 16 different points within the catchment at depths of 10 cms. until the rock bed was reached. Tambito catchment was classified according to slope, aspect and vegetation cover to produce 25 classes. During the field work the criteria was to collect the maximum samples number in the most representative classes. Appendix 6 contains the soil data. - Soil texture: Was calculated by averaging the values obtained in the first three soil layers (30 cms). Texture was calculated using the Bouyoucous Standard method in the Soil Laboratory of the International Center for Tropical Agriculture CIAT. - Bulk density: Undisturbed Auger samples of 5.1 CMS were taken at depths of 10 cms. Wet weight was taken no more than four hours later and dry weight after drying the samples at 105 C for 48 hours. These were used to calculate bulk density.

- Hydraulic conductivity: This parameter was calculated using a Disk Infiltrometer (Decagon Devices, Inc.). TheTheory supporting its functioning is found in Zhang (1997). - Stone density: 10 random samples were taken from different points within the catchment and values were calculated measuring its weight and the volume of water displaced by them. - Soil erodability (K): The relation between texture and organic matter was used to identify the K value from tables reported by Kirkby and Morgan (1984).

Vegetation parameters: Leaves from every land use in the catchment were collected. Fresh (dry) and wet weight was measured in the field. To calculate the area, the samples were photographed in a flat sheet of paper with known area. Pictures were then scanned and processed to correct visual distortions in commercial graphic software. Field measurements tables and examples of scanned images are presented in Appendix 7. Incoming photo-synthetically active radiation (PAR) was measured with sensors held upright at 1, 3 and 6 meters above the ground. An exponential relation was used to calculate the radiation at the top of the canopy and Beerâ&#x20AC;&#x2122;s Law was used to calculate the Leaf Area Index (LAI). With this set of data the following parameters were calculated: - Leaf density (LD): weight of leaves per unit area (g/m2). - Specific leaf area: area per weight of leaves (m2/g). - LAI: area of leaves per unit area of ground. - Specific Water retention (SWR): weight of water per area of leaves (g/m2) - Cover: Relation of gaps with canopy cover (fraction 0 - 1) - Canopy Storage capacity: SWR * Cover * LAI (mm) - Initial Biomass: LAI * LD * 0.5. 27

Data was collected directly from the field between the 1 July and 1 August 1998.

IX.2.

4.3.2 Calibration and Validation

Originally, river discharge was considered as the key variable for calibration and validation. During the field work pressure sensors were installed in Palo Verde and Tambito catchments in two homogeneous sections built over 10 meters along the river. Unfortunately the sensors were not sensitive enough to changes in the amount of water in the river and no data was recorded. For this reason calibration and validation are not included in this document. For the purpose of this research, it was enough to obtain the model response to different land cover scenarios.

5. RESULTS

5.1 Cellular Automata Model

From the first set of figures and tables presented in Appendix 2 and 3 it is possible to conclude that land use change is associated with presence of roads and rivers. The sequence in almost all images is: Forest to Scrub and Scrub to Pasture. Intact forest is mainly located at great distance from rivers and roads. This processes were more common in middle altitudes possibly due to the location of the main road (Panamerican Highway) in this area. In the high altitudes the process is similar but in a more fragmented way due to limited access relative to the lower altitudes. New areas of Forest were founded in 1970 where Pasture occurred during 1946. Scrub is maybe the most consistent land cover over the time series considered in this study and in some cases is converted into bare soil. In the neighbourhood analysis presented in Appendix 3 (Tables A3.9 to A3.11) there are no clear patterns of change that depend on the extent of a specific land use around each category. These results can be associated with image pixel resolution (25 meters), which is a small size necessary to outline the neighbourhood relations. This data clearly shows the tendency of the land to be converted completely into Pasture. Some areas with Scrub and Pasture turned to Forest again but the overall proportion that exhibited this is very small. While 80 % of the Forest changed to Pasture and Scrub, only 11 % of the Scrub and Pasture returned to Forest. Tables 5.1 and 5.2 show the changes in land use with respect to the distance of roads and rivers. If the Pasture is far from roads, it is more likely to return to Forest and the most common land cover change is towards Scrub and Pasture again.

Table 5.1 Conversion from land cover in 1946 towards different land covers in 1970 considering the frequency of the distance to roads (n=40727 pixels).

Distance to roads 1 - 200

% LU in 1970 LU in 1946 Forest (1) Pasture (2) 1 3 9 2 2 35 3 3 18

Scrub (3) 4 8 18

200 - 400

1 2 3

4 4 4

6 28 18

4 11 21

400 - 600

1 2 3

1 4 3

6 41 25

1 8 11

> 600

1 2 3

0 5 2

6 69 18

0 0 0

Table 5.2 Conversion from land cover in 1946 towards different land covers in 1970 considering the frequency of the distance to rivers (n=48573 pixels).

Distance to rivers 1 - 100

% LU in 1970 LU in 1946 Forest (1) Pasture (2)

Scrub (3)

1 2 3

3 3 4

8 36 17

3 10 17

100 - 200

1 2 3

4 2 2

7 30 23

4 7 22

200 - 300

1 2 3

3 1 0

12 23 22

8 8 22

> 300

1 2 3

1 0 0

38 11 36

2 8 3

Although the numerical results obtained from this set of tables are still being processed, two basic rules were qualitatively considered limited by terrain slope: 1. deforestation around the roads and, 30

2. deforestation in areas adjacent of previous deforested land. The preliminary cellular automata model was used over the Tambito catchment and run in fifty time steps. Figure 5.1 shows the change in LUC over time, as generated by the CA model. Both primary and secondary forest decrease significantly over time and are replaced by pasture, which increases dramatically from 36 Ha to 1322 Ha over 1424 Ha in 30 time steps.

In 30 time steps the primary forest is almost

completely removed, with very low levels of secondary forest remaining. With time this is also wholly removed with complete conversion to pasture over the whole catchment. This can be seen in diagrams of the catchment over time in Figure 5.2 Total deforestation was attained in the time step No. 42. Scenarios for 0, 5, 20 and 30 time step's were used as inputs in the hydrological model.

