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Environmental and economic effects of water and deflation destruction of steppe soil in Ukraine

Dudiak Nataliia, Pichura Vitalii, Potravka Larisa, Stratichuk Natalia

Journal of Water and Land Development

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2021

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no. 50

10--26

EN

Water and wind erosion are the most powerful factors in the decrease of soil fertility and a threat to food security. The study was conducted on the steppe zone in Ukraine (total area of 167.4 thous. km2), including agricultural land (131.6 thous. km2). At the first stage, the modeling of spatial differentiation of water and wind erosion manifestations was carried out to calculate losses of soil (Mg∙ha–1) and to determine their degradation. At the second stage, soil-climatic bonitet of zonal soils (points) is carried out to determine their natural fertility (Mg∙ha–1). At the third stage, the spatial adjustment of the natural soil fertility to the negative effect of erosion was carried out. This made it possible to calculate crop losses and total financial losses due to water and wind erosion. The integrated spatial modeling showed that about 68.7% of arable land was constantly affected by the combined erosion, in particular the area of low eroded arable land (16.8%), and medium and highly eroded land (22.1%). Due to erodibility of soil, about 23.3% of agricultural land transferred from the category of high and medium quality to medium, low and very low quality, which is caused by the loss of soil fertility of up to 70%, crop losses of up to 1.93 Mg∙ha–1 ha–1 and eduction of agricultural income up to 390 USD∙ha–1. In the steppe region under the research, gross crop losses from erosion were up to 15.11 thous. Mg∙ha–1 (3.05 mln USD). In order to protect soils, improve fertility and increase crop yields in the steppe zone in Ukraine, the following measures were suggested: adaptive and landscape erosion control design with elements of conservation farming in accordance with the spatial differentiation of soil quality and extent of water erosion deflation danger.

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Spatial Modeling of the Effects of Deflation Destruction of the Steppe Soils of Ukraine

Dudiak Nataliia Vasylivna, Pichura Vitalii Ivanovich, Potravka Larisa Aleksandrovna, Stroganov Alexander Alexandrovich

Journal of Ecological Engineering

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2020

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Vol. 21, nr 2

166--177

EN

The decrease in the agricultural efficiency is associated with the influence of wind erosion, the consequence of which is a decrease in the soil fertility. Significant effects of wind erosion are typical of the arid and semi arid zones with a small amount of precipitation, high air temperature and degree of evaporation, reinforced by strong winds and low differentiation of plant protection. It has been proven that the intensity of the effects of deflation processes depends on the physical and geographical conditions of the distribution of agricultural land, systematic soil protection activities and the availability of vegetation. It has been established that the acceleration of the deflation processes occurs in the territories with increased anthropogenic pressure, which leads to ecological disturbance of the natural balance characterizing territorial ecosystems. In the course of the studies it was found that the natural processes of wind erosion are significantly enhanced by the absence of a scientifically-based and ecological land development system of agriculture, which leads to destruction of the soil cover, reduction of soil fertility, damage to the agricultural crops and, thus, the economic damage. As a result of application of the GIS and ERS technologies, the empirical-statistical model of the possible soil loss due to wind erosion in the territory of the Steppe zone of Ukraine, it has been found that in the course of the deflation processes in the territory taken by naked fallow upon the absence of the conditions for the deflation resistance activities, the value of soil loss at the epicenter of dust storms can reach about 600 t / ha. Studies proved the importance of the deflation resistant action of the vegetation cover, which tends to an increase in the erosion dangerous (favorable) areas of agricultural land by 1.7 times, which reduces the soil loss by 5.62 times. In accordance with the intensity of the effects of the deflation processes and the increase of the soil losses, the contour and land development deflation resistance activities with elements of soil protection agriculture were proposed.

