The objective of this study is to develop a method of appraising rock aggregate resources, using open data and open source tools. The availability of aggregates in Finland is mostly determined by competing land use and restrictions on extraction. Therefore, it is important to determine the extent of available resources, especially near areas of high demand. The study area consists of the 14 municipalities in the Helsinki metropolitan area, a total of 3841 km(2). The data used are open access, provided by the Geological Survey of Finland and the National Land Survey. These are combined in a GIS to identify locations where extraction of aggregates is possible. Geology, limitations and the highest and lowest point of possible extraction are determined. These are used to estimate the available resources and locate the economically feasible sites. Data used include a digital elevation model and layers on geology and land use. The results show that competing land use has sterilized most aggregate locations in the area. Remaining locations are concentrated on the edges. However, some potential sites remain. Field evaluations and comparison to previous studies show that the method has potential in evaluating remaining resources and directing further study for prospective production areas. The model is fast in coarsely determining aggregate volume. It is highly suitable for focusing expert fieldwork. Land use in the area continues to sterilize new locations. To avoid economic and ecological damage, a plan should be implemented for securing this resource. This may include the reserving of locations, reducing use, checking legislation on production and recycling used aggregates.

There exist numerous ecotechnologies for recovery and reuse of carbon and nutrients from various waste streams before they are lost to runoff. However, it remains largely unknown how growing implementation of such ecotechnologies affect nutrient emissions to surface waters at catchment scale. Here, this knowledge gap is addressed by application of SWAT model in three case study catchments draining to the Baltic Sea: Vantaanjoki (Finland), Fyrisån (Sweden) and Słupia (Poland). Sustainability analysis with Multi-Criteria Analysis was applied in the stakeholder workshops in the case study areas to assess different ecotechnology alternatives. The following ecotechnologies received the highest sustainability scores: in Vantaanjoki anaerobic digestion, based on mostly agricultural residues; in Fyrisån source-separation of wastewaters; in Słupia nutrient extraction within the wastewater treatment process. The effect of application of digestate on agricultural soils in the Vantaanjoki catchment was simulated by adjusting the model parameters describing the organic carbon content and physical properties of soil. The results showed small reductions of nutrient loads to the Gulf of Finland. Larger reductions of nutrient loads to Lake Mälaren in Sweden and the Baltic Sea in Poland were achieved as a result of the wastewater treatment upgrades. In the Fyrisån catchment, higher relative reductions were simulated for TN than TP, and in dry years than in wet years. Although the studied ecotechnologies did not show as high effectiveness in nutrient load reduction as combinations of traditional Best Management Practices reported in literature, they do have other multiple benefits including crop yield increase, electricity, heat and bio-based fertilizer production.

The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modeling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, that is, 96 × 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting-habitat suitability modeling, that is, the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas (PAs) was considerable, but our analysis also revealed many potentially high-quality forest stands outside PAs. However, many of these sites are increasingly threatened by logging because of increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and a corresponding approach would also be feasible and recommendable elsewhere where similar data are available.

Periglacial environments are characterized by highly dynamic landscapes. Freezing and thawing lead to ground movement, associated with cryoturbation and solifluction. These processes are sensitive to climate change and variably distributed depending on multiple environmental factors. In this study, we used multi-geometry Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to investigate the spatial distribution of the mean annual ground velocity in a mountainous landscape in Northern Norway. Statistical modeling was employed to examine how periglacial ground velocity is related to environmental variables characterizing the diverse climatic, geomorphic, hydrological and biological conditions within a 148 km(2) study area. Two-dimensional (2D) InSAR results document mean annual ground velocity up to 15 mm/yr. Vertical and horizontal velocity components in the East-West plane show variable spatial distribution, which can be explained by the characteristics of cryoturbation and solifluction operating differently over flat and sloping terrain. Statistical modeling shows that slope angle and mean annual air temperature variables are the most important environmental factors explaining the distribution of the horizontal and vertical components, respectively. Vegetation and snow cover also have a local influence, interpreted as indicators of the ground material and moisture conditions. The results show contrasted model performance depending on the velocity component used as a response variable. In general, our study highlights the potential of integrating radar remote sensing and statistical modeling to investigate mountainous regions and better understand the relations between environmental factors, periglacial processes and ground dynamics.

The trend of energy policy in European Union as well as in international context has lately been to increase the share of renewable biofuels. The causes for this are global warming, shrinking reserves of fossil fuels and governments' aspiration for energy independence. Microalgae have shown to be a potential source of biofuels. Though cultivation of microalgae has a long history, has production for fuel yet been unprofitable. Production has become more effective as cultivation has shifted from open ponds to controlled photobioreactors but to achieve effective cultivation methods substantially more understanding on the ecophysiology of microalgae is needed. The aim of my thesis was to research the optimal light intensity and temperature of photosynthesis for three microalgae (Chlorella pyrenoidosa, Euglena gracilis and Selenastrum sp.), which are the main parameters limiting the level of photosynthesis in nutrient rich environments such as photobioreactor. The research strains were incubated in eight light intensities (0,15-250 µmol m-2 s-2) and in 5-6 temperatures (10-35 °C). Photosynthetic activity was determined with radiocarbon method which is based on the stoichiometry of photosynthesis. The purpose of radiocarbon method is to estimate how much dissolved carbon dioxide do the algae assimilate when photosynthesizing. In the method the algae are incubated in light and dark bottles where certain amount of radiocarbon (14C) has been added as a tracer. The algae fix 14C in the proportion to available 12C. 14C method has become the most common way to measure the photosynthesis of microalgae. All of the algal strains grew in 10-30 °C but C. pyrenoidosa was the only one which grew also in 35 °C. The data was analyzed by fitting them with two photosynthesis-light intensity relationship models and one photosynthesis-temperature relationship model and as a result values of essential parameters, i.e. optimal light intensity (Iopt) and temperature (Topt) for photosynthesis, could be estimated. The model which gave the best fit was chosen to describe the photosynthesis-light intensity relationship. The optimal light intensity for C. pyrenoidosa ranged between 121–242 µmol m-2 s-2 and optimal temperature was 15 °C. Corresponding values for E. gracilis were 117-161 µmol m-2 s-2 and 24,1 °C, and for Selenastrum sp. 126-175 µmol m-2 s-2 and 16,7 °C. Q10-values were also determined. With all research strains, the level of photosynthesis increased as light intensity and temperature grew until optimal values were reached. The strains tolerated higher light intensities in warmer temperatures but after reaching the optimal temperature, the level of photosynthesis did not increase any more with elevating temperature. Robust algal strains, i.e. strains, that are most adaptable in terms of light intensity and temperature, are the most prominent ones for biofuel production. From these research strains the most adaptable strain in terms of light intensity was C. pyrenoidosa and in terms of temperature Selenastrum sp. C. pyrenoidosa had superior carbon fixation rate in relation to cell size. Therefore it can be concluded that C. pyrenoidosa is the most suitable algal strains for biofuel applications of the strains assessed here.

