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Understanding long-term soil and landscape evolution can help us understand the threats to current-day soils, landscapes and their functions. The temporal evolution of soils and landscapes can be studied using geochronometers, such as optically stimulated luminescence (OSL) particle ages or radionuclide inventories. Also, soil–landscape evolution models (SLEMs) can be used to study the spatial and temporal evolution of soils and landscapes through numerical modelling of the processes responsible for the evolution. SLEMs and geochronometers have been combined in the past, but often these couplings focus on a single geochronometer, are designed for specific idealized landscape positions, or do not consider multiple transport processes or post-depositional mixing processes that can disturb the geochronometers in sedimentary archives. We present ChronoLorica, a coupling of the soil–landscape evolution model Lorica with a geochronological module. The module traces spatiotemporal patterns of particle ages, analogous to OSL ages, and radionuclide inventories during the simulations of soil and landscape evolution. The geochronological module opens rich possibilities for data-based calibration of simulated model processes, which include natural processes, such as bioturbation and soil creep, as well as anthropogenic processes, such as tillage. Moreover, ChronoLorica can be applied to transient landscapes that are subject to complex, non-linear boundary conditions, such as land use intensification, and processes of post-depositional disturbance which often result in complex geo-archives. In this contribution, we illustrate the model functionality and applicability by simulating soil and landscape evolution along a two-dimensional hillslope. We show how the model simulates the development of the following three geochronometers: OSL particle ages, meteoric 10Be inventories and in situ 10Be inventories. The results are compared with field observations from comparable landscapes. We also discuss the limitations of the model and highlight its potential applications in pedogenical, geomorphological or geological studies.

Soils and landscapes can show complex, non-linear evolution, especially under changing climate or land use. Soil-landscape evolution models (SLEMs) are increasingly equipped to simulate the development of soils and landscapes over long timescales under these changing drivers, but provide large data output that can be difficult to interpret and communicate. New tools are required to analyse and communicate large model output. In this work, I show how spatial and temporal trends in previously published model results can be summarized and conceptualized with evolutionary pathways, which are possible trajectories of the development of soil patterns. Simulated differences in rainfall and land use control progressive or regressive soil development and convergence or divergence of the soil pattern. These changes are illustrated with real-world examples of soil development and soil complexity. The use of evolutionary pathways for analysing the results of SLEMs is not limited to the examples in this paper, but they can be used on a wide variety of soil properties, soil pattern statistics and models. With that, evolutionary pathways provide a promising tool to analyse and communicate soil model output, not only for studying past changes in soils, but also for evaluating future spatial and temporal effects of soil management practices in the context of sustainability.

Understanding long-term soil and landscape evolution can help us understand the threats to current-day soils, landscapes and their functions. The temporal evolution of soils and landscapes can be studied using geochronometers, such as optically stimulated luminescence (OSL) particle ages or radionuclide inventories. Also, soil–landscape evolution models (SLEMs) can be used to study the spatial and temporal evolution of soils and landscapes through numerical modelling of the processes responsible for the evolution. SLEMs and geochronometers have been combined in the past, but often these couplings focus on a single geochronometer, are designed for specific idealized landscape positions, or do not consider multiple transport processes or post-depositional mixing processes that can disturb the geochronometers in sedimentary archives. We present ChronoLorica, a coupling of the soil–landscape evolution model Lorica with a geochronological module. The module traces spatiotemporal patterns of particle ages, analogous to OSL ages, and radionuclide inventories during the simulations of soil and landscape evolution. The geochronological module opens rich possibilities for data-based calibration of simulated model processes, which include natural processes, such as bioturbation and soil creep, as well as anthropogenic processes, such as tillage. Moreover, ChronoLorica can be applied to transient landscapes that are subject to complex, non-linear boundary conditions, such as land use intensification, and processes of post-depositional disturbance which often result in complex geo-archives. In this contribution, we illustrate the model functionality and applicability by simulating soil and landscape evolution along a two-dimensional hillslope. We show how the model simulates the development of the following three geochronometers: OSL particle ages, meteoric 10Be inventories and in situ 10Be inventories. The results are compared with field observations from comparable landscapes. We also discuss the limitations of the model and highlight its potential applications in pedogenical, geomorphological or geological studies.

