A review of applied methods in Europe for flood-frequency analysis in a changing environment

The report presents a review of methods used in Europe for trend analysis, climate change projections and non-stationary analysis of extreme precipitation and flood frequency. In addition, main findings of the analyses are presented, including a comparison of trend analysis results and climate change projections. Existing guidelines in Europe on design flood and design rainfall estimation that incorporate climate change are reviewed. The report concludes with a discussion of research needs on non-stationary frequency analysis for considering the effects of climate change and inclusion in design guidelines. Trend analyses are reported for 21 countries in Europe with results for extreme precipitation, extreme streamflow or both. A large number of national and regional trend studies have been carried out. Most studies are based on statistical methods applied to individual time series of extreme precipitation or extreme streamflow using the non-parametric Mann-Kendall trend test or regression analysis. Some studies have been reported that use field significance or regional consistency tests to analyse trends over larger areas. Some of the studies also include analysis of trend attribution. The studies reviewed indicate that there is some evidence of a general increase in extreme precipitation, whereas there are no clear indications of significant increasing trends at regional or national level of extreme streamflow. For some smaller regions increases in extreme streamflow are reported. Several studies from regions dominated by snowmelt-induced peak flows report decreases in extreme streamflow and earlier spring snowmelt peak flows. Climate change projections have been reported for 14 countries in Europe with results for extreme precipitation, extreme streamflow or both. The review shows various approaches for producing climate projections of extreme precipitation and flood frequency based on alternative climate forcing scenarios, climate projections from available global and regional climate models, methods for statistical downscaling and bias correction, and alternative hydrological models. A large number of the reported studies are based on an ensemble modelling approach that use several climate forcing scenarios and climate model projections in order to address the uncertainty on the projections of extreme precipitation and flood frequency. Some studies also include alternative statistical downscaling and bias correction methods and hydrological modelling approaches. Most studies reviewed indicate an increase in extreme precipitation under a future climate, which is consistent with the observed trend of extreme precipitation. Hydrological projections of peak flows and flood frequency show both positive and negative changes. Large increases in peak flows are reported for some catchments with rainfall-dominated peak flows, whereas a general decrease in flood magnitude and earlier spring floods are reported for catchments with snowmelt-dominated peak flows. The latter is consistent with the observed trends. The review of existing guidelines in Europe on design floods and design rainfalls shows that only few countries explicitly address climate change. These design guidelines are based on climate change adjustment factors to be applied to current design estimates and may depend on design return period and projection horizon. The review indicates a gap between the need for considering climate change impacts in design and actual published guidelines that incorporate climate change in extreme precipitation and flood frequency. Most of the studies reported are based on frequency analysis assuming stationary conditions in a certain time window (typically 30 years) representing current and future climate. There is a need for developing more consistent non-stationary frequency analysis methods that can account for the transient nature of a changing climate

Sensitivity of discharge and flood frequency to twenty-first century and late Holocene changes in climate and land use (River Meuse, northwest Europe)

We used a calibrated coupled climate–hydrological model to simulate Meuse discharge over the late Holocene (4000–3000 BP and 1000–2000 AD). We then used this model to simulate discharge in the twenty-first century under SRES emission scenarios A2 and B1, with and without future land use change. Mean discharge and medium-sized high-flow (e.g. Q99) frequency are higher in 1000–2000 AD than in 4000–3000 BP; almost all of this increase can be attributed to the conversion of forest to agriculture. In the twentieth century, mean discharge and the frequency of medium-sized high-flow events are higher than in the nineteenth century; this increase can be attributed to increased (winter half-year) precipitation. Between the twentieth and twenty-first centuries, anthropogenic climate change causes a further increase in discharge and medium-sized high-flow frequency; this increase is of a similar order of magnitude to the changes over the last 4,000 years. The magnitude of extreme flood events (return period 1,250-years) is higher in the twenty-first century than in any preceding period of the time-slices studied. In contrast to the long-term influence of deforestation on mean discharge, changes in forest cover have had little effect on these extreme floods, even on the millennial timescale

Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale

Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes

Integrating spatial and temporal approaches for explaining bicycle crashes in high-risk areas in Antwerp (Belgium)

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle-motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle-motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads

Spatio-temporal modeling of soil characteristics for soilscape reconstruction

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Understanding drivers of species distribution change: a trait-based approach

The impacts of anthropogenic environmental change on biodiversity are well documented, with threats such as habitat loss and climate change identified as causes of change in species distributions. The high degree of variation in responses of species to environmental change can be partly explained through comparative analyses of species traits. I carried out a phylogenetically informed trait-based analysis of plant range change in Britain, discovering that traits associated with competitive ability and habitat specialism both explained variation in range changes. Competitive, habitat generalists out-perform ed species specialised to nutrient-poor conditions; a result which can be attributed to the impact of agricultural intensification in Britain. A limitation of the comparative approach is that the models do not directly test the impact of environmental change on species distribution patterns, but instead infer potential impacts. I tested the potential of comparative analyses from a spatial context by conducting a spatial analysis of plant distribution change in Britain, examining the direct impact of environmental change on the spatial distribution of the trait characteristics of species that have gone locally extinct. I discovered a loss of species associated with nitrogen poor soils in regions that had an increase in arable land cover, a result that supports the results from the trait-based analysis of plant range change and demonstrates that comparative studies can accurately infer drivers of distribution change. I found that the cross-region transferability of trait-based models of range change to be related to land cover similarity, highlighting that the trait-based approach is dependent on a regional context. Additionally, I discovered that traits derived from distribution data were significant predictors of range shift across many taxonomic groups, out-performing traditional life history traits. This thesis highlights the potential of the data accumulated through the increased public participation in biological recording to address previously unanswerable ecological research questions.Open Acces

Assessing spatial uncertainties of land allocation using the scenario approach and sensitivity analysis

The paper assess uncertainty of future spatial allocation of agricultural land in Europe. To assess the possible future development of agricultural production and land for the period 2000 – 2030, two contrasting scenarios are constructed. The scenarios storylines lead to different measurable assumptions concerning scenario specific drivers (variables) and parameters. Many of them are estimations and thus include a certain level of uncertainty regarding their true values. This leads to uncertainty of the scenario outcomes. In this study we use sensitivity analysis to estimate the uncertainty of agricultural land use.spatial uncertainty, scenario approach, sensitivity analysis., Agribusiness, Agricultural and Food Policy, Community/Rural/Urban Development, Food Consumption/Nutrition/Food Safety, Labor and Human Capital,

Empirically Derived Suitability Maps to Downscale Aggregated Land Use Data

Understanding mechanisms that drive present land use patterns is essential in order to derive appropriate models of land use change. When static analyses of land use drivers are performed, they rarely explicitly deal with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional, logistic analysis of land use drivers in Belgium. It is shown that purely regressive logistic models can only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best model of land use distribution, a purely autoregressive (or neighbourhood-based) model is appropriate. Moreover, it is also concluded that a neighbourhood based only on the 8 surrounding cells leads to the best logistic regression models at this scale of observation. This statement is valid for each land use type studied – i.e. built-up, forests, cropland and grassland.