1500 1250 1000

Primary Forest 750

Secondary Forest Pasture

500 250 0 VEG0

VEG5

VEG20

VEG30

Land Use/Cover time step

Figure 5.1 Changes in LUC in four time steps generated by the CA model.

Current road

Step 0

Pasture Secondary Forest Primary Forest

Step 5

Figure 5.2 a and b (next page) LUC patterns generated in different time steps by the CA model.

Step 10

Step 15

Pasture Secondary Forest

Step 20

Primary Forest

Step 25

Step 30

Figure 5.2 b.

XI.

5.2 Hydrological Simulation

In Figure 5.3 the daily pattern of evaporation (EVP) clearly shows a dependence upon the net radiation. When rain occurs, EVP is depleted temporarily and at the end of the month the EVP raises almost four fold, relative to the initial condition. EVP is greater in the presence of more vegetation cover (VEG0 vs. VEG30), according to reality if we consider the role of vegetation in the interception process. Looking at the EVP in the motion maps, is possible to appreciate local patterns that are dependent upon slope, aspect and vegetation cover. Those slopes facing east show higher values of EVP in comparison with west facing slopes. Cloudiness was observed in the field to occur mainly during the afternoon, which confirms the result of lower EVP on west facing slopes. Figure 5.4 shows the infiltration fluxes in the catchment outlet. Figure 5.5 shows the patterns of bulk density at the water front measured in the outlet of the catchment. Note the difference in the scale between graphs a and b which are intended to show the difference at the end of the two periods. Due to changes in land use cover shown in Figures 5.1 and 5.2, there are responses in evaporation rates and level of infiltration. Evaporation decreases from 0.67 m/m/day in the original catchment to 0.45 mm/m/day as deforestation occurs. Infiltration rates also respond to vegetation loss with an increase from 33.6 to 35.5 mm/m/day. This is as expected as loss of vegetation leads to a reduction in intercepted evapotranspiration. Similarly reduced interception makes more water available at ground level as thus increases infiltration levels.

800 700

EVP (mm)

veg0

500

veg5

veg20 400

veg30

300

Rainfall (mm)

(mm/hr) 600

200 25

100

30 145

139

133

127

121

115

109

103

0 hour

800

700

5 10

500

400 300

200 25

100

30 151

145

139

133

127

121

115

109

103

0 hour

Figure 5.3 Hourly evaporation in Tambito watershed upon 4 scenarios of LUC for January 1998.

Rainfall (mm)

EVP (mm)

600

Infiltration 50000

45000 2

35000 mm

30000

25000 20000

15000 10000

Rainfall (mm)

40000

5000 148

141

134

127

120

113

106

10 1

0 hour

Infiltration 350000

0 2 4

250000

200000

8 10

150000

100000

Rainfall (mm)

300000

50000

0 148

141

134

127

120

113

106

20 hour

Figure 5.4 Hourly infiltration in Tambito watershed for January 1998.

0 1 2 3 4 5 6 7 8 9 10

(mm/hr) veg0 veg5 veg20

141

131

121

111

101

veg30

Bulk Density

0.919 0.918 0.917 0.916 0.915 0.914 0.913 0.912 0.911 0.91

0.98

0.97

0.96

0.95 15 0.94 20

0.93

145

136

127

118

109

100

30 28

0.91 19

0.92 1

Bulk Density

hourly

Figure 5.5 Hourly bulk density at the water front in the outlet of Tambito watershed (January 1998)

The simplified hydrological model used in the evaluation of different scenarios of land use/cover reproduced with good approximation the behaviour of parameters like evaporation and infiltration reported by the literature. Some adjustments are required in the incorporation of distributed parameters related with the bulk density. The slope and interception of the equation of bulk density versus depth generated with field measurements could not be applied into the model. It was necessary to use a

general equation obtained in previous studies in the area supplied by the research team.

0.80

36.00

0.70

35.50

0.60

35.00

0.50 mm/day

34.50

0.40 34.00

0.30

33.50

0.20

33.00

0.10 Land Use/Cover 0.00 Time step

VEG0

VEG5

VEG20

VEG30

Evaporation

0.68

0.63

0.48

0.45

Infiltration

33.67

34.07

35.19

35.49

32.50

Figure 5.6 Total fluxes of evaporation and infiltration in Tambito watershed (January 1998

)

6. CONCLUSIONS

Logical rules can be produced with the study of historical patterns of land use in a simplified way. The identification of logical and general rules of land use change in environments like the selected study area is limited by the complexity associated with the diversity in land use/cover classes. This makes it necessary to simplify the class numbers with the associated loss of information. Balance between this loss of information and the reliability of the results appears as a trade off in land use modelling research.

The integration of empirical models with physically hydrological based models showed a great potential in the evaluation of 'future' scenarios of land use. Although the scenarios analysed here are not as complex as the reality is, with the identification and integration of more factual rules it can be possible to model more complex landscape structures.

Part of this research involved the use of several geographical information systems and some modelling GIS software. The problems associated with format conversion between software formed the most critical components of this study. This problem can be solve by the develop of more integrated GIS or the more friendly interfaces between different brands.

It is suggested for futures application of the model to try with more complex scenarios of land use/cover through the incorporation of new rules and extreme hydrological conditions to establish the range of response of critical variables like erosion, runoff and evaporation.

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APPENDIX 1. CABUYAL WATERSHED MAPS

Figure A.1 Land use series for 1946, 1970 and 1989 in the Cabuyal Watershed - Cauca - Colombia.

Figure A.2 Aspect, altitudinal ranges and slope in the Cabuyal Watershed - Cauca - Colombia

Figure A.3 Proximity to roads and rivers in the Cabuyal Watershed - Cauca - Colombia

APPENDIX 2. PATTERNS CHANGE ANALYSIS

Figures A.2.1 to A.2.17 illustrate the patterns of change between the three series of land use/cover and some of the observed tendencies are signalled with ovals. In all figures black lines represent rivers and red lines roads. Explanations of Figure are presented below eachone.

Figure A2.1. Forest LUC conversion in the higher zone. Dashed ovals = new land uses and pointed oval = new forest.

