Juha Aalto

Rainfall erosivity in Europe

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.

Soil moisture's underestimated role in climate change impact modelling in low‐energy systems

Shifts in precipitation regimes are an inherent component of climate change, but in low-energy systems are often assumed to be less important than changes in temperature. Because soil moisture is the hydrological variable most proximally linked to plant performance during the growing season in arctic-alpine habitats, it may offer the most useful perspective on the influence of changes in precipitation on vegetation. Here we quantify the influence of soil moisture for multiple vegetation properties at fine spatial scales, to determine the potential importance of soil moisture under changing climatic conditions. A fine-scale data set, comprising vascular species cover and field-quantified ecologically relevant environmental parameters, was analysed to determine the influence of soil moisture relative to other key abiotic predictors. Soil moisture was strongly related to community composition, species richness and the occurrence patterns of individual species, having a similar or greater influence than soil temperature, pH and solar radiation. Soil moisture varied considerably over short distances, and this fine-scale heterogeneity may contribute to offsetting the ecological impacts of changes in precipitation for species not limited to extreme soil moisture conditions. In conclusion, soil moisture is a key driver of vegetation properties, both at the species and community level, even in this low-energy system. Soil moisture conditions represent an important mechanism through which changing climatic conditions impact vegetation, and advancing our predictive capability will therefore require a better understanding of how soil moisture mediates the effects of climate change on biota.

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

New gridded daily climatology of Finland: Permutation‐based uncertainty estimates and temporal trends in climate

Long-term time series of key climate variables with a relevant spatiotemporal resolution are essential for environmental science. Moreover, such spatially continuous data, based on weather observations, are commonly used in, e.g., downscaling and bias correcting of climate model simulations. Here we conducted a comprehensive spatial interpolation scheme where seven climate variables (daily mean, maximum, and minimum surface air temperatures, daily precipitation sum, relative humidity, sea level air pressure, and snow depth) were interpolated over Finland at the spatial resolution of 10 × 10 km2. More precisely, (1) we produced daily gridded time series (FMI_ClimGrid) of the variables covering the period of 1961–2010, with a special focus on evaluation and permutation-based uncertainty estimates, and (2) we investigated temporal trends in the climate variables based on the gridded data. National climate station observations were supplemented by records from the surrounding countries, and kriging interpolation was applied to account for topography and water bodies. For daily precipitation sum and snow depth, a two-stage interpolation with a binary classifier was deployed for an accurate delineation of areas with no precipitation or snow. A robust cross-validation indicated a good agreement between the observed and interpolated values especially for the temperature variables and air pressure, although the effect of seasons was evident. Permutation-based analysis suggested increased uncertainty toward northern areas, thus identifying regions with suboptimal station density. Finally, several variables had a statistically significant trend indicating a clear but locally varying signal of climate change during the last five decades.

Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures

The Brown-Resnick max-stable process has proven to be well-suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniques. In this paper we exploit a case in which the full likelihood of a Brown-Resnick process can be calculated, using componentwise maxima and their partitions in terms of individual events, and we propose two new approaches to inference. The first estimates the partitions using declustering, while the second uses random partitions in a Markov chain Monte Carlo algorithm. We use these approaches to construct a Bayesian hierarchical model for extreme low temperatures in northern Fennoscandia.

The meso-scale drivers of temperature extremes in high-latitude Fennoscandia

Extreme temperatures are key drivers controlling both biotic and abiotic processes, and may be strongly modified by topography and land cover. We modelled mean and extreme temperatures in northern Fennoscandia by combining digital elevation and land cover data with climate observations from northern Finland, Norway and Sweden. Multivariate partitioning technique was utilized to investigate the relative importance of environmental variables for the variation of the three temperature parameters: mean annual absolute minima and maxima, and mean annual temperature. Generalized additive modeling showed good performance, explaining 84–95 % of the temperature variation. The inclusion of remotely sensed variables improved significantly the modelling of thermal extremes in this system. The water cover variables and topography were the most important drivers of minimum temperatures, whereas elevation was the most important factor controlling maximum temperatures. The spatial variability of mean temperatures was clearly driven by geographical location and the effects of topography. Partitioning technique gave novel insights into temperature-environment relationship at the meso-scale and thus proved to be useful tool for the study of the extreme temperatures in the high-latitude setting.

