28 research outputs found
Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales
Heat demand of buildings and related CO2 emissions caused by energy supply contribute
to global climate change. Spatial data-based heat planning enables municipalities to reorganize
local heating sectors towards efficient use of regional renewable energy resources. Here, annual
heat demand of residential buildings is modeled and mapped for a German federal state to provide
regional basic data. Using a 3D building stock model and standard values of building-type-specific
heat demand from a regional building typology in a Geographic Information Systems (GIS)-based
bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the
construction period of residential buildings, aggregated on municipality sections and hectare grid
cells, are used to show how census-based spatial data sets can enhance the approach. Partial results
from all three models are validated against reported regional data on heat demand as well as against
gas consumption of a municipality. All three models overestimate reported heat demand on regional
levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid
cells data set leads to best prediction accuracy values at municipality section level, showing the
benefit of integrating this high detailed spatial data set on building age
Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model
Soil compaction caused by field traffic is one of the main threats to agricultural landscapes. Compacted soils have a reduced hydraulic conductivity, lower plant growth and increased surface runoff resulting in numerous environmental issues such as increased nutrient leaching and flood risk. Mitigating soil compaction, therefore, is a major goal for a sustainable agriculture and environmental protection. To prevent undesirable effects of field traffic, it is essential to know where and when soil compaction may occur. This study developed a model for soil compaction risk assessment of arable soils at regional scale. A combination of (i) soil, weather, crop type and machinery information; (ii) a soil moisture model and (iii) soil compaction models forms the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment). The SaSCiA-model computes daily maps of soil compaction risk and associated area statistics for varying depths at actual field conditions and for entire regions. Applications with open access data in two different study areas in northern Germany demonstrated the model’s applicability. Soil compaction risks strongly varied in space and time throughout the year. SaSCiA allows a detailed spatio-temporal analysis of soil compaction risk at the regional scale, which exceed those of currently available models. Applying SaSCiA may support farmers, stakeholders and consultants in making decision for a more sustainable agriculture
Soil Penetration Resistance after One-Time Inversion Tillage: A Spatio-Temporal Analysis at the Field Scale
Conservation agriculture may lead to increased penetration resistance due to soil compaction. To loosen the topsoil and lower the compaction, one-time inversion tillage (OTIT) is a measure frequently used in conservation agriculture. However, the duration of the positive effects of this measure on penetration resistance is sparsely known. Therefore, the aim of this study was to analyze the spatio-temporal behavior of penetration resistance after OTIT as an indicator for soil compaction. A field subdivided into three differently tilled plots (conventional tillage with moldboard plough to 30 cm depth (CT), reduced tillage with chisel plough to 25 cm depth (RT1) and reduced tillage with disk harrow to 10 cm depth (RT2)) served as study area. In 2014, the entire field was tilled by moldboard plough and penetration resistance was recorded in the following 5 years. The results showed that OTIT reduced the penetration resistance in both RT-plots and led to an approximation in all three plots. However, after 18 (RT2) and 30 months (RT1), the differences in penetration resistance were higher (p < 0.01) in both RT-plots compared to CT. Consequently, OTIT can effectively remove the compacted layer developed in conservation agriculture. However, the lasting effect seems to be relatively shor
Of Animal Husbandry and Food Production—A First Step towards a Modular Agent-Based Modelling Platform for Socio-Ecological Dynamics
Agent-based models provide detailed, bottom-up approaches to investigate complex socio-ecological systems. This study presents a first step towards a modular agent-based simulation that is based upon empirical data, as well as environmental suitability maps and an assessment of livestock units. To illustrate the capabilities of our simulation, we use a geographically explicit approach to simulate a component of the production of animal products of a rural settlement in the lower Bakırçay catchment, western Turkey. The model structurally couples various agent types representing several elements and processes of the animal husbandry and food production value chain, such as sedentary herders—practising daily, short-distance pastoralism—and their flocks of goats and sheep, as well as milking and slaughtering. The modelling tool captures the fundamental socio-ecological dynamics of animal husbandry and food production in rural settlements. Therefore, the tool is valuable as a basis to discuss hypotheses regarding the number of animals that are needed to cover the requirements of different growing populations
Of Animal Husbandry and Food Production - A First Step towards a Modular Agent-Based Modelling Platform for Socio-Ecological Dynamics