The first four classes in Figure A2.1 correspond with the new classes of LUC than was occupied in 1946 with forest. The two remaining classes are new areas in forest than in 1946 were pasture and scrub. It is possible to notice that the forest changed mainly towards pasture and scrub in areas adjacent to roads and partially close to river networks protected with forest before. On the other hand, new forest has re-growth close to river streams. Unchanged areas in forest are located in between the stream rivers and far from roads.

Figure A2.2. Forest LUC conversion in the lower zone. Dashed ovals = new land uses and pointed ovals = new forest

In Figure A2.2 occurs the same pattern as in the higher zone with the exception of the road influence.

Figure A2.3. Forest LUC conversion in the middle zone. Dashed ovals = new land uses and pointed ovals = new forest

In the middle zone, illustrated in Figure 3, changes occur along rivers in a clearer way than in higher and lower zones perhaps because of the antecedent conditions or the remaining forest.

Figure A2.4. Forest LUC conversion in the higher zone between 1970 - 1989. Dashed ovals = new land uses and pointed ovals = new forest

Figure A2.4 shows a similar pattern than in the period 1946 - 1970. Moreover it is fragmented in small patches along the rivers.

Figure A2.5. Forest LUC conversion in the lower zone between 1970 - 1989. Dashed ovals = new land uses and pointed ovals = new forest

Figure A2.5 illustrates a similar pattern founded in precedent series, fragmented in some cases, but with dominance of the scrub instead of pasture as a new land use after forest. Some streams are almost completely recovered with forest where pasture was before.

Figure A2.6. Scrub LUC conversion in the higher zone between 1946 - 1970. Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover

The pattern of scrub is different from forest pattern. In the first place, this LUC tends to be in its original position and as a second trend, scrub change towards pasture in both cases in areas randomly distributed between the river channels. New areas in scrubs are preceded by forest and pasture located before close to rivers.

Figure A2.7. Scrub LUC conversion in the lower zone between 1946 - 1970. Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover

Figure A2.7 shows the lower zone, which presents more extended changes related to scrubs. In first degree, the major part is turned to pastures followed by bare soil in the lowest zone. Pastures in a bigger extent than in the higher zone preceded new areas in scrub. Areas with scrub again are present along rivers. In all cases the direction of the change follow the direction of the rivers.

Figure A2.8. Scrub LUC conversion in the middle zone between 1946 - 1970. Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover

In the middle zone, the dynamic of scrub is located closer to rivers. New areas in scrub coming from pasture are more frequent than from other uses (Figure A2.8).

APPENDIX 3. CROSSTABULATION TABLES

Table A3.1. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 0 to 45 degrees. 0-3%

1946 1970 LUC1 LUC3 LUC6 LUC7 LUC1 0 3 3 LUC3 62 71 63 LUC4 0 2 8 LUC6 0 0 3 LUC7 38 23 22

Total 3 70 3 1 23

1970 1989 LUC1 LUC3 LUC4 LUC6 LUC7 LUC1 0 3 0 0 0 LUC3 18 58 45 0 37 LUC4 64 12 45 0 28 LUC5 0 12 9 0 4 LUC6 0 0 0 0 0 LUC7 18 15 0 100 31

Total 2 51 19 9 0 19

1946 1970 LUC1 LUC3 LUC6 LUC7 LUC1 14 6 0 9 LUC3 75 72 0 64 LUC4 0 4 0 2 LUC6 0 2 100 6 LUC7 11 16 0 18

Total 7 70 4 3 17

1970 1989 LUC1 LUC3 LUC4 LUC6 LUC7 LUC1 12 4 0 6 0 LUC3 21 54 30 36 53 LUC4 28 16 68 3 16 LUC5 0 15 3 0 2 LUC6 0 0 0 6 0 LUC7 39 12 0 48 30

Total 4 50 18 11 0 17

12 - 30 % 1946 1970 LUC1 LUC3 LUC6 LUC7 LUC1 23 5 0 13 LUC3 63 76 0 54 LUC4 0 2 0 1 LUC6 0 1 100 13 LUC7 13 15 0 18

Total 8 68 2 6 16

1970 1989 LUC1 LUC3 LUC4 LUC6 LUC7 LUC1 18 3 0 5 2 LUC3 35 49 26 36 36 LUC4 16 17 74 27 10 LUC5 0 10 0 0 1 LUC6 0 0 0 5 0 LUC7 30 22 0 28 52

Total 4 44 17 7 0 28

30 - 50 % 1946 1970 LUC1 LUC3 LUC6 LUC7 LUC1 38 4 13 LUC3 50 71 44 LUC4 0 2 0 LUC6 4 2 29 LUC7 8 22 14

Total 10 55 1 17 17

1970 1989 LUC1 LUC3 LUC4 LUC6 LUC7 LUC1 23 0 0 5 1 LUC3 22 53 50 31 32 LUC4 8 11 50 13 0 LUC5 0 5 0 0 0 LUC6 0 0 0 0 0 LUC7 48 31 0 51 67

Total 3 43 9 3 0 42

50 - 75 % 1946 1970 LUC1 LUC3 LUC6 LUC7 LUC1 20 10 12 LUC3 70 36 35 LUC4 0 3 0 LUC6 0 2 42 LUC7 10 48 11

Total 12 36 1 28 23

1970 1989 LUC1 LUC3 LUC4 LUC6 LUC7 LUC1 29 0 0 2 0 LUC3 15 63 100 18 22 LUC4 0 2 0 12 0 LUC5 0 2 0 0 0 LUC6 0 0 0 0 0 LUC7 56 34 0 69 78

Total 4 35 4 1 0 56

Total 12 27 0 24 37

1970 1989 LUC1 LUC3 LUC4 LUC6 LUC7 LUC1 36 0 4 2 LUC3 29 75 14 26 LUC4 0 0 18 0 LUC5 0 3 0 0 LUC6 0 0 0 0 LUC7 36 22 64 72

