Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue

FLT3 mutations in Early T-Cell Precursor ALL characterize a stem cell like leukemia and imply the clinical use of tyrosine kinase inhibitors

Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) has been identified as high-risk subgroup of acute T-lymphoblastic leukemia (T-ALL) with a high rate of FLT3-mutations in adults. To unravel the underlying pathomechanisms and the clinical course we assessed molecular alterations and clinical characteristics in a large cohort of ETP-ALL (n = 68) in comparison to non-ETP T-ALL adult patients. Interestingly, we found a high rate of FLT3-mutations in ETP-ALL samples (n = 24, 35%). Furthermore, FLT3 mutated ETP-ALL was characterized by a specific immunophenotype (CD2+/CD5-/CD13+/CD33-), a distinct gene expression pattern (aberrant expression of IGFBP7, WT1, GATA3) and mutational status (absence of NOTCH1 mutations and a low frequency, 21%, of clonal TCR rearrangements). The observed low GATA3 expression and high WT1 expression in combination with lack of NOTCH1 mutations and a low rate of TCR rearrangements point to a leukemic transformation at the pluripotent prothymocyte stage in FLT3 mutated ETP-ALL. The clinical outcome in ETP-ALL patients was poor, but encouraging in those patients with allogeneic stem cell transplantation (3-year OS: 74%). To further explore the efficacy of targeted therapies, we demonstrate that T-ALL cell lines transfected with FLT3 expression constructs were particularly sensitive to tyrosine kinase inhibitors. In conclusion, FLT3 mutated ETP-ALL defines a molecular distinct stem cell like leukemic subtype. These data warrant clinical studies with the implementation of FLT3 inhibitors in addition to early allogeneic stem cell transplantation for this high risk subgroup

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.Peer reviewe

Retrospective Analysis and scenario-based Projection of Land-Cover Change. The Example of the Upper Western Bug river Catchment, Ukraine