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Geomodelling of Destruction of Soils of Ukrainian Steppe Due to Water Erosion

Dudiak Nataliia Vasylivna, Pichura Vitalii Ivanovich, Potravka Larisa Aleksandrovna, Stratichuk Natalia Vladimirovna

Journal of Ecological Engineering

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2019

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Vol. 20, nr 8

192--198

EN

Spatial raster distribution models of the values of factors influencing the potential soil erosion hazard were created using GIS technologies. The erosion hazard was estimated using the modified RUSLE (Revised Universal Soil Loss Equation) model. The potential of annual soil loss of arable land was calculated. The spatial gradation of erosion violation of administrative and territorial units in the steppe zone of Ukraine was provided. About 32.7% of arable land that is subject to high erosion hazard was allocated. About 48 administrative and territorial units have a specific area less than 5% of erosion disturbed lands. They are characterized by a resistant type of agrolandscapes regarding the water-erosion processes. Most administrative and territorial units with high erosive-accumulative potential (the percentage of the area is 15% or more) are located in the western and southwestern parts of the steppe zone of Ukraine. The specific area of erosion hazardous lands reaches up to 32% in separate administrative-territorial units. The obtained results allow determining the need for a spatially discrete-distribution implementation of adaptive-landscape anti-erosion design with the elements of soil-protective agriculture.

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Modelowanie agentowe – nowoczesna koncepcja modelowania w GIS

Dzieszko P, Bartkowiak K., Giełda-Pinas K

Roczniki Geomatyki

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2013

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T. 11, z. 4(61)

7--16

PL

One of the most prospective bottom-up approaches to modeling of human-environment relations is agent-based modeling (ABM). ABM is a modern technique more and more often used in Geographical Information Science. It is based on entities called agents which can make spatial decisions. They can also exchange information with each other. Moreover, they have attributes which allow to describe their actual state. In classical approach to modeling, all entities are often quite similar. It is possible to create a model with very similar entities within ABM. These entities may behave slightly differently. Agents can have identical attributes and quite different decision rules. It allows a user to apply randomness in a model which is really crucial in environmental studies. ABM and simulation can be traced to investigations into complex adaptive systems, the evolution of cooperation and artificial life. Unlike other modeling approaches, ABM begins and ends with the agent’s perspective. The application of ABM to simulating dynamics within GIS has seen a considerable increase over the last decade. Both agents and decisions they make have spatial reference. So linking AMB with GIS is a natural consequence of these two techniques development. ABM is normally a very useful decision making process, in extreme events simulation, forecasting the environment development, spatial planning, and environmental impact assessment. In this paper. possibilities of the use of ABM were presented. ABM is a modern research technique within GIS. Most important features of ABM were described as well as well-known software platforms and toolsets for agent-based model creating. Finally, information when the ABM can be especially useful in research work and how to select the best system which will fit the standards of our model was provided.

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Agenci w modelowaniu agentowym (ABM)

Dzieszko P, Bartkowiak K, Giełda-Pinas K

Roczniki Geomatyki

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2013

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T. 11, z. 4(61)

17--23

PL

Modelowanie agentowe (ang. agent-based modelling – ABM) stanowi dynamicznie rozwijającą się metodę modelowania, o szerokim spektrum zastosowań w różnych dziedzinach nauki i życia codziennego. Obecnie postępująca integracja ABM z systemami informacji geograficznej dostarcza zaawansowanych i kompleksowych narzędzi do geomodelowania. Modele agentowe są cyfrową reprezentacją systemów takich jak ekosystemy, społeczności i gospodarki, złożonych z elementów i obiektów rozmieszczonych we wspólnym otoczeniu (środowisku działania). Unikalność modelowania agentowego polega na umożliwieniu zdefiniowania zasad decyzyjnych jednostek - agentów, określeniu uwarunkowań, w jakich funkcjonują oraz zrealizowaniu tych zasad w dowolnej ilości iteracji w celu przeanalizowania rezultatów działania systemu. Agenci w modelu mogą być wysoce zróżnicowani. Mogą mieć charakter ożywiony (np. rolnicy, mieszkańcy, właściciele ziemscy) oraz nieożywiony (np. firmy, samochody). Mogą także być pogrupowani w większe jednostki (np. społeczności, narodowości, budynki, gospodarstwa domowe, miasta, sieci drogowe) oraz mogą być mobilni (np. pieszo, samochodem, firmy zmieniające siedzibę, mieszkańcy, którzy się przeprowadzają). Ze względu na wewnętrzną strukturę, agentów dzielimy na słabych i silnych. Słabi agenci mają uproszczoną strukturę wewnętrzną i proste zasady decyzyjne, podczas gdy zasady decyzyjne agentów silnych czerpią z wiedzy sztucznej inteligencji, a oni sami potrafią się uczyć, rozwiązywać problemy i planować. Agenci wyposażeni są w atrybuty, które pozwalają opisać ich aktualny stan. Posiadają też sprecyzowane zasady decyzyjne, które pozwalają im podejmować decyzje czasie i przestrzeni oraz czynności, które podejmowane są przez agentów po podjętej decyzji. Mnogość zastosowań modeli agentowych sprawia, że agenci posiadają skrajnie różne charakterystyki, a to powoduje, że trudno jest przypisać im jedne uniwersalne i wspólne cechy. Niemniej agenci najczęściej posiadają kilka cech, które nie zmieniają się w zależności od zastosowania modelu, mianowicie: autonomię, różnorodność, aktywność, cel, interaktywność, ograniczoną racjonalność, mobilność, i możliwość uczenia się. W modelach matematycznych najczęściej wszystkie elementy i obiekty danego typu są identyczne. Założeniem modelowania agentowego jest możliwość różnicowania agentów oraz możliwość zastosowania losowości w ich zachowaniach nawet, jeśli mają podobną budowę. Agenci mogą mieć identyczne atrybuty, ale skrajnie różne zasady decyzyjne, co pozwala wprowadzić do modelu bardzo istotny w naukach przyrodniczych element losowości.