Total 6 37 4 1 0 52

3 - 12 %

> 75 %

LUC1 LUC4 LUC7

1946 1970 LUC1 LUC3 LUC6 LUC7 LUC1 0 14 11 LUC3 0 31 27 LUC4 0 0 0 LUC6 0 3 34 LUC7 100 53 28

Forest Annual crops Scrub

LUC2 LUC5

Pine Seasonal crops

LUC3 LUC6

Pasture Bare Soil

Table A3.2. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 45 to 90 degrees. 0-3%

1946 1970

1970

LUC1 LUC3 LUC6 LUC7 Total

1989

LUC1 3 - 12 %

1970

LUC1 LUC3 LUC6 LUC7 Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7 Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7 Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7 Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

30 - 50 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7 Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7 Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

50 - 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7 Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7 Total

LUC1

LUC3

LUC4

100

LUC6

LUC5

LUC7

LUC6

LUC7

> 75 %

1946 1970

LUC1 LUC4 LUC7

LUC1 1946

1970

LUC1 LUC3 LUC4 LUC6 LUC7 Total

1970

LUC1 LUC3 LUC6 LUC7 Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7 Total

LUC1

LUC3

100

LUC4

LUC6

LUC5

LUC7

100

LUC6

LUC7

Forest Annual crops Scrub

LUC2 LUC5

Pine Seasonal crops

LUC3 LUC6

Pasture Bare Soil

Table A3.3. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 90 to 135 degrees. 0-3%

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

3 - 12 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

Total

100

30 - 50 %

1946

1970 Total

1989

LUC1

1970 LUC1 LUC3 LUC6 LUC7 0

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

50 - 75 %

1946

LUC1 LUC3 LUC4 LUC6 LUC7

Total

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

100

LUC6

LUC5

LUC7

LUC6

LUC7

> 75 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

100

Table A3.4. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 135 to 180 degrees. 0-3%

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

LUC1

LUC3

LUC4

LUC6

LUC7

3 - 12 %

LUC3

LUC4

100

LUC5

100

LUC6

LUC7

1946 1970

Total

1970

LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

30 - 50 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

Total

100

LUC7

50 - 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

100

> 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC3

100

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

100

Table A3.5. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 180 to 215 degrees. 0-3%

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

3 - 12 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

Total

100

LUC7

Total

100

30 - 50 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

50 - 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC4

1 4

LUC6

LUC5

LUC7

LUC6

LUC7

> 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

Table A3.6. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 215 to 270 degrees. 0-3%

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

3 - 12 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

30 - 50 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

50 - 75 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

> 75 %

1946

1970

1970 LUC1 LUC3 LUC6 LUC7

Total

1989 LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

Table A3.7. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 270 to 315 degrees. 0-3%

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

3 - 12 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

30 - 50 %

1946 1970

1970 Total

1989

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

50 - 75 %

LUC1 LUC3 LUC6 LUC7

1946 1970

LUC1 LUC3 LUC4 LUC6 LUC7

Total

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

> 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

Table A3.8. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650 MASL and for aspect from 315 to 359 degrees. 0-3%

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC3

100

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

3 - 12 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

12 - 30 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

100

LUC5

LUC7

LUC6

LUC7

30 - 50 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC4

7 2

LUC6

LUC5

LUC7

LUC6

LUC7

50 - 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

> 75 %

1946 1970

1970

LUC1 LUC3 LUC6 LUC7

Total

1989

LUC1 LUC3 LUC4 LUC6 LUC7

Total

LUC1

LUC3

100

LUC3

LUC4

LUC6

LUC5

LUC7

LUC6

LUC7

Table A3.9 Conversion from Forest in 1946 towards different land covers in 1970 considering the frequency of the neighbours of the same class (n=8814 pixels). % neighbourhood 1 2 3 4 5 6 7 8 9

Land Use Forest 1 1 1 1 1 1 1 1 10

Pasture 5 4 5 3 4 5 4 4 22

Scrub 2 2 2 1 1 2 1 2 10

Table A5.10 Conversion from Pasture in 1946 towards different land covers in 1970 considering the frequency of the neighbours of the same class (n=23116 pixels). % neighbourhood 1 2 3 4 5 6 7 8 9

Land Use Forest 0 0 0 0 0 0 0 0 4

Pasture 3 3 4 3 3 5 4 5 45

Scrub 1 1 1 1 1 1 1 1 11

Table A3.11 Conversion from Scrub in 1946 towards different land covers in 1970 considering the frequency of the neighbours of the same class (n=21047 pixels). % Neighbourhood 1 2 3 4 5 6 7 8 9

Land Use Forest 1 0 1 0 0 0 0 0 5

Pasture 4 3 4 2 2 4 2 3 25

Scrub 2 1 2 1 1 2 1 2 30

APPENDIX 4. CELLULAR AUTOMATA RULES AND MODEL

Variables and classes used in the identification of cellular automata rules: Cover: (Land use/cover) Forest Pasture Scrub Probability transition 0: = doesn t occur 1: >= 50 % 2: < 50 % Slope (Percentage) 0-3% 3 - 12 % 12 - 30 % 30 - 50 % 50 - 75 % > 75 % Aspect (Azimut) Asp1: 0 - 45 Asp2: 45 - 90 Asp3: 90 - 135 Asp4: 135 - 180 Asp5: 180 - 225 Asp6: 225 - 270 Asp7: 270 - 315 Asp8: 315 - 359 Altitude (meters) Alt1: < 1650 MASL Alt2: > 1650 MASL */ altitud < 1650 and aspect between 0 and 45 degrees. if altitude < 1650 and aspect > 0 and < 45 and slope > 0 and < 3 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope > 3 and < 12 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope > 12 and < 30 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope = > 30 and < 50 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope = > 50 and < 75 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope = > 75 and cover = 1 then newcover = 3 if altitude < 1650 and aspect > 0 and < 45 and slope > 0 and < 3 and cover = 2 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope > 3 and < 12 and cover = 2 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope > 12 and < 30 and cover = 2 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope = > 30 and < 50 and cover = 2 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope = > 50 and < 75 and cover = 2 then newcover = 3 if altitude < 1650 and aspect > 0 and < 45 and slope = > 75 and cover = 2 then newcover = 3 if altitude < 1650 and aspect > 0 and < 45 and slope > 0 and < 3 and cover = 3 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope > 3 and < 12 and cover = 3 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope > 12 and < 30 and cover = 3 then newcover = 2 if altitude < 1650 and aspect > 0 and < 45 and slope = > 30 and < 50 and cover = 3 then newcover = 3 if altitude < 1650 and aspect > 0 and < 45 and slope = > 50 and < 75 and cover = 3 then newcover = 3 if altitude < 1650 and aspect > 0 and < 45 and slope = > 75 and cover = 3 then newcover = 3 */ altitud < 1650 and aspect between 45 and 90 degrees. if altitude < 1650 and aspect > 45 and < 90 and slope > 0 and < 3 and cover = 1 then newcover = 0 if altitude < 1650 and aspect > 45 and < 90 and slope > 3 and < 12 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 45 and < 90 and slope > 12 and < 30 and cover = 1 then newcover = 2