Land-cover and land-use change are highly dynamic and contribute to changes in the water balance. The most common changes are urbanisation, deforestation and desertification. This dissertation deals with the topic of projecting land cover (LC) into the near future with the help of the scenario technique. The aim of the thesis is the projection of the urban and rural land-cover change (LCC) till 2025. Two research questions are addressed in this work: (1) Which integrated concept can be developed to combine different methods to project urban and rural LCC into the future based on past LCC? (2) Is it possible to implement the developed concept and does the implementation deliver plausible results? To answer the research questions, a 4-step concept is adopted which serves as workflow for projecting the LCC: (i) the definition of the scenario context, and with that the definition of the study area, (ii) the identification of spatial and dynamic drivers for LCC, consisting of spatial drivers that are location-dependent, such as slope or soil type, and dynamic drivers of LCC, such as demographic and economic development, (iii) scenario formulation and projection of identified drivers, and (iv) scenario-based projections of future LCC, which means its quantitative and spatial modification (demand and allocation). For implementation and testing, the Upper Western Bug River catchment in Ukraine serves as the study site. The extent of the study area reaches from the source of the Western Bug to the Dobrotvir gauging station and is thus entirely located in Ukraine. This presents the first step of the developed concept of the projection of LCC. The existing geo-database for implementation is scarce. LC data is available for the territory of the EU (e.g. CORINE Land Cover) but not for Ukraine. Therefore, the implementation of the second step had to focus on the derivation of LC data for three-time steps to get the basis for the LCC. A classification of satellite scenes of Landsat and SPOT are done for the time steps 1989, 2000, and 2010. The two decades show a huge development of LCC. The increase of ‘artificial surface’ and unmanaged ‘grassland’ is visible with the decrease of ‘arable land’ and ‘forests’. An extended statistical analysis considering the systematic LCC reveals stable transition pathways, which in turn are the basis of the projection of future land cover. This refers to the second step of the concept: change detection. One transition pathway is that ‘arable land’ is not used and converted in settlement areas, but rather changes into ‘grassland’. With the derived LC and the analysed LCC as a basis of the work, the search for spatial and dynamic drivers start at the third time step. A list of dynamic drivers is first compiled, via literature research, and then tested for effect on LCC with statistical analyses. The dynamic driving forces are the ‘Gross Domestic Product’ (GDP) and ‘population development’. Spatial driving forces are laws/planning practices, fertility, slope, distance to the city Lviv, settlements, roads, or rivers. As a result, population development has an effect on the change in the LC class to 'artificial surface' from 'grassland' and 'arable land' from ‘grassland'. The implementation of the third step is done with the help of four storylines where the overall development of the dynamic drivers are included towards 2025. With that it is possible to project them into the future. The fourth step includes the calculation of the demand for each LC class with the projected dynamic drivers. The areas that have a high probability to change into another LC class are determined in suitability maps (allocation) which are derived by translating the transition pathways into GIS algorithms including the spatial driving forces. The class of 'artificial surface' changes the most under scenario A until 2025 and less under scenario D — the sustainable scenario. The LC class 'arable land' decreases in scenario A and B, but has the strongest development in scenario D. The LC class of unmanaged ‘grassland’ is quite stable under scenario A and B, but decreases in C and D. The results of systematic changes in ‘arable land’ that changes into ‘grassland’ are different compared to developments in other countries like Germany. The protection and conservation of arable land is not seen as strongly in other Eastern European countries as it is in the Upper Western Bug River catchment. In turn, the identified spatial and dynamic drivers fit other studies in Eastern Europe. The applied concept of projecting LCC with these steps are highly flexible for implementation in other study sites. However, the volume of work can differ within the steps because of the available databases. In Ukraine the available LCC data was not detailed enough to carry out a future projection. So, a main part of the work is dedicated to the derivation of past LC for different time steps. The involvement of regional experts helped to gain detailed knowledge of processes of LCC. The advantage of the presented concept with the mixture of quantitative (e.g. satellite analyses, statistical analyses) and qualitative methods can overcome methodological knowledge gaps. In addition, the retrospective analyses, as starting points, for the projection of future LCC carves out the site-specific allocation of change.:Acknowledgements..............................................................................................................................III Abstract..................................................................................................................................................IV Zusammenfassung..............................................................................................................................VII Contents..................................................................................................................................................X Abbreviations......................................................................................................................................XIV 1.Introduction.................................................................................................................................1 1.1Background................................................................................................................................1 1.2Objectives and Research Questions.......................................................................................3 1.3Structure....................................................................................................................................3 2.Basics of the Work......................................................................................................................5 2.1Land Cover, Land Use and Land-cover Change....................................................................5 2.2Projection of Land-Cover Change...........................................................................................6 2.3Drivers of Land-Cover and Land-Use Change.....................................................................10 2.4Basics of Scenario Methods..................................................................................................12 3.Conceptual Framework............................................................................................................15 3.1Step1: Definition of the Scenario Context...........................................................................17 3.2Step 2: Identification of Spatial and Dynamic Drivers of Land-Cover Change...............18 3.3Step 3: Scenario Formulation and Projection of Identified Drivers.................................18 3.4Step 4: Scenario-based Projections of Future Land-Cover Change.................................19 4.Implementation and Testing of the Framework..................................................................20 4.1Step 1: Definition of the Scenario Context..........................................................................20 4.2Step 2: Identification of Spatial and Dynamic Drivers for Land-Cover Change..............24 4.3Step 3: Scenario Formulation and Projection of Drivers...................................................28 4.4Step 4: Scenario-based Projections of future Land-Cover Change..................................32 5.Discussion..................................................................................................................................36 5.1Discussion of the Methods....................................................................................................36 5.2Discussion of the Empirical Results.....................................................................................42 6.Conclusions and Outlook........................................................................................................47 7.Reference List............................................................................................................................49 8.Appendix....................................................................................................................................58 8.1Position and Affiliation of the Interviewed Experts...........................................................59 8.2Suitability Maps.......................................................................................................................60 8.3Research Articles.....................................................................................................................66 8.3.1Research Article 1: Retrospective Analysis of Systematic Land-Cover Change in the......... Upper Western Bug River catchment, Ukraine.....................................................................67 8.3.2Research article 2: Cross-Sectoral Projections of Future Land-Cover Change for the........ Upper Western Bug River catchment, Ukraine.....................................................................8

Retrospective Analysis and scenario-based Projection of Land-Cover Change. The Example of the Upper Western Bug river Catchment, Ukraine

