Geospatial data on the sediments of Lake Balaton.

Freshwater lakes in most inhabited areas of the world are threatened by water quality issues. Standard water conservation measures have shown efficiency in the past; however, polluted lakes have only partially recovered from eutrophication. Our knowledge is still incomplete about the sensitivity of these lakes to different anthropogenic sources and to the changes in their internal processes due to global warming. In this article, we present a database comprising sediment data from Lake Balaton (Hungary), which can facilitate further analysis helping to better understand the internal processes and changes occurring in the lake. The published dataset includes the following parameters measured in 4211 lake bed sediment samples: pHKCl, calcium carbonate (CaCO3), organic carbon (Corg), total nitrogen (Ntotal), soluble phosphorus (AL-P2O5) and soluble potassium (AL-K2O), magnesium (Mg2+), zinc (Zn2+), copper (Cu2+) and manganese (Mn2+). We are confident that our database serves as a strong basis for further research relating to freshwater lakes influenced by human activities.

From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping.

Spatially explicit, quantitative information on soil hydraulic properties is required in various modelling schemes. At European scale, EU-SoilHydroGrids proved its applicability in a number of studies, in ecological predictions, geological and hydrological hazard assessment, agri-environmental models, among others. Inspired by its continental antecedent, an analogous, but larger scale, national, 3D soil hydraulic database was elaborated for the territory of Hungary (HU-SoilHydroGrids) supported by various improvements (i-iv) in the computation process. Pedotransfer functions (PTFs) were built in the form of i) advanced machine learning methods and ensemble models, and trained on the ii) national soil hydrophysical dataset. The set of predictors used in PTFs was supplemented by iii) additional environmental auxiliary variables. Spatial layers of the soil hydraulic parameters were generated using iv) 100 m resolution information on primary soil properties, namely DOSoReMI.hu. HU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties (water content at saturation, field capacity and wilting point, saturated hydraulic conductivity and van Genuchten parameters for the description of the moisture retention curve) with national coverage at 100 m spatial resolution down to 2 m depth for six GSM standard depth layers. The HU-SoilHydroGrids has significantly lower squared error in the case of describing the moisture retention curve and hydraulic conductivity than the EU-SoilHydroGrids. The derived 3D soil hydraulic database (ver1.0) is presently available in National Laboratory for Water Science and Water Safety for project partners in order to test its functional performance in describing hydrological and ecological processes.

Disentangling the ecosystem service 'flood regulation': Mechanisms and relevant ecosystem condition characteristics.

Riverine floods cause increasingly severe damages to human settlements and infrastructure. Ecosystems have a natural capacity to decrease both severity and frequency of floods. Natural flood regulation processes along freshwaters can be attributed to two different mechanisms: flood prevention that takes place in the whole catchment and flood mitigation once the water has accumulated in the stream. These flood regulating mechanisms are not consistently recognized in major ecosystem service (ES) classifications. For a balanced landscape management, it is important to assess the ES flood regulation so that it can account for the different processes at the relevant sites. We reviewed literature, classified them according to these mechanisms, and analysed the influencing ecosystem characteristics. For prevention, vegetation biomass and forest extent were predominant, while for mitigation, the available space for water was decisive. We add some aspects on assessing flood regulation as ES, and suggest also to include flood hazard into calculations.

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

Spatially explicit estimation of heat stress-related impacts of climate change on the milk production of dairy cows in the United Kingdom.

Dairy farming is one the most important sectors of United Kingdom (UK) agriculture. It faces major challenges due to climate change, which will have direct impacts on dairy cows as a result of heat stress. In the absence of adaptations, this could potentially lead to considerable milk loss. Using an 11-member climate projection ensemble, as well as an ensemble of 18 milk loss estimation methods, temporal changes in milk production of UK dairy cows were estimated for the 21st century at a 25 km resolution in a spatially-explicit way. While increases in UK temperatures are projected to lead to relatively low average annual milk losses, even for southern UK regions (<180 kg/cow), the 'hottest' 25×25 km grid cell in the hottest year in the 2090s, showed an annual milk loss exceeding 1300 kg/cow. This figure represents approximately 17% of the potential milk production of today's average cow. Despite the potential considerable inter-annual variability of annual milk loss, as well as the large differences between the climate projections, the variety of calculation methods is likely to introduce even greater uncertainty into milk loss estimations. To address this issue, a novel, more biologically-appropriate mechanism of estimating milk loss is proposed that provides more realistic future projections. We conclude that South West England is the region most vulnerable to climate change economically, because it is characterised by a high dairy herd density and therefore potentially high heat stress-related milk loss. In the absence of mitigation measures, estimated heat stress-related annual income loss for this region by the end of this century may reach £13.4M in average years and £33.8M in extreme years.