if altitude < 1650 and aspect > 45 and < 90 and slope = > 30 and < 50 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 45 and < 90 and slope = > 50 and < 75 and cover = 1 then newcover = 2 if altitude < 1650 and aspect > 45 and < 90 and slope = > 75 and cover = 1 then newcover = 3 . . .

*/ Neighbourhood analysis 3 by 3 if neighbourhood if neighbourhood if neighbourhood if neighbourhood

> 1 and < 9 and cover = 1 then newcover = 2 > 1 and < 9 and cover = 2 then newcover = 2 > 1 and < 8 and cover = 3 then newcover = 2 > 8 and cover = 3 then newcover = 3

*/road distance If roaddistance < 200 and cover = 1 then newcover = 2 If roaddistance < 200 and cover = 2 then newcover = 2 If roaddistance < 200 and cover = 3 then newcover = 3 If roaddistance > 200 and < 400 and cover = 1 then newcover = 2 If roaddistance > 200 and < 400 and cover = 2 then newcover = 2 If roaddistance > 200 and < 400 and cover = 3 then newcover = 2 If roaddistance > 400 and < 600 and cover = 1 then newcover = 2 If roaddistance > 400 and < 600 and cover = 2 then newcover = 2 If roaddistance > 400 and < 600 and cover = 3 then newcover = 3 If roaddistance > 600 and cover = 1 then newcover = 2 If roaddistance > 600 and cover = 2 then newcover = 2 If roaddistance > 600 and cover = 3 then newcover = 2 */ river distance If riverdistance < 100 and cover = 1 then newcover = 2 If riverdistance < 100 and cover = 2 then newcover = 2 If riverdistance < 100 and cover = 3 then newcover = 3 If riverdistance > 100 and < 200 and cover = 1 then newcover = 2 If riverdistance > 100 and < 200 and cover = 2 then newcover = 2 If riverdistance > 100 and < 200 and cover = 3 then newcover = 3 If riverdistance > 200 and < 300 and cover = 1 then newcover = 2 If riverdistance > 200 and < 300 and cover = 2 then newcover = 2 If riverdistance > 200 and < 300 and cover = 3 then newcover = 2 If riverdistance > 300 and cover = 1 then newcover = 2 If riverdistance > 300 and cover = 2 then newcover = 2 If riverdistance > 300 and cover = 3 then newcover = 2

CELLULAR AUTOMATA MODEL

# Cellular Automata Model. Mark Mulligan September 1998 # one time slice represent undefined time. binding #maps #input InitLandUse=veg.map; Roads=roads.map; Rivers=rivers.map; SlopeDeg=slopedeg.map; Pits=pits.map; #output LandUse=Landuse; Majority=majority; ProxPit=proxpit.map; ProxRiv=proxriv.map; 69

ProxRoads=Proxroad.map; ProxDefor=proxdef.map; Defor=defor; BoolTrue=booltrue.map; NewX=newx; Newy=newy; NewSec=newsec; #time series #input Time=time.tss; Random=random.tss; #output #tables #constants cellsize=25; ProbSlope=1; Nnew=10; areamap clone.map; timer 1 50 1;

#hours

initial LandUse=InitLandUse; report ProxRiv=spread(Rivers,0,sin(SlopeDeg)); report ProxPit=spread(Pits,0,sin(SlopeDeg)); ProxRoads=if(mapmaximum(ordinal(Roads)) gt 0 then spread(Roads,0,sin(SlopeDeg)) else 0); dynamic #1 is primary 2 is secondary 3 is deforested #random seed for regrowth of secondary

Defor=boolean(if(LandUse eq 3 then 1 else 0)); report ProxDefor=spread(Defor,0,sin(SlopeDeg));

Defor=if(ProxDefor le mapminimum(ProxDefor)+cellsize then 1 else Defor); Defor=if(ProxPit le mapminimum(ProxPit)+cellsize then 1 else Defor); Defor=if(ProxRoads le mapminimum(ProxRoads)+cellsize then 1 else Defor);

RandomX=timeinputscalar(Random,1); RandomY=timeinputscalar(Random,2);

NewX=boolean(if(xcoordinate(BoolTrue) ge 1006600+(RandomX*cellsize)-12.5 and xcoordinate(BoolTrue) le 1006600+(RandomX*cellsize)+12.5 then 1 else 0)); NewY=boolean(if(ycoordinate(BoolTrue) ge 766075+(RandomY*cellsize)-12.5 and ycoordinate(BoolTrue) le 766075+(RandomY*cellsize)+12.5 then 1 else 0)); LandUse=if(NewX eq 1 and NewY eq 1 and LandUse eq 3 then 2 else LandUse);

#neighbourhood LandUse=if(Defor eq 1 then 3 else LandUse); Majority=windowmajority(LandUse,cellsize); report LandUse=if(LandUse ne Majority then Majority else LandUse);

APPENDIX 5. HYDROLOGICAL MODEL CODE.

