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Field courses are essential for subjects like Earth Sciences, Geography and Ecology. In these topics, GIS is used to manage and analyse spatial data, and offers quantitative methods that are beneficial for fieldwork. This paper presents changes made to a first-year Earth Sciences field course in the French Alps, where new GIS methods were introduced. Students use GIS in preparation to explore their research area using an elevation model and satellite images, formulate hypotheses and plan the fieldwork. During the fieldwork, a pilot group managed their field-observations using GIS and made digital maps. Students praise the use of quantitative digital maps in the preparation. Students made use of the available techniques during the fieldwork, although this could be further intensified. Some students were extra motivated due to the technical nature as well as the additional analytical possibilities. The use of GIS was experienced as a steep learning curve by students, and not all staff members are confident in supervising students using GIS, which calls for a sufficient preparation and training of both students and staff. The use of GIS adds abstract analyses and quantitative assessment, which is a complementary learning style to fieldwork that mostly focuses on practical skills.

Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high‐resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high‐accuracy and resolution (5 m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R2, +17% Relative Contribution) and over the set of simpler predictors (+18% R2, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated. Plain Language Summary: In this study, we show that surface water maps derived from satellite imagery can be used to create variables that predict the incidence of malaria equally well as precipitation, which is currently one of the best performing predictors of malaria. The condition for surface water to perform well as predictor, is that the surface water maps from which they are created are of sufficiently high detail and that they mimic the processes of how malaria spreads. Knowing this, more effort can be put into the development of better representations of surface water in predictors and frequently updated detailed surface water maps for regions with high rates of malaria. This will help the prediction of malaria, which is important to stop its spread. Key Points: Surface water predictors can be equally strong or better predictors for malaria compared to precipitationSurface water predictors predict malaria better if derived from higher resolution mapsSurface water predictors become stronger if they resemble the processes of malaria dispersion more closely [ABSTRACT FROM AUTHOR]

Disturbance-dependent grasslands, often associated with hydromorphological and fire dynamics, are threatened, especially in subtropical climates. In the Nepalese and Indian Terai Arc Landscape at the foot of the Himalayas, natural and cultural grasslands serve a viable role for greater one-horned rhinoceros (Rhinoceros unicornis) and for grazers that form prey of the Royal Bengal tiger (Panthera tigris). The grasslands are vulnerable to encroachment of forest. We aimed to establish the effects of environmental drivers, in particular river discharge, river channel dynamics, precipitation and forest fires, on the spatio-temporal dynamics of these grasslands. The study area is the floodplain of the eastern branch of the Karnali River and adjacent western part of Bardia National Park. We created annual time series (1993–2019) of land cover with the use of field data, remotely sensed LANDSAT imagery and a supervised classification model. Additionally, we analysed the pattern of grassland patches and aerial photographs of 1964. Between 1964 and 2019, grassland patches decreased in abundance and size due to encroachment of forest. Outside the floodplain, conversion of grassland to bare substrate coincides with extreme precipitation events. Within the floodplain, conversion of grassland to bare substrate correlates with the magnitude of the annual peak discharge of the bifurcated Karnali River. Since 2009, however, this correlation is absent due to a shift of the main discharge channel to the western branch of the Karnali River. Consequently, alluvial tall grasslands (Saccharum spontaneum dominant) have vastly expanded between 2009 and 2019. Because the hydromorphological processes in the floodplain have become more static, other sources of disturbances – local flooding of ephemeral streams, anthropogenic maintenance, grazing and fires – are more paramount to prevent encroachment of grasslands. Altogether, our findings underscore that a change in the environmental drivers impact the surface area and heterogeneity of grassland patches in the landscape, which can lead to cascading effects for the grassland-dependent megafauna. [ABSTRACT FROM AUTHOR]

Objective: Environmental factors that drive obesity are often studied individually, whereas obesogenic environments are likely to consist of multiple factors from food and physical activity (PA) environments. This study aimed to compose and describe a comprehensive, theory‐based, expert‐informed index to quantify obesogenicity for all neighborhoods in the Netherlands. Methods: The Obesogenic Built Environment CharacterisTics (OBCT) index consists of 17 components. The index was calculated as an average of componential scores across both food and PA environments and was scaled from 0 to 100. The index was visualized and summarized with sensitivity analysis for weighting methods. Results: The OBCT index for all 12,821 neighborhoods was right‐skewed, with a median of 44.6 (IQR = 10.1). Obesogenicity was lower in more urbanized neighborhoods except for the extremely urbanized neighborhoods (>2500 addresses/km2), where obesogenicity was highest. The overall OBCT index score was moderately correlated with the food environment (Spearman ρ = 0.55, p <0.05) and with the PA environment (ρ = 0.38, p <0.05). Hierarchical weighting increased index correlations with the PA environment but decreased correlations with the food environment. Conclusions: The novel OBCT index and its comprehensive environmental scores are potentially useful tools to quantify obesogenicity of neighborhoods. [ABSTRACT FROM AUTHOR]

Disturbance-dependent grasslands, often associated with hydromorphological and fire dynamics, are threatened, especially in subtropical climates. In the Nepalese and Indian Terai Arc Landscape at the foot of the Himalayas, natural and cultural grasslands serve a viable role for rhinos (Rhinoceros unicornis) and the prey of the Royal Bengal tiger (Panthera tigris). The grasslands are vulnerable for encroachment of forest. We aimed to establish the effects of environmental drivers, in particular river discharge, river channel dynamics, precipitation, and forest fires, on space-time dynamics of these grasslands. The study area is the floodplain of the eastern branch of the Karnali River and adjacent western part of Bardia National Park. We created two annual time series of land cover with the use of field data, remotely sensed LANDSAT imagery and a supervised classification model. Additionally, we analysed aerial photographs of 1964 and the pattern of grassland patches. From 1964 to 2019, grasslands saw a transition to forest and grassland patches decreased in size and number. Outside the floodplain, successional setbacks of grassland coincide with extreme precipitation events. Within the floodplain, successional setbacks of grassland correlate with the magnitude of the annual peak discharge. However, this relationship is absent after 2009 due to a westward shift of the main discharge channel of the bifurcated Karnali River with a vast expansion of alluvial tall grasslands (Saccharum spontaneum dominant) as consequence. Since 2009, hydromorphological processes in the floodplain have become more static. This is supported by an observed decrease in water coverage (-53%) in the dry season, an absence of successional setbacks, and decreased morphodynamics of river channels. For forest fires, the surface area that annually burns is observed to be more variable in recent years and the maximum extent affected by fires is in an increasing trend. Because the hydromorphological processes in the floodplain have become more static, other sources of disturbances - ephemeral streams, anthropogenic maintenance, grazing and fires - are more paramount to prevent encroachment of grasslands. Altogether, our findings underscore that a change in the environmental drivers impact the surface area and heterogeneity of grassland patches in the landscape, which can lead to cascading effects for the grassland-dependent fauna. [ABSTRACT FROM AUTHOR]

Objective: The aim of the current study was to establish whether the neighbourhood food environment, characterised by the healthiness of food outlets, the diversity of food outlets and fast-food outlet density within a 500 m or 1000 m street network buffer around the home address, contributed to ethnic differences in diet quality.Design: Cross-sectional cohort study.Setting: Amsterdam, the Netherlands.Participants: Data on adult participants of Dutch, South-Asian Surinamese, African Surinamese, Turkish and Moroccan descent (n total 4728) in the HELIUS study were analysed.Results: The neighbourhood food environment of ethnic minority groups living in Amsterdam is less supportive of a healthy diet and of less diversity than that of participants of Dutch origin. For example, participants of Turkish, Moroccan and South-Asian Surinamese descent reside in a neighbourhood with a significantly higher fast-food outlet density (≤1000 m) than participants of Dutch descent. However, we found no evidence that neighbourhood food environment characteristics directly contributed to ethnic differences in diet quality.Conclusion: Although ethnic minority groups lived in less healthy food environments than participants of ethnic Dutch origin, this did not contribute to ethnic differences in diet quality. Future research should investigate other direct or indirect consequences of residing in less supportive food environments and gain a better understanding of how different ethnic groups make use of their neighbourhood food environment. [ABSTRACT FROM AUTHOR]

Formation of alluvial stratigraphy is controlled by autogenic processes that mix their imprints with allogenic forcing. In some alluvial successions, sedimentary cycles have been linked to astronomically‐driven, cyclic climate changes. However, it remains challenging to define how such cyclic allogenic forcing leads to sedimentary cycles when it continuously occurs in concert with autogenic forcing. Accordingly, we evaluate the impact of cyclic and non‐cyclic upstream forcing on alluvial stratigraphy through a process‐based alluvial architecture model, the Karssenberg and Bridge (2008) model (KB08). The KB08 model depicts diffusion‐based sediment transport, erosion and deposition within a network of channel belts and associated floodplains, with river avulsion dependent on lateral floodplain gradient, flood magnitude and frequency, and stochastic components. We find cyclic alluvial stratigraphic patterns to occur when there is cyclicity in the ratio of sediment supply over water discharge (Qs/Qw ratio), in the precondition that the allogenic forcing has sufficiently large amplitudes and long, but not very long, wavelengths, depending on inherent properties of the modelled basin (e.g. basin subsidence, size, and slope). Each alluvial stratigraphic cycle consists of two phases: an aggradation phase characterized by rapid sedimentation due to frequent channel shifting and a non‐deposition phase characterized by channel belt stability and, depending on Qs/Qw amplitudes, incision. Larger Qs/Qw ratio amplitudes contribute to weaker downstream signal shredding by stochastic components in the model. Floodplain topographic differences are found to be compensated by autogenic dynamics at certain compensational timescales in fully autogenic runs, while the presence of allogenic forcing clearly impacts the compensational stacking patterns. [ABSTRACT FROM AUTHOR]

Background: In the past two decades, the built environment emerged as a conceptually important determinant of obesity. As a result, an abundance of studies aiming to link environmental characteristics to weight-related outcomes have been published, and multiple reviews have attempted to summarise these studies under different scopes and domains. We set out to summarise the accumulated evidence across domains by conducting a review of systematic reviews on associations between any aspect of the built environment and overweight or obesity. Methods: Seven databases were searched for eligible publications from the year 2000 onwards. We included systematic literature reviews, meta-analyses and pooled analyses of observational studies in the form of cross-sectional, case–control, longitudinal cohort, ecological, descriptive, intervention studies and natural experiments. We assessed risk of bias and summarised results structured by built environmental themes such as food environment, physical activity environment, urban–rural disparity, socioeconomic status and air pollution. Results: From 1850 initial hits, 32 systematic reviews were included, most of which reported equivocal evidence for associations. For food- and physical activity environments, associations were generally very small or absent, although some characteristics within these domains were consistently associated with weight status such as fast-food exposure, urbanisation, land use mix and urban sprawl. Risks of bias were predominantly high. Conclusions: Thus far, while most studies have not been able to confirm the assumed influence of built environments on weight, there is evidence for some obesogenic environmental characteristics. Registration: This umbrella review was registered on PROSPERO under ID CRD42019135857. [ABSTRACT FROM AUTHOR]

Environmental exposures are increasingly investigated as possible drivers of health behaviours and disease outcomes. So-called exposome studies that aim to identify and better understand the effects of exposures on behaviours and disease risk across the life course require high-quality environmental exposure data. The Netherlands has a great variety of environmental data available, including high spatial and often temporal resolution information on urban infrastructure, physico-chemical exposures, presence and availability of community services, and others. Until recently, these environmental data were scattered and measured at varying spatial scales, impeding linkage to individual-level (cohort) data as they were not operationalised as personal exposures, that is, the exposure to a certain environmental characteristic specific for a person. Within the Geoscience and hEalth Cohort COnsortium (GECCO) and with support of the Global Geo Health Data Center (GGHDC), a platform has been set up in The Netherlands where environmental variables are centralised, operationalised as personal exposures, and used to enrich 23 cohort studies and provided to researchers upon request. We here present and detail a series of personal exposure data sets that are available within GECCO to date, covering personal exposures of all residents of The Netherlands (currently about 17 M) over the full land surface of the country, and discuss challenges and opportunities for its use now and in the near future. [ABSTRACT FROM AUTHOR]

Spatial point tracks are of concern for an increasing number of analysts studying spatial behaviour patterns and environmental effects. Take an epidemiologist studying the behaviour of cyclists and how their health is affected by the city's air quality. The accuracy of such analyses critically depends on the positional accuracy of the tracked points. This poses a serious privacy risk. Tracks easily reveal a person's identity since the places visited function as fingerprints. Current obfuscation-based privacy protection methods, however, mostly rely on point quality reduction, such as spatial cloaking, grid masking or random noise, and thus render an obfuscated track less useful for exposure assessment. We introduce simulated crowding as a point quality preserving obfuscation principle that is based on adding fake points. We suggest two crowding strategies based on extending and masking a track to defend against inference attacks. We test them across various attack strategies and compare them to state-of-the-art obfuscation techniques both in terms of information loss and attack resilience. Results indicate that simulated crowding provides high resilience against home attacks under constantly low information loss. [ABSTRACT FROM AUTHOR]

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github. com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D-2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results. [ABSTRACT FROM AUTHOR]

Canopy nitrogen (N) concentration and content are linked to several vegetation processes. Therefore, canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there are ample C data available to constrain the models, widespread N data are lacking. Remotely sensed vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. Vegetation indices could be a valuable tool to detect canopy N concentration and canopy N content at larger scale. In this paper, we conducted a regional case-study analysis to investigate the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from European Space Agency (ESA) Envisat satellite at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (N gm-2, of ground area) from a Mediterranean forest inventory in the region of Catalonia, in the northeast of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20, 15, 10 and 5 km) and at the original spatial resolution of 1 km. The results at higher spatial resolution (1 km) yielded significant log-linear relationships between MTCI and both canopy N concentration and content: r2 =0.32 and r2 =0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r2 =0.24 and r2 =0.44, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r2 =0.19). At the species level, canopy N concentration was strongly related to MTCI for European beech plots (r2=0.69). These results present a new perspective on the application of MTCI time series for canopy N detection. [ABSTRACT FROM AUTHOR]

The export of wood pellets from the southeastern United States (USA) has grown significantly in recent years, following rising demand from Europe. Increased wood pellet demand could lead to spatially variable changes in timberland management and area in the USA. This study presents an assessment of the impacts of increasing wood pellet demand (an additional 11.6 Mt by 2030) on land-use dynamics, taking into account developments in other wood product markets as well as expected changes in other land uses. An economic model for the forest sector of the southeastern USA (SRTS) was linked to a land-use change model (PLUC) to identify potential locations of land-use change following scenarios of demand for pellets and other wood products. Projections show that in the absence of additional demand for wood pellets, natural timberland area is projected to decline by 450-15 000 km2 by 2030, mainly through urbanization and pine plantation establishment. Under the high wood pellet demand scenario, more (2000-7500 km2) natural timberland area is retained and more (8000-20 000 km2) pine plantation is established. Shifts from natural timberland to pine plantation occur predominantly in the Atlantic coastal region. Future work will assess the impact of projected transitions in natural timberland and pine plantations on biodiversity and carbon storage. This modeling framework can be applied for multiple scenarios and land-use projections to identify locations of timberland area changes for the whole southeastern USA, thereby informing the debate about potential impacts of wood pellet demand on land-use dynamics and environmental services. © 2017 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster-Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arc-minute resolution, but a version parameterized at 30 arc-minute resolution is also available. Both versions are available as open source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model. PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1D-2D-hydrodynamic models, respectively. Here, we describe the main components of the model, compare results of the 30 arcminute and the 5 arc-minute versions and evaluate their model performance using GRDC discharge data. Results show that model performance of the 5 arc-minute version is notably better than that of the 30 arc-minute version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arc-minute model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated water withdrawal, by source and sector, matches reasonably well with reported water withdrawal from AQUASTAT. [ABSTRACT FROM AUTHOR]

Canopy nitrogen (N) concentration and content are linked to several vegetation processes and canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there is ample C data available to constrain the models, widespread N data are lacking. Remote sensing and vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. In this paper we conducted a regional case-study analysis investigating the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from ESA ENVISAT at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (g m-2) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r²â€‰= 0.32 and r²â€‰= 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r²â€‰= 0.25 and r²â€‰= 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r²â€‰= 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r²â€‰= 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. Keywords: vegetation index, MERIS, foliar nitrogen concentration, foliar nitrogen content, plant functional types, Mediterranean forest, remote sensing. [ABSTRACT FROM AUTHOR]

Dynamic environmental models use a state transition function, external inputs and parameters to simulate the change of real-world processes over time. Modellers specify the state transition function and the external inputs required in the process calculation of each time step in a component model, a self-contained numerical module representing an individual spatio-temporal process. Depending on the application case of a component model – such as standalone execution or in an integrated model – the source of the external input needs to be specified. The required external inputs can thereby be obtained by a file operation in case of a standalone execution. Alternatively, required inputs can be obtained from other component models, in case the component model is part of an integrated model. Using different notations to specify these input requirements, however, requires a modification of the state transition function per application case and therefore would reduce the generic applicability of a component model. To address this problem, we propose the function object notation as a means to specify the input requirements of a component model. This function object notation provides modellers with a uniform syntax to express the input requirements within the state transition function. During component initialisation, the function objects can be parameterised with different external sources. In addition to a uniform syntax, the function object notation allows a modeller to specify a request-reply execution flow of the coupled models (i.e. a component requests data needed for its own progress from another component). We extend the request-reply execution approach to Monte Carlo simulations and implement a software framework prototype. Using this prototype, we build an exemplary integrated model by coupling components for land use change, hydrology and Eucalyptus tree growth at different temporal discretisations to obtain the probability for bioenergy plant growing in a hypothetical catchment. The presented approach allows modellers to specify input requirements in the state transition function independently from the source of external inputs and therefore increases the reusability of these component models. [ABSTRACT FROM AUTHOR]

A spatially explicit land use change model is typically based on the assumption that the relationship between land use change and its explanatory processes is stationary. This means that model structure and parameterization are usually kept constant over the model runtime, ignoring potential systemic changes in this relationship resulting from societal changes. We have developed a methodology to test for systemic changes and demonstrate it by assessing whether or not a land use change model with a constant model structure is an adequate representation of the land use system given a time series of observations of past land use. This was done by assimilating observations of real land use into a land use change model, using a Bayesian data assimilation technique, the particle filter. The particle filter was used to update the prior knowledge about the model structure, i.e. the selection and relative importance of the explanatory processes for land use change allocation, and about the parameters. For each point in time for which observations were available the optimal model structure and parameterization were determined. In a case study of sugar cane expansion in Brazil, it was found that the assumption of a constant model structure was not fully adequate, indicating systemic change in the modelling period (2003–2012). The systemic change appeared to be indirect: a factor has an effect on the demand for sugar cane, an input variable, in such a way that the transition rules and parameters have to change as well. Although an inventory was made of societal changes in the study area during the studied period, none of them could be directly related to the onset of the observed systemic change in the land use system. Our method which allows for systemic changes in the model structure resulted in an average increase in the 95% confidence interval of the projected sugar cane fractions of a factor of two compared to the assumption of a stationary system. This shows the importance of taking into account systemic changes in projections of land use change in order not to underestimate the uncertainty of future projections. [ABSTRACT FROM AUTHOR]

Model studies suggest that semiarid ecosystems with patterned vegetation can respond in a nonlinear way to climate change. This means that gradual changes can result in a rapid transition to a desertified state. Previous model studies focused on the response of patterned semiarid ecosystems to changes in mean annual rainfall. The intensity of rain events, however, is projected to change as well in the coming decades. In this paper, we study the effect of changes in rainfall intensity on the functioning of patterned semiarid ecosystems with a spatially explicit model that captures rainwater partitioning and runoff-runon processes with simple event-based process descriptions. Analytical and numerical analyses of the model revealed that rainfall intensity is a key parameter in explaining patterning of vegetation in semiarid ecosystems as low mean rainfall intensities do not allow for vegetation patterning to occur. Surprisingly, we found that, for a constant annual rainfall rate, both an increase and a decrease in mean rainfall intensity can trigger desertification. An increase negatively affects productivity as a greater fraction of the rainwater is lost as runoff. This can result in a shift to a bare desert state only if the mean rainfall intensity exceeds the infiltration capacity of bare soil. On the other hand, a decrease in mean rainfall intensity leads to an increased fraction of rainwater infiltrating in bare soils, remaining unavailable to plants. Our findings suggest that considering rainfall intensity as a variable may help in assessing the proximity to regime shifts in patterned semiarid ecosystems and that monitoring losses of resource through runoff and bare soil infiltration could be used to determine ecosystem resilience. [ABSTRACT FROM AUTHOR]