Land-cover and land-use change are highly dynamic and contribute to changes in the water balance. The most common changes are urbanisation, deforestation and desertification. This dissertation deals with the topic of projecting land cover (LC) into the near future with the help of the scenario technique. The aim of the thesis is the projection of the urban and rural land-cover change (LCC) till 2025. Two research questions are addressed in this work: (1) Which integrated concept can be developed to combine different methods to project urban and rural LCC into the future based on past LCC? (2) Is it possible to implement the developed concept and does the implementation deliver plausible results? To answer the research questions, a 4-step concept is adopted which serves as workflow for projecting the LCC: (i) the definition of the scenario context, and with that the definition of the study area, (ii) the identification of spatial and dynamic drivers for LCC, consisting of spatial drivers that are location-dependent, such as slope or soil type, and dynamic drivers of LCC, such as demographic and economic development, (iii) scenario formulation and projection of identified drivers, and (iv) scenario-based projections of future LCC, which means its quantitative and spatial modification (demand and allocation). For implementation and testing, the Upper Western Bug River catchment in Ukraine serves as the study site. The extent of the study area reaches from the source of the Western Bug to the Dobrotvir gauging station and is thus entirely located in Ukraine. This presents the first step of the developed concept of the projection of LCC. The existing geo-database for implementation is scarce. LC data is available for the territory of the EU (e.g. CORINE Land Cover) but not for Ukraine. Therefore, the implementation of the second step had to focus on the derivation of LC data for three-time steps to get the basis for the LCC. A classification of satellite scenes of Landsat and SPOT are done for the time steps 1989, 2000, and 2010. The two decades show a huge development of LCC. The increase of ‘artificial surface’ and unmanaged ‘grassland’ is visible with the decrease of ‘arable land’ and ‘forests’. An extended statistical analysis considering the systematic LCC reveals stable transition pathways, which in turn are the basis of the projection of future land cover. This refers to the second step of the concept: change detection. One transition pathway is that ‘arable land’ is not used and converted in settlement areas, but rather changes into ‘grassland’. With the derived LC and the analysed LCC as a basis of the work, the search for spatial and dynamic drivers start at the third time step. A list of dynamic drivers is first compiled, via literature research, and then tested for effect on LCC with statistical analyses. The dynamic driving forces are the ‘Gross Domestic Product’ (GDP) and ‘population development’. Spatial driving forces are laws/planning practices, fertility, slope, distance to the city Lviv, settlements, roads, or rivers. As a result, population development has an effect on the change in the LC class to 'artificial surface' from 'grassland' and 'arable land' from ‘grassland'. The implementation of the third step is done with the help of four storylines where the overall development of the dynamic drivers are included towards 2025. With that it is possible to project them into the future. The fourth step includes the calculation of the demand for each LC class with the projected dynamic drivers. The areas that have a high probability to change into another LC class are determined in suitability maps (allocation) which are derived by translating the transition pathways into GIS algorithms including the spatial driving forces. The class of 'artificial surface' changes the most under scenario A until 2025 and less under scenario D — the sustainable scenario. The LC class 'arable land' decreases in scenario A and B, but has the strongest development in scenario D. The LC class of unmanaged ‘grassland’ is quite stable under scenario A and B, but decreases in C and D. The results of systematic changes in ‘arable land’ that changes into ‘grassland’ are different compared to developments in other countries like Germany. The protection and conservation of arable land is not seen as strongly in other Eastern European countries as it is in the Upper Western Bug River catchment. In turn, the identified spatial and dynamic drivers fit other studies in Eastern Europe. The applied concept of projecting LCC with these steps are highly flexible for implementation in other study sites. However, the volume of work can differ within the steps because of the available databases. In Ukraine the available LCC data was not detailed enough to carry out a future projection. So, a main part of the work is dedicated to the derivation of past LC for different time steps. The involvement of regional experts helped to gain detailed knowledge of processes of LCC. The advantage of the presented concept with the mixture of quantitative (e.g. satellite analyses, statistical analyses) and qualitative methods can overcome methodological knowledge gaps. In addition, the retrospective analyses, as starting points, for the projection of future LCC carves out the site-specific allocation of change.:Acknowledgements..............................................................................................................................III Abstract..................................................................................................................................................IV Zusammenfassung..............................................................................................................................VII Contents..................................................................................................................................................X Abbreviations......................................................................................................................................XIV 1.Introduction.................................................................................................................................1 1.1Background................................................................................................................................1 1.2Objectives and Research Questions.......................................................................................3 1.3Structure....................................................................................................................................3 2.Basics of the Work......................................................................................................................5 2.1Land Cover, Land Use and Land-cover Change....................................................................5 2.2Projection of Land-Cover Change...........................................................................................6 2.3Drivers of Land-Cover and Land