# Bendum Hydro Model. (C) Mark MUlligan, Department of Geography, King's College London. # December 1997. Updated August 1998. # one time slice represents one hour binding #maps #input Vegetation=veg.map; RainStat=rainstat.map; TopMod=topmod.map; Porosity=poros.map; Init=init.map; Sand=sand.map; Silt=silt.map; Clay=clay.map; Lddmap=ldd.map; sampleplaces=samples.map; Slopedeg=slopedeg.map; Aspectdeg=aspect.map; #0-360 aspect map pits=pits.map; SpecificWaterRetention=Specwat.map; #output IntercEvap=ievap; Rainfall=rainfa; KsAtWF=ksatwf; SoilDepth=soild.map; Runoff=runoff; Infil=infil; WF=DepWf; solarmap=solar; netmap=net; Evap=evap; Theta=theta; LeafAreaIndex=LAI; LeafBiomass=leafbiom.map; RootBiomass=rootbiom; Erosion=erosion; BDatWF=bdatwf; Recharge=recharge; #Tempsum=tempsum.map; #Tempsand=tempsand.map; #Tempsilt=tempsilt.map; #Tempclay=Tempclay.map; #TempMPd=Tempmpd.map; #TempSdPd=tempsdpd.map; #TempPhi=tempphi.map; Bvalue=bvalue.map; CanopyStorage=canstor.map; #time series #input RainFile=rainfall.tss; Time=time.tss; Clouds=Cloud.tss; #output KsatWFTimeSeries=minkswf.tss; SolarTimeSeries=sumsolar.tss; EvapTimeSeries=sumevap.tss; thetimets=thetime.tss; InfilTimeSeries=suminfil.tss; RunoffTimeSeries=sumrunof.tss; BDatWFTimeSeries=mnBdatwf.tss; TotRainTimeSeries=totrain.tss;

#tables IBData=ibdata.tbl; #constants Latitude=2.5; # Longitude=77; #positive west GMeridian=0; SolarConst=1367; SSTB=0; pi=3.141592654; MaxDepth=3.94; #should ensure that this tallies with BD depth function so max BD=2.6; BDslope=0.5921; #slope of BD function BDinterc=0.9; #interc of BD function RUE=5.5; AirTemp=20; LeafDensity=270; #g/m2 K=0.2; #soil erodability N=1.66; #Musgrave M=2.0; #Musgrave NetRadIntercept=-3.56;#based on bendum aws NetRadSlope=0.719;#based on bendum aws RockD =2.6; #Rock density (g/cm3) #SoilDepth=1.0;#metres areamap dem.map; timer 1 300 1; #hours

initial report SoilDepth=1.0+(TopMod/mapmaximum(TopMod))*(MaxDepth-1.0); WF=SoilDepth*1000*0.01; #mm - approx 5% Theta=(WF/(SoilDepth*1000)); SlopeDeg=scalar(Slopedeg);#degrees -OK AspectDeg=(180-scalar(Aspectdeg)); #degrees -OK #solar.map=0;#for sums only InitialBiomass=lookupscalar(IBData,Vegetation); LeafAreaIndex=(0.5*InitialBiomass)/LeafDensity; LeafBiomass=0.5*InitialBiomass; RootBiomass=0.5*InitialBiomass; Cover=1; CanopyStorageCapacity=SpecificWaterRetention*Cover*LeafAreaIndex; CanopyStorage=0; dynamic #------------------------------solar radiation--------------------------#PCRASTER calculates trig functions using the inputs in degrees. JulDay=timeinputscalar(Time,1); thetime=timeinputscalar(Time,2); DayAngle=(2*pi*(JulDay/365))*(180/pi); #degrees Declination=(0.006918-0.399912*cos(DayAngle)+0.070257*sin(DayAngle)0.006758*cos(2*DayAngle)+0.000907 *sin(2*DayAngle)-0.002697*cos(3*DayAngle)+0.00148*sin(3*DayAngle))*(180/pi); #degrees #report thetimets=mapmaximum(thetime); Solartime=thetime+(4*(GMeridian-Longitude))+((0.000075+0.001868*cos(DayAngle) -0.032077*sin(DayAngle)-0.014615*cos(2*DayAngle)-0.04089*sin(2*DayAngle))*(229.18)); #degrees HourAngle=(((1200-(Solartime-50))/100)*15); #{degrees} OrbitalEcc=1+0.033*cos((2*pi*JulDay/365)); #radians SSTA=SolarConst*OrbitalEcc*((sin(Latitude)*cos(SlopeDeg)-cos(Latitude) *sin(SlopeDeg)*cos(AspectDeg))*sin(Declination)+(cos(Latitude)*cos(SlopeDeg)+sin(Latitud e)* sin(SlopeDeg)*cos(AspectDeg))*cos(Declination)*cos(HourAngle)+cos(Declination)*sin(Slop eDeg)* sin(AspectDeg)*sin(HourAngle));#{W/m2} SSTA= if(SSTA gt 0 then SSTA else 0); Cloud=0.5; #Cloud=timeinputscalar(Clouds,1); SSTA=SSTA*(1-Cloud); #atmos attenuation - monte carlo

solarmap=SSTA; report SolarTimeSeries=maptotal(SSTA); SSTA=NetRadIntercept+(NetRadSlope*SSTA); #net radiation (W/m2) netmap=SSTA; SSTA=(SSTA*60*60)/1000000; #net radiation MJ PAR=0.5*SSTA; EtFrac=Theta*(1-(CanopyStorage/CanopyStorageCapacity)); InLFrac=(CanopyStorage/CanopyStorageCapacity); PotEvap = if(SSTA gt 0 then (SSTA/2.45) else 0); report Evap=PotEvap*(((1-Cover)*Theta)+(Cover*LeafAreaIndex*EtFrac)); report IntercEvap=PotEvap*(Cover*LeafAreaIndex*InLFrac); report EvapTimeSeries=maptotal(Evap);