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

The aim of this study was to develop mathematical energy balance models for early and middle lactation period of dairy cows. The traits for predicting were information of diet, feed, milk production, milk composition, body weight and body condition score. This study was a part of development work of KarjaKompassi-project. The data used in this study was based on 12 feeding experiments performed in Finland. The complete data from the studies included 2647 weekly records from multiparous dairy cows and 1070 weekly records from primiparous dairy cows. The data was collected from calving to 8-28 weeks of lactation. Three-fourths of the totals of 344 dairy cows were Finnish Ayshire cows and the rest of the cows were Friesian Cattle. The cows were fed by the Finnish feeding standards. The data was handled by the Mixed-procedure of the SAS-programme. The outliers were removed with Tukey´s method. The relationship between energy balance and predictor traits was studied with correlation analysis. The regression analysis was used to predicting energy balance. To quantify the relationship of lactation day to energy balance, 5 functions were fitted. The random factor was a cow in the experiment. The model fit was assessed by residual mean square error, coefficient of determination and Bayesian information criterion. The best models were validated in the independent data. Ali-Schaeffer achieved the highest fit functions. It was used by the basal model. The error in every model grew after the 12th lactation week, because the number of records decreased and energy balance turned positive. The proportion of concentrate in the diets and concentrate dry matter intake index were the best predictors of energy balance from traits of diet. Milk yield, ECM, milk fat and milk fat-protein ratio were good predictors during lactation period. The RMSE was lower when ECM was standardized. The body weight and body condition score didn’t improve the predictive value of the basal model. The models can be used to predict energy balance in the herd level, but they are not applicable for predicting individual cow energy balance.

High inputs of nutrients and organic matter make coastal seas places of intense air-sea CO2 exchange. Due to their complexity, the role of coastal seas in the global air-sea CO2 exchange is, however, still uncertain. Here, we investigate the role of phytoplankton stoichiometric flexibility and extracellular DOC production for the seasonal nutrient and CO2 partial pressure (pCO(2)) dynamics in the Gulf of Bothnia, Northern Baltic Sea. A 3-D ocean biogeochemical-physical model with variable phytoplankton stoichiometry is for the first time implemented in the area and validated against observations. By simulating non-Redfieldian internal phytoplankton stoichiometry, and a relatively large production of extracellular dissolved organic carbon (DOC), the model adequately reproduces observed seasonal cycles in macronutrients and pCO(2). The uptake of atmospheric CO2 is underestimated by 50% if instead using the Redfield ratio to determine the carbon assimilation, as in other Baltic Sea models currently in use. The model further suggests, based on the observed drawdown of pCO(2), that observational estimates of organic carbon production in the Gulf of Bothnia, derived with the 14C method, may be heavily underestimated. We conclude that stoichiometric variability and uncoupling of carbon and nutrient assimilation have to be considered in order to better understand the carbon cycle in coastal seas.

Despite the development of advanced process-based methods for estimating the discharge capacity of vegetated river channels, most of the practical one-dimensional modeling is based on a relatively simple divided channel method (DCM) with the Manning flow resistance formula. This study is motivated by the need to improve the reliability of modeling in practical applications while acknowledging the limitations on the availability of data on vegetation properties and related parameters required by the process-based methods. We investigate whether the advanced methods can be applied to modeling of vegetated compound channels by identifying the missing characteristics as parameters through the formulation of an inverse problem. Six models of channel discharge capacity are compared in respect of their uncertainty using a probabilistic approach. The model with the lowest estimated uncertainty in explaining differences between computed and observed values is considered the most favorable. Calculations were performed for flume and field settings varying in floodplain vegetation submergence, density, and flexibility, and in hydraulic conditions. The output uncertainty, estimated on the basis of a Bayes approach, was analyzed for a varying number of observation points, demonstrating the significance of the parameter equifinality. The results showed that very reliable predictions with low uncertainties can be obtained for process-based methods with a large number of parameters. The equifinality affects the parameter identification but not the uncertainty of a model. The best performance for sparse, emergent, rigid vegetation was obtained with the Mertens method and for dense, flexible vegetation with a simplified two-layer method, while a generalized two-layer model with a description of the plant flexibility was the most universally applicable to different vegetative conditions. In many cases, the Manning-based DCM performed satisfactorily but could not be reliably extrapolated to higher flows.

State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter > 15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67 degrees (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2 degrees (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.