EN

Agent-based modeling (ABM) is a dynamically developing method of modeling broadly used in various areas of science and in everyday life. Integration of ABM with geographic information systems provides advanced and comprehensive instruments for geomodeling. Agent-based models are digital representation of such systems as eco-systems, societies and economies composed of elements and objects located in common environment (action environment). Unique nature of agent-based modeling consists in the possibility to define the rules of decision-making of individual agents, to determine conditions of their functioning and to implement these rules in any number of iterations in order to analyze the results of the system operation. Agents in the model may be highly diversified. They may be alive (e.g. farmers, inhabitants, landlords) or inanimate (e.g. companies, cars). They may be also grouped in bigger units (e.g. buildings, households, cities, road networks) and they may be mobile (e.g. companies changing their seat, inhabitants moving to other places). Because of the internal structure, we divide agents into strong and weak. Weak agents have simplified internal structure and simple decision-making rules, while decision-making rules of strong agents draw from the knowledge of artificial intelligence and these agents can learn, solve problems and make plans. Agents have attributes allowing them to describe their present state. They also have defined decision-making rules allowing them to take decisions about time and place and actions taken by the agents after the decision is made. Multiple application of agent-based models entails extremely varied characteristic features of agents and this, in turn makes difficult assigning to them universal and common features. Nevertheless, agents usually have a few features which do not change depending on the model applied, namely: autonomy, variety, activeness, goal, interactivity, limited rationality, mobility and ability to learn. In mathematical models most often all elements and objects of a given type are identical. Possibility to differentiate agents and to randomize their behaviours is assumed in agent-based modeling even when they have similar structure. Agents may have identical attributes but extremely different decision-making rules, which allows to introduce to the model the element of randomness so important in natural sciences.

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Wpływ metodologii przetwarzania na wyniki statycznego modelowania 3D

Papiernik B.

Prace Naukowe Instytutu Nafty i Gazu

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2010

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nr 170 wyd. konferencyjne

213--217

PL

Przestrzenne modelowanie geologiczne, tzw. modelowanie statyczne jest podstawowym narzędziem ilościowej analizy budowy struktur wgłębnych. Uzyskiwane wyniki modelowania zależą w dużym stopniu od zastosowanej procedury przetwarzania. W artykule zilustrowano tą tezę na dwóch przykładach. Pokazano wpływ zmian parametrów krigingu na 2D model prędkości opracowany na podstawie danych z niecki miechowskiej. Porównano również wyniki modelowania algorytmami deterministycznym (kriging) i stochastycznymi (algorytmy sekwencyjne Petrela) w rejonie antykliny Zaosia.

EN

Spatial (3D) geomodeling, so called structural-parametric modeling, belongs to basic tools of the quantitative subsurface analysis. Modeling results are strongly dependent on numerical routines applied. That thesis was illustrated in the paper with two case studies. The influence of kriging settings on the 2D velocity model was demonstrated using data from the Miechow Though. The author also compared results of the 3D lithologic and parametrical modeling with the use of the deterministic algorithm (kriging) and the stochastic simulation techniques (Petrel sequential algorithms) in the Zaosie Anticline area.