Agent-based models provide detailed, bottom-up approaches to investigate complex socio-ecological systems. This study presents a first step towards a modular agent-based simulation that is based upon empirical data, as well as environmental suitability maps and an assessment of livestock units. To illustrate the capabilities of our simulation, we use a geographically explicit approach to simulate a component of the production of animal products of a rural settlement in the lower Bakırçay catchment, western Turkey. The model structurally couples various agent types representing several elements and processes of the animal husbandry and food production value chain, such as sedentary herders—practising daily, short-distance pastoralism - and their flocks of goats and sheep, as well as milking and slaughtering. The modelling tool captures the fundamental socio-ecological dynamics of animal husbandry and food production in rural settlements. Therefore, the tool is valuable as a basis to discuss hypotheses regarding the number of animals that are needed to cover the requirements of different growing populations
Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods
Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted
Estimation of Soil Material Transportation by Wind Based on in Situ Wind Tunnel Experiments
25% and 40% of territory of Hungary is moderate to highly vulnerable to deflation. However, precise estimates about the soil loss and related losses of organic matter and nutrients due to wind erosion are missing in most cases. In order to determine magnitudes of nutrient masses removed at wind velocities that frequently occur in SE Hungary, in-situ experiments using a portable wind tunnel have been conducted on small test plots with an erosional length of 5.6 m and a width of 0.65 m. The wind tunnel experiments have been carried through on a Chernozem which is typical for this region. In order to compare the effects of soil coverage on the masses of blown soil sediment and adsorbed nutrients, two soil surface types have been tested under similar soil moisture und atmospheric conditions: (1) bare soil (dead fallow) and (2) bare soil surface interrupted by a row of maize plants directed downwind along the center line of the test plots. The results of our experiments clearly show that a constant wind velocity of 15 m s-1 (at a height of 0.3 m) lasting over a short time period of 10 minutes can already cause noticeable changes in the composition and size of soil aggregates at the top of the soil surface. Due to the grain size selectivity of the erosive forces the relative share of soil aggregates comprising diameters > 1 mm increased by 5-10% compared with the unaffected soil. Moreover it has shown that short time wind erosion events as simulated in this study can result in erosion rates between 100 and 120 g m-2, where the erosion rates measured for bare soils are only slightly, but not significantly higher than those of the loosely vegetated ones. Soil samples taken from sediment traps mounted in different heights close to the outlet of the wind tunnel point to an enrichment of organic matter (OM) of about 0.6 to 1 % by mass referred to the control samples. From these findings has been calculated that the relocation of organic matter within short term wind erosion events can amount to 4.5 to 5.0 g OM m-2. With the help of portable field wind tunnel experiments we can conclude that our valuable, high quality chernozems are struck by wind erosion mainly in drought periods
Location Modeling of Final Palaeolithic Sites in Northern Germany
Location modeling, both inductive and deductive, is widely used in archaeology to predict
or investigate the spatial distribution of sites. The commonality among these approaches is their
consideration of only spatial effects of the first order (i.e., the interaction of the locations with the
site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely
considered. We introduce a deductive approach to investigating such second-order effects using
linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to
simulate a point distribution using expert knowledge of two distinct hunter–gatherer groups, namely,
reindeer hunters and elk hunters. The modeled points and point densities were compared with the
actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects
of varying intensity for different periods, were applied. The results reveal differences between the
two investigated groups, with the reindeer hunters showing location-related interaction patterns,
indicating a spatial memory of the preferred locations over an extended period of time. Overall, this
paper shows that second-order effects occur in the geographical modeling of archaeological finds and
should be taken into account by using approaches such as the one presented in this paper
Transforming landscapes: Modeling land-use patterns of environmental borderlands
How did socio-cultural transformation processes change land-use patterns? Throughout the last 50 years, outstanding comprehensive geographic,
archaeobiological, and archaeological data have been produced for the area of Oldenburger Graben, Schleswig-Holstein, Germany. Based on this
exceptional data set, we are able to study the land-use patterns for a period ranging from the Final Mesolithic until the Late Neolithic (4600–1700 BCE).
By application of fuzzy modeling techniques, these patterns are investigated diachronically in order to assess the scale of transformations between the
different archaeological phases. Based on nutrient requirements and proposed dietary composition estimates derived from empirical archaeobotanical,
archaeozoological, and stable isotope data, the required extent of the areas for different land-use practices are modeled. This information is made spatially
explicit using a fuzzy model that reconstructs areas of potential vegetation and land-use for each transformation phase. Pollen data are used to validate
the type and extent of land-use categories. The model results are used to test hypotheses on the dynamics of socio-cultural transformations: can we
observe a diversification of land-use patterns over time or does continuity of land-use practices prevail? By integrating the different lines of evidence within
a spatially explicit modeling approach, we reach a new quality of data analysis with a high degree of contextualization. This allows testing of hypotheses
about Neolithic transformation processes by an explicit adjustment of our model assumptions, variables, and parameters