#-------------------------------hydroparameters-----------------------------------------------------Tempsum=(Sand*ln(1.025))+(Silt*ln(0.026))+(Clay*ln(0.001)); Tempsand=Sand*(ln(1.025)**2)-(Tempsum**2); Tempsilt=Silt*(ln(0.026)**2)-(Tempsum**2); Tempclay=Clay*(ln(0.001)**2)-(Tempsum**2); TempMPd=exp(Tempsum); TempSdPd=abs(Tempsand+Tempsilt+Tempclay)**0.5; TempPhi=-0.5*TempMPd**-0.5; Bvalue=-2*TempPhi+0.2*TempSdPd; #PhiEtoWF=TempPhi*(BDinterc+(BDatWF-BDinterc)/2); #PhiEWhole=TempPhi*(BDinterc+((BDslope*SoilDepth)/2)); # i.e halfway between BD at surface and final BD BDatBedrock=(BDslope*SoilDepth)+BDinterc; #g/cm3 BDatBedrock=if(BDatBedrock gt RockD then RockD else BDatBedrock); KsatBedrock=((4*10**-3)*((1.3/BDatBedrock)**(1.3*Bvalue))*exp(6.9*Clay3.7*Sand))*35280; #-------------------------------hydrology-----------------------------------------------------Rainfall=timeinputscalar(RainFile,RainStat); CanopyStorageCapacity=SpecificWaterRetention*Cover*LeafAreaIndex; CanopyEmpty=CanopyStorageCapacity-CanopyStorage; report Rainfall=if(Rainfall le CanopyEmpty then 0 else Rainfall-CanopyEmpty); CanopyStorage=if(Rainfall le CanopyEmpty then CanopyStorage+Rainfall else CanopyStorageCapacity); BDatWF=(BDslope*(WF/1000))+BDinterc;#g/cm3 assumes that a shallower soil has a lower final bd than deep soil BDatWF=if(BDatWF gt RockD then RockD else BDatWF); report BDatWFTimeSeries=mapminimum(BDatWF); KsAtWF=((4*10**-3)*((1.3/BDatWF)**(1.3*Bvalue))*exp(-6.9*Clay-3.7*Sand))*35280;#mm/hr report KsAtWF = if(WF/1000 gt SoilDepth then KsatBedrock else KsAtWF); #lower bdy report KsatWFTimeSeries=mapminimum(KsAtWF); Runoff, Infil=accuthresholdflux,accuthresholdstate(Lddmap,Rainfall,KsAtWF); report RunoffTimeSeries=timeoutput(pits,Runoff); report Runoff=Runoff*0.0004444; # conversion to cumecs for cellsize of 40 m (1hr timestep) report Infil=Infil*1; report TotRainTimeSeries=maptotal(Rainfall); report InfilTimeSeries=maptotal(Infil); report Recharge=KsatBedrock*((Theta)**(2*Bvalue+3)); # mm #Throughflux=Theta*tan( SlopeDeg); #Throughflow=accuflux( lddmap,Throughflux); BDAhead=(BDslope*((WF+(Infil-Evap-Recharge*RockD))/1000))+BDinterc;#g/cm3 to infil*2.6 mm ahead #wrong? BDAhead=if(BDAhead gt RockD then RockD else BDAhead); PorAhead=1-((BDatWF+((BDAhead-BDatWF)/2))/RockD); # fractional WF=WF+((Infil-Evap-Recharge)/PorAhead); #mm WF=if(WF lt 0 then 0 else WF); WF = if(WF/1000 gt SoilDepth then SoilDepth*1000 else WF); Theta=Theta+((Infil-Evap-Recharge)/(SoilDepth*1000)); #m3water/m3soil report Theta=if(Theta lt 0 then 0 else Theta); report CanopyStorage=if(IntercEvap le CanopyStorage then CanopyStorage-IntercEvap else 0); #-------------------------------growth------------------------------------------------------

Growth=(RUE*Theta)*(0.95*(1-exp(-0.7*LeafAreaIndex))*PAR);#grams Growth=Growth*0.8; #growth resp Maintenance=(0.015*(((AirTemp-15)/10)**1.5))/24; #g/g/hr LeafBiomass=LeafBiomass+(Theta*Growth)-(Maintenance*LeafBiomass); RootBiomass=RootBiomass+((1-Theta)*Growth)-(Maintenance*RootBiomass); LeafBiomass=if(LeafBiomass le 0 then 0.001 else LeafBiomass);#grams report RootBiomass=if(RootBiomass le 0 then 0.001 else LeafBiomass);#grams report LeafAreaIndex=LeafBiomass/LeafDensity; Cover=if(LeafAreaIndex ge 1 then 1 else LeafAreaIndex); #Treefall=1-(Slopedeg/90)*; #Cover=1-(SlopeDeg/90);#treefall controlled cover

#-------------------------------Erosion------------------------------------------------------

report Erosion=K*(Runoff**M)*(SlopeDeg**N)*(2.71**(-0.07*Cover)); #mm #SoilDepth=SoilDepth-(Erosion/1000);

APPENDIX 6. SOIL DATA

Table A.1 Soil properties corresponding with the 25 classes of the sampling scheme. Soil map zone 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

slope vegetation aspect Ksat class class class (cm/s) 1 1 2 2 3 3 3 2 1 3 1 3 3 2 2 2 3 1 1 3 1 2 2 3 2

4 4 4 4 4 4 6 6 6 6 6 2 2 6 2 2 2 2 2 6 4 2 4 4 6

2 1 2 1 2 1 2 2 2 1 1 2 0 1 2 1 1 2 1 0 0 0 0 0 0

0.0012 0.0012 0.0028 0.0028 0.0028 0.0028 0.0028 0.0027 0.0006 0.0020 0.0006 0.0071 0.0071 0.0027 0.0047 0.0047 0.0071 0.0077 0.0077 0.0020 0.0012 0.0047 0.0028 0.0028 0.0027

Bulk Clay Silt Density 0.69 0.69 0.80 0.80 0.85 0.85 0.83 1.00 0.96 0.94 0.96 0.74 0.74 1.00 0.85 0.85 0.74 0.87 0.87 0.94 0.69 0.85 0.80 0.85 1.00

0.63 0.63 0.57 0.57 0.56 0.56 0.56 0.58 0.60 0.53 0.60 0.58 0.58 0.58 0.56 0.56 0.58 0.58 0.58 0.53 0.63 0.56 0.57 0.56 0.58

0.20 0.20 0.24 0.24 0.24 0.24 0.24 0.22 0.19 0.23 0.19 0.20 0.20 0.22 0.23 0.23 0.20 0.19 0.19 0.23 0.20 0.23 0.24 0.24 0.22

Sand Porosity Erodability 0.17 0.17 0.19 0.19 0.20 0.20 0.20 0.20 0.21 0.24 0.21 0.22 0.22 0.20 0.21 0.21 0.22 0.23 0.23 0.24 0.17 0.21 0.19 0.20 0.20

0.72 0.72 0.68 0.68 0.66 0.66 0.67 0.58 0.62 0.63 0.62 0.71 0.71 0.58 0.66 0.66 0.71 0.65 0.65 0.63 0.72 0.66 0.68 0.66 0.58

0.19 0.19 0.19 0.19 0.29 0.19 0.21 0.19 0.19 0.21 0.21 0.19 0.21 0.21 0.19 0.21 0.21 0.21 0.21 0.29 0.29 0.29 0.29 0.29 0.29

APPENDIX 7. VEGETATION FIELD MEASUREMENTS

Table A7.1 Leave measurements in Primary Forest Plot. Tambito, Cauca - Colombia FOREST PLOT ID number 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0 41.0 50.0 51.0 52.0 53.0 Total

Dry weight Wet weight water storage 13.7 20.6 6.8 12.6 17.3 4.7 10.3 15.1 4.7 15.6 21.4 5.8 2.9 3.8 0.9 1.4 2.1 0.7 1.6 2.4 0.8 10.3 14.6 4.4 13.8 22.4 8.6 169.5 211.1 41.6 17.7 28.1 10.4 17.0 27.2 10.2 27.6 31.9 4.3 13.5 19.6 6.1 25.6 30.3 4.8 54.9 60.3 5.4 23.6 30.5 6.9 39.6 44.4 4.8 11.4 15.7 4.3 40.2 45.5 5.4 11.4 16.4 5.0 4.0 4.5 0.5 48.4 62.2 13.8 10.7 15.6 4.9 36.9 46.7 9.8 57.4 75.2 17.8 14.1 17.3 3.2 34.1 44.6 10.5 46.8 76.5 29.7 7.4 11.1 3.6 18.7 27.5 8.8 37.8 74.2 36.4 23.7 28.3 4.6 34.3 40.1 5.7 68.8 95.2 26.4 81.3 125.5 44.2 24.2 30.2 6.0 46.6 56.5 9.9 130.3 148.9 18.6 45.7 59.3 13.6 23.4 31.9 8.5 34.2 42.4 8.2 1363.0 1794.1 431.2

Table A7.2. Leave measurements in scanned images from Primary Forest in Tambito, Cauca - Colombia. FOREST PLOT File Name Area (cm2) jrub11a 5550.0 jrub13 4644.0 jrub9 4654.5 jrub15 7149.0 jrub14 7149.0 jrub16a 7149.0 jrub12 7149.0 jrub17 7149.0 Total 50593.5

Pixel No. Pixels on leaves Area of leaves in cm2 354460.0 105480.0 1651.6 278880.0 144884.0 2412.7 356547.0 137515.0 1795.2 225330.0 73541.0 2333.2 326849.0 62328.0 1363.3 320661.0 191354.0 4266.2 225375.0 66752.0 2117.4 299837.0 209934.0 5005.4 2387939.0 991788.0 21013.1

Table A7.3. Leave measurements in Secondary Forest Plot. Tambito, Cauca - Colombia SECONDARY FOREST PLOT ID number Dry weight Wet weight Water storage 1.0 24.1 40.0 15.9 2.0 13.1 20.2 7.0 3.0 23.0 42.2 19.2 4.0 71.4 92.5 21.1 5.0 140.2 160.3 20.2 6.0 96.1 111.6 15.5 7.0 24.5 34.6 10.1 8.0 42.3 56.7 14.4 9.0 4.3 5.9 1.6 10.0 2.1 3.0 0.9 11.0 25.9 31.9 5.9 12.0 19.7 32.3 12.5 Total 527.2 683.6 156.4

Table A7.4. Leave measurements in scanned images from Secondary Forest in Tambito, Cauca - Colombia. SECONDARY FOREST PLOT File Name Area (cm2) Pixel No. Pixels on leaves Area of leaves Total Dry in cm2 weight jrub28 7149.0 346480.0 224872.0 4639.8 271.8 jrub27 7149.0 393451.0 137927.0 2506.1 214.9 7146.0 486.7

Table A7.5. Pasture leave measurements in Tambito, Cauca - Colombia. PASTURE PLOT sample Dry weight Wet weight Water storage Area Leaves (cm2) 1.0 6.6 12.5 5.9 184.2 2.0 7.9 13.4 5.5 140.9 3.0 7.2 19.8 12.6 291.2 4.0 6.1 10.0 3.9 112.8 5.0 4.8 9.6 4.8 153.5 Total 32.5 65.3 32.8 882.5

Table A7.6 Vegetation Parameters for three different Land Use in Cauca - Colombia Land Use Type

Leaves area (m2)

Leaves weight (g)

Primary Forest Secondary Forest Pasture

2.1 0.7 0.1

1363.0 486.7 32.5

431.2 144.4 32.8

1.4 1.4 2.1

649.0 685.5 368.5

Specific Leaf Area (m2/g)

Initial Biomass (g/m2)

Specific Water Retention (g/m2)

Cover

Canopy Storage Capacity (mm)

0.0015 0.0015 0.0027

1804.4 1905.7 59.0

205.3 203.4 371.9

0.9 0.9 1.0

0.25 0.25 0.03

Primary Forest Secondary Forest Pasture

Water weight (g) Leaf Area Index

Leaf Density (g/m2)

Figure A7.1. Primary forest leaves scanned from pictures taken in Tambito, Cauca - Colombia

Figure A7.2. Secondary forest leaves scanned from pictures taken in Tambito, Cauca - Colombia

Figure A7.3. Pasture leaves scanned from pictures taken in Tambito, Cauca - Colombia

Figure A7.4. Canopy Forest cover scanned from pictures taken in Tambito, Cauca - Colombia