Remarks to the debate on mapping heavy metals in soil and soil monitoring in the European Union.

We provide an overview of the main features of the LUCAS topsoil survey of the EU in comparison to the GEMAS survey. In addition we describe the policy requirements and scientific principles of soil monitoring programs.

Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment.

Soil contamination is one of the greatest concerns among the threats to soil resources in Europe and globally. Despite of its importance there was only very course scale (1/5000km(2)) data available on soil heavy metal concentrations prior to the LUCAS topsoil survey, which had a sampling density of 200km(2). Based on the results of the LUCAS sampling and auxiliary information detailed and up-to-date maps of heavy metals (As, Cd, Cr, Cu, Hg, Pb, Zn, Sb, Co and Ni) in the topsoil of the European Union were produced. Using the maps of heavy metal concentration in topsoil we made a spatial prediction of areas where local assessment is suggested to monitor and eventually control the potential threat from heavy metals. Most of the examined elements remain under the corresponding threshold values in the majority of the land of the EU. However, one or more of the elements exceed the applied threshold concentration on 1.2Mkm(2), which is 28.3% of the total surface area of the EU. While natural backgrounds might be the reason for high concentrations on large proportion of the affected soils, historical and recent industrial and mining areas show elevated concentrations (predominantly of As, Cd, Pb and Hg) too, indicating the magnitude of anthropogenic effect on soil quality in Europe.

Mapping geogenic radon potential by regression kriging.

Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.

Influence of lowland forests on subsurface salt accumulation in shallow groundwater areas.

In flat sedimentary plains in areas with a sub-humid climate, tree planting on grasslands and arable lands creates strong hydrological shifts. As a result of deep rooting and high water uptake of trees, groundwater levels drop and subsurface salt accumulation increases. Tree planting has expanded globally and in Hungary it reached rates of 15 000 ha year(-1), being focused mainly in the Great Hungarian Plain where forests replace grasslands and crops in a region with widespread shallow groundwater. We performed soil and groundwater observations in 31 pairs of forest and control plots in the region, including gradients of initial water table depth and salinity, soil layering, and tree species and age. Accumulated tree biomass was positively correlated with soil salinization rates following tree planting, being also affected by species (poplar > common oak > black locust) and stand age. Differences among tree species effects appeared to be related to their growth rates. Due to downward deep percolation and salt leaching episodes during the Hungarian winters, the observed salt accumulation rates were lower than those described under similar settings in the warmer Argentine Pampas.

Cesium-137 concentration of soils in Pest County, Hungary.

This paper presents the results of measurements of (137)Cs in soils in Pest County, Hungary. We investigated forty five soil monoliths from monitoring locations of a countrywide Soil Information and Monitoring System (SIMS) at depths of 0-30, 30-60, 60-90, 90-120 and 120-150 cm. The (137)Cs concentrations were determined by gamma spectroscopy. We found that only the upper layer of soil (0-30 cm) contained (137)Cs above the detection limit (0.5 Bq kg(-1)). The (137)Cs concentration values ranged from the detection limit to 61.1 Bq kg(-1) ± 2.2 Bq kg(-1) and were lognormally distributed. The concentrations had a geometric mean 6.4 Bq kg(-1) and a geometric standard deviation 2.3 (an arithmetic mean 9.5 Bq kg(-1), an arithmetic standard deviation 11.3 Bq kg(-1)). We constructed a (137)Cs map for Pest County this is the first detailed (137)Cs map in Hungary. Concentrations were systematically higher (10.0-61.1 Bq kg(-1)) than average in the Pilis and Buda Mountains and the Northern part of the Gödöllo Hills. In contrast, low concentrations (0.0-10.0 Bq kg(-1)) characterized the southern part of the Gödöllo Hills, the Pest Plane and the Börzsöny Mountains. Two highest values were 46.9 Bq kg(-1) and 61.1 Bq kg(-1): one of these localities, a loamy brown forest soil was chosen to study relationship between (137)Cs migration and clay materials of the soil. According to differential thermal analysis (DTA) and x-ray diffraction (XRD) analyses, illite and kaolinite were dominant in the soil. The amount of clay was closely proportional to (137)Cs concentration (R = 0.89). At the locality having the highest surface concentration, 78% of the total detected (137)Cs concentration was measured in the top 3 cm layer of soil profile and there was no detectable concentration below 20 cm. This result indicates that penetration of (137)Cs into the soil is a very slow process in this case. Analysis of this depth profile showed lower (137)Cs migration parameter values (effective diffusion coefficient and migration velocity) than predicted in a previous study for brown forest soils in the same area of Hungary.