Geomorphological maps provide information on the relief, genesis and shape of the earth's surface and are widely used in sustainable spatial developments. The quality of geomorphological maps is however rarely assessed or reported, which limits their applicability. Moreover, older geomorphological maps often do not meet current quality requirements and require updating. This updating is time-consuming and because of its qualitative nature difficult to reproduce, but can be supported by novel computational methods. In this paper, we address these issues by (1) quantifying the uncertainty associated with manual geomorphological mapping, (2) exploring the use of convolutional neural networks (CNNs) for semi-automated geomorphological mapping and (3) testing the sensitivity of CNNs to uncertainties in manually created evaluation data. We selected a test area in the Dutch push-moraine district with a pronounced relief and a high variety of landforms. For this test area we developed five manually created geomorphological maps and 27 automatically created landform maps using CNNs. The resulting manual maps are similar on a regional level. We could identify the causes of disagreement between the maps on a local level, which often related to differences in mapping experience, choices in delineation and different interpretations of the legend. Coordination of mapping efforts and field validation are necessary to create accurate and precise maps. CNNs perform well in identifying landforms and geomorphological units, but fail at correct delineation. The human geomorphologist remains necessary to correct the delineation and classification of the computed maps. The uncertainty in the manually created data that are used to train and evaluate CNNs have a large effect on the model performance and evaluation. This also advocates for coordinated mapping efforts to ensure the quality of manually created training and test data. Further model development and data processing are required before CNNs can act as standalone mapping techniques.

We present a set of new geomorphometric variables that express landscape position relative to breaklines in hillslopes, and test whether these variables are of value in explaining soil property variation in three study sites in the United States, the Netherlands, and Spain. Underlying this work is the recognition that slope breaks, such as cliff lines, lynchets, and large slumped blocks, are associated with processes that affect soil formation around them. For each study site, we digitized slope breaks and calculated vertical and horizontal distance to the nearest lower and higher slope breaks, as well as the relative position between multiple slope breaks, the slope increase at the next higher slope break, and the slope decrease at the next lower slope break. We then assessed the value of these geomorphometric variables in the prediction of slope properties by simple linear regression. At each study site, models were fitted to existing soil observations using a traditional set of geomorphometric variables, and the traditional set plus our newly developed variables. Model comparison indicated that the new variables substantially improved model fit and reduced model error for the site in the United States (Kansas, n = 100), improved model fit but did not reduce model error for the site in the Netherlands (Limburg, n = 192), and did not solve model overfitting issues for the small dataset in Spain (Malaga, n = 66).

Purpose Spatial and temporal patterns of past erosional events are a useful and needed information to explain observed soil patterns in different landscapes. Soil thickness reflects the overall expression of pedogenesis and erosion. Forested soils of Northern Germany exhibit varying soil thicknesses with thin soils on crest positions and buried soils at the footslope. The aim of this study is to reconstruct the complex soil mass redistribution and soil patterns of this forested area due to different periods of erosion and stability. Methods We explored the explanatory power of both 10Be (in situ and meteoric) on a hillslope and we 14C-dated buried horizons at different depths. Results The 10Be depth profiles did not show an exponential decrease with depth. They had a ‘bulge’ shape indicating clay translocation and interaction with oxyhydroxydes (meteoric 10Be), bioturbation and soil mass redistribution (in situ 10Be). The combined application of both 10Be and 14C dating revealed progressive and regressive phases of soil evolution. Although Melzower Forest is protected (same vegetation) since the past 250 years, both 10Be clearly indicated major soil mass redistribution along the investigated catena. Conclusion A strong erosion impulse must have occurred between 4.5 and 6.8 kyr BP indicating an earlier human impact on soil erosion than previously postulated (~ 3 kyr earlier). Our findings correlate in fact with the first settlements reported for this region (~ 6.8 kyr BP) and show their immediate effect on soils. The overall soil redistribution rates in this forest are surprisingly similar to those obtained from a nearby agricultural area., Journal of Soils and Sediments, 21 (12), ISSN:1439-0108, ISSN:1614-7480

Humans have substantially altered soil and landscape patterns and properties due to agricultural use, with severe impacts on biodiversity, carbon sequestration and food security. These impacts are difficult to quantify, because we lack data on long-term changes in soils in natural and agricultural settings and available simulation methods are not suitable to reliably predict future development of soils under projected changes in climate and land management. To help overcome these challenges, we developed the HydroLorica soil-landscape evolution model, that simulates soil development by explicitly modelling the spatial water balance as driver of soil and landscape forming processes. We simulated 14500 years of soil – formation under natural conditions for three scenarios of different rainfall inputs. For each scenario we added a 500-year period of intensive agricultural land use, where we introduced tillage erosion and changed vegetation type. Our results show substantial differences between natural soil patterns under different rainfall input. With higher rainfall, soil patterns become more heterogeneous due to increased tree throw and water erosion. Agricultural patterns differ substantially from the natural patterns, with higher variation of soil properties over larger distances and larger correlations with terrain position. In the natural system, rainfall is the dominant factor influencing soil variation, while for agricultural soil patterns landform explains most of the variation simulated. The cultivation of soils thus changed the dominant factors and processes influencing soil formation, and thereby also increased predictability of soil patterns. Our study highlights the potential of soil-landscape evolution modelling for simulating past and future developments of soil and landscape patterns. Our results confirm that humans have become the dominant soil forming factor in agricultural landscapes.

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity.

An effective vaccine is available for the hepatitis B virus (HBV), which is a very contagious human pathogen. The prevalence of chronic HBV infection is very low in the Netherlands (

Nonspatial theory on pathogen evolution generally predicts selection for maximal number of secondary infections, constrained only by supposed physiological trade-offs between pathogen infectiousness and virulence. Spread of diseases in human populations can, however, exhibit large scale patterns, underlining the need for spatially explicit approaches to pathogen evolution. Here, we show, in a spatial model where all pathogen traits are allowed to evolve independently, that evolutionary trajectories follow a single relationship between transmission and clearance. This trade-off relation is an emergent system property, as opposed to being a property of pathogen physiology, and maximizes outbreak frequency instead of the number of secondary infections. We conclude that spatial pattern formation in contact networks can act to link infectiousness and clearance during pathogen evolution in the absence of any physiological trade-off. Selection for outbreak frequency offers an explanation for the evolution of pathogens that cause mild but frequent infections.

The effect of vaccination programs on transmission of infectious disease is usually assessed by monitoring programs that rely on notifications of symptomatic illness. For monitoring of infectious diseases with a high proportion of asymptomatic cases or a low reporting rate, molecular sequence data combined with modern coalescent-based techniques offer a complementary tool to assess transmission. Here, the authors investigate the added value of using viral sequence data to monitor a vaccination program that was started in 1998 and was targeted against hepatitis B virus in men who have sex with men in Amsterdam, the Netherlands. The incidence in this target group, as estimated from the notifications of acute infections with hepatitis B virus, was low; therefore, there was insufficient power to show a significant change in incidence. In contrast, the genetic diversity, as estimated from the viral sequence collected from the target group, revealed a marked decrease after vaccination was introduced. Taken together, the findings suggest that introduction of vaccination coincided with a change in the target group toward behavior with a higher risk of infection. The authors argue that molecular sequence data provide a powerful additional monitoring instrument, next to conventional case registration, for assessing the impact of vaccination.