Vegetation Mediates Soil Temperature and Moisture in Arctic-Alpine Environments

Abstract Soil temperature and moisture are key determinants of abiotic and biotic processes in arctic-alpine regions. They are important links to understanding complex ecosystem dynamics under changing climate. The aims of this study were to (1) quantify fine-scale soil temperature and soil moisture variation, and (2) assess the influence of vegetation on soil temperature and moisture patterns in a northern European arctic-alpine environment. Inclusion of vegetation variables significantly improved models of soil temperature and moisture, despite abiotic variables (local topography and soil properties) being the most influential predictors. Temperature varied by ≥5 °C and moisture by ≥50% (volumetric water content) over very short distances (≥ 1 m), reflecting the extreme spatial heterogeneity of thermal and hydrological conditions in these systems. These results thus highlight the biotic mediation of changes in abiotic conditions, showing how vegetation can strongly affect local habitat conditions at fine spatial scales in arctic-alpine environments.

Potential for extreme loss in high-latitude Earth surface processes due to climate change

Climatically driven Earth surface processes (ESPs) govern landscape and ecosystem dynamics in high-latitude regions. However, climate change is expected to alter ESP activity at yet uncertain rate and amplitude. We examined the sensitivity of key ESPs (cryoturbation, solifluction, nivation, and palsa mires) to changing climate by modeling their distribution in regard to climate, local topography, and soil variables in northern Fennoscandia. The distributions of ESPs were then forecasted under two future time periods, 2040–2069 and 2070–2099, using ensemble modeling and three emission scenarios. Increase of 2°C in current temperature conditions caused an almost complete loss of ESPs, highlighting the extreme climatic sensitivity of high-latitude geomorphic processes. Forecasts based on three scenarios suggest a disappearance of suitable climate for studied ESPs by the end of this century. This could initiate multiple opposing feedback between land surface and atmosphere through changes in albedo, heat fluxes, and biogeochemical cycles.

Statistical Forecasting of Current and Future Circum‐Arctic Ground Temperatures and Active Layer Thickness

Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 10 km. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedback. Plain Language Summary Modeling of circum-Arctic ground thermal regime is critical to better predict the local climate change impacts on Arctic ecosystems and societies, thus supporting effective mitigation strategies. In this study we present a new approach to create ground temperature and active layer thickness (i.e. seasonally thawed ground layer on the top of permafrost) data layers over northern hemisphere at unprecedented fine spatial resolution (ca. 1km), that is unreachable with contemporary models. Our data indicate currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 10 km. However, this extent is likely to dramatically contract in the future, as our results suggest consistent, but region-specific, alterations in ground thermal conditions due to climate change. Our results provide new opportunities to estimate future changes in Arctic ground thermal state, which has important implications on greenhouse gas fluxes and infrastructure hazards due to permafrost degradation.

Statistical modelling predicts almost complete loss of major periglacial processes in Northern Europe by 2100

The periglacial realm is a major part of the cryosphere, covering a quarter of Earth’s land surface. Cryogenic land surface processes (LSPs) control landscape development, ecosystem functioning and climate through biogeochemical feedbacks, but their response to contemporary climate change is unclear. Here, by statistically modelling the current and future distributions of four major LSPs unique to periglacial regions at fine scale, we show fundamental changes in the periglacial climate realm are inevitable with future climate change. Even with the most optimistic CO2 emissions scenario (Representative Concentration Pathway (RCP) 2.6) we predict a 72% reduction in the current periglacial climate realm by 2050 in our climatically sensitive northern Europe study area. These impacts are projected to be especially severe in high-latitude continental interiors. We further predict that by the end of the twenty-first century active periglacial LSPs will exist only at high elevations. These results forecast a future tipping point in the operation of cold-region LSP, and predict fundamental landscape-level modifications in ground conditions and related atmospheric feedbacks.Cryogenic land surface processes characterise the periglacial realm and control landscape development and ecosystem functioning. Here, via statistical modelling, the authors predict a 72% reduction of the periglacial realm in Northern Europe by 2050, and almost complete disappearance by 2100.