Modelling of intensive and extensive farming in CLUE

land use modelling framework EURURALIS, and will allow EURURALIS to predict the effect on land use intensity of future policy under different scenarios. In turn, this makes it possible to predict policy effects on intensity-related biodiversity issues on the EU-level. Our method defines agricultural land use intensity in terms of nitrogen input. For arable land, it first combines the Land Use / Cover Area frame statistical Survey (LUCAS) dataset with Common Agricultural Policy Regionalised Impact modelling system (CAPRI) results to assess probability of occurrence for three classes of intensity. For grassland, it uses available spatially explicit predictions of livestock intensity to assess probability of occurrence for two classes of intensity. Then, agricultural land in different intensity classes is spatially allocated using a simple allocation algorithm. We illustrate and evaluate this method for five countries: the Netherlands, Portugal, Spain, Greece and Poland. Intensity predictions are made for two years: 2000 (ex-post) and 2025 (using the Financial Policy Reform Scenario from the FP6 EU SENSOR project). This report contains building bocks for a possible future quality status of the method

Using Climber's Guidebooks to Assess Rock Fall Patterns Over Large Spatial and Decadal Temporal Scales: An Example from the Swiss Alps

High-mountain geomorphic processes enjoy increasing scientific and societal interest. This is because these processes are perceived to be changing more than elsewhere and because their effects on infrastructure and tourism are significant. Rock fall is among the processes that receive most attention due to its presumed intimate relation with permafrost, which is widely degrading. However, over decadal temporal scales and for entire mountain ranges, there is very limited information on the changes in frequency and location of rock fall. This hampers our understanding. Here, I assess the value of information contained in a 146-year record of climber's guidebooks of the Bernese Alps in Switzerland to derive changes in rock fall danger. The results show that guidebooks’ authors, themselves experienced climbers, perceived increases in rates and changes in positions of rock fall. The increases were mainly reported since the year 2000. It appears that datasets derived from guidebooks can provide valuable context for more detailed, higher resolution data sources.<br/

Understanding landscape dynamics over thousand years : combining field and model work : with case study in the Drakensberg foothill, KwaZulu-Natal, South Africa

The title of this thesis is “Understanding landscape dynamics over thousands of years : combining field and model work, with a case study in the Drakensberg Foothills, KwaZulu-Natal, South Africa”. As the title clearly states, the overall objective is an increased knowledge of landscape dynamics through the combination of fieldwork and landscape evolution modelling. Fieldwork is the topic of Chapter 2. The 50 kilo-annum (ka) landscape evolution of the research area in Okhombe valley in the Drakensberg Foothills is studied. Results are presented from extensive fieldwork in Okhombe valley, combined with laboratory work. Starting around 50 ka and continuing until around 30 ka, with cooler temperatures and more rainfall than at present, the slow processes of solifluction and creep transported material from the steep upper slopes of the research area to the concave areas that were immediately downstream. At least two major mudflow events partly or completely covered the solifluction deposits at the end of this period, around 29 ka. When temperatures and rainfall decreased toward the Last Glacial Maximum, grassland was likely replaced by denser shrubland. Overland flow and water erosion were inhibited. At the onset of warmer and wetter climate around 15 ka, shrubby vegetation retreated to higher altitudes and Okhombe valley was again covered with grassland. This decrease in vegetation cover, together with increased rainfall, resulted in higher rates of fluvial redistribution. Presently, erosion is still widespread in the area. The knowledge of landscape evolution was put to the test in a landscape evolution model in Chapter 5. Chapters 3 and 4 prepared the LAPSUS model for this task by discussing two important aspects of landscape evolution modelling. Chapter 3 presents a method to deal with an important conceptual and technical issue in long-term landscape evolution modelling. Conventional models consider depressions in Digital Elevation Models (DEMs) spurious, and remove them before modelling. Long-term multi-process landscape evolution models predict depressions, that therefore must be considered non-spurious. A method is detailed that allows these models to identify and include these depressions in dynamic landscapes. Identification first finds sinks, then adds neighbouring cells to the corresponding depression until a saddle is crossed. Inclusion of depressions in the dynamic landscape led to a procedure to deal with flows of water and sediment into and out of depressions. Depressions can be completely or partly filled with sediment. Partial filling, from each of the neighbouring cells, takes the shape of an above- and below-water delta with user-defined slope. Chapter 4 discusses ways to more formally list, make and report choices involved in setting-up multi-process landscape evolution models. This discussion is necessary now that models are increasingly combining multiple processes in one study. Choices in model set-up must be made regarding the extent and resolution of time, space and processes. A scheme is presented that can guide workers in making these choices, and tests to determine case-optimal set-ups are discussed using four case studies. In Chapter 5 , LAPSUS is used with the lessons from Chapters 3 and 4 in mind, to test the landscape reconstruction developed in Chapter 2. Adding to existing process descriptions, the processes of creep, solifluction and biological and frost weathering were developed for LAPSUS. A sensitivity analysis was performed, both for individual processes and for the overall model. Model calibration was trial and error and of qualitative nature. It attempted to simultaneously match model results to fieldwork conclusions for three outputs: zonal process activity over time, relative process activity over time and zonal development of soildepth. After calibration, model results suggested that a very slow wave of sediment moved through the landscape after the onset of the Holocene. Waves of sediment this slow have not been reported before. It is also suggested that erosion following this wave is continuing until today. Chapter 5 also shows that landscape evolution model results allow significant refinements of single-process interpretations of deposits, and can fill in erosional hiatuses in stratigraphical records. Chapter 6 goes one step further and tests whether the LAPSUS version of Chapter 5 is able to discriminate between landscape responses to stable and changed climate for the next millenium in Okhombe valley. This is an important first step in the use of landscape evolution models in the assessment of the effect of human-induced changing climate. Results of landscape evolution models are, of course, uncertain. This chapter tests the influence of parameter uncertainty, assumes that the influence of uncertainty in process descriptions and model structure is minor, and ignores uncertainty in input values (e.g. climatic records). LAPSUS was run hundreds of times, using random parameter values drawn from their joint probability distributions for three levels of assumed uncertainty and for stable and changed climate. Results indicate that LAPSUS can discriminate between the two climate scenarios in most cases, even at the highest level of parameter uncertainty. An explorative, uncertain and relative conclusion about changes in landscape evolution as a result of climate change can be drawn: erosion will likely be stronger in the concave positions, and deposition will likely be stronger further downstream than under stable climate. Chapter 7 combines results of the previous chapters. A subdivision of similar deposits in KwaZulu-Natal in four types is proposed using knowledge about the conditions that resulted in the deposits in Okhombe valley. Then, four innovations in landscape evolution modelling that the work in chapter 3-6 has contributed to, are summarized. These innovations are combined into a proposal for iterative model-fieldwork combinations in geomorphology. Eventually the focus is on the role that landscape evolution models can play in studies of land dynamics, given their inherent complex systems’ properties. <br/

Multi-process Late Quaternary landscape evolution modelling reveals lags in climate response over small spatial scales

Landscapes evolve in complex, non-linear ways over Quaternary timespans. Integrated geomorphological field studies usually yield plausible hypotheses about timing and impact of process activity. Landscape Evolution Models (LEMs) have the potential to test and falsify these landscape evolution hypotheses. Despite this potential, LEMs have mainly been used with hypothetical data and rarely to simulate the evolution of an actual landscape. In this paper, we use a LEM (LAPSUS: LandscApe ProcesS modelling at mUlti dimensions and scaleS) to explore if it is possible to test and falsify conclusions of an earlier field study on 50 ka landscape evolution in Okhombe Valley, KwaZulu Natal, South Africa. In this LEM, five landscape processes interact without supervision: water driven erosion and deposition, creep, solifluction, biological weathering and frost weathering. Calibration matched model results to three types of qualitative fieldwork observations: individual process activity over time, relative process activity over time and net landscape changes over time. Results demonstrate that landscape evolution of the Okhombe valley can be plausibly simulated. A particularly interesting and persistent feature of model results are erosional and depositional phases that lag climatic drivers both by decades, and by several ka within a few hundred meters. The longer lag has not been reported for this spatial scale before and may be an effect of slow landscape-soil-vegetation feedbacks. The combined modelling and fieldwork results allow a more complete understanding of these responses to climate change and can fill in hiatuses in the stratigraphical record. Suggestions are made for methodological adaptations for future LEM studies

LORICA - A new model for linking landscape and soil profile evolution: Development and sensitivity analysis

Soils and landscapes evolve in tandem. Landscape position is a strong determinant of vertical soil development, which has often been formalized in the catena concept. At the same time, soil properties are strong determinants of geomorphic processes such as overland erosion, landsliding and creep. We present a new soilscape evolution model; LORICA, to study these numerous interactions between soil and landscape development. The model is based on the existing landscape evolution model LAPSUS and the soil formation model MILESD. The model includes similar soil formation processes as MILESD, but the main novelties include the consideration of more layers and the dynamic adaption of the number of layers as a function of the soil profile's heterogeneity. New processes in the landscape evolution component include a negative feedback of vegetation and armouring and particle size selectivity of the erosion-deposition process. In order to quantify these different interactions, we present a full sensitivity analysis of the input parameters. First results show that the model successfully simulates various soil-landscape interactions, leading to outputs where the surface changes in the landscape clearly depend on soil development, and soil changes depend on landscape location. Sensitivity analysis of the model confirms that soil and landscape interact: variables controlling amount and position of fine clay have the largest effect on erosion, and erosion variables control among others the amount of chemical weathering. These results show the importance of particle size distribution, and especially processes controlling the presence of finer clay particles that are easily eroded, both for the resulting landscape form as for the resulting soil profiles. Further research will have to show whether this is specific to the boundary conditions of this study or a general phenomenon. © 2015 Elsevier Ltd

Pro-glacial soil variability and geomorphic activity - the case of three Swiss valleys

Soils in pro-glacial areas are often approached from a chronosequence viewpoint. In the chronosequence approach, the objective is to derive rates of soil formation from differences in properties between soils of different age. For this reason, in chronosequence studies, soils are sampled in locations that are assumed geomorphically stable and that have different age. As a result, these studies do not necessarily yield a complete view of soil variability in pro-glacial areas, and may miss important relations between geomorphology and soil development. In this contribution, we present new soil observations from three closely related pro-glacial areas in Switzerland. These observations were intended to get closer to a complete view of soil variability, and to assess impacts from factors other than time on soil development. About 40 soils were visited in each pro-glacial valley in a combined design-convenience sampling scheme and described in the field. Linear modelling was used to assess effects of time and topographic factors on soil properties. The time since glacial retreat turned out to rarely explain more than half of the variation in soil properties, and a linear model combining effects of time and topographic variables explained typically about half of the variation in each pro-glacial valley. Models differed and were not transferable between valleys. Apparently, time and the present-day shape of the landscape combined are insufficient information to accurately predict soil properties. Field evidence points to the importance of the geomorphic history and regime of the valleys as a reason for this. Copyright (C) 2014 John Wiley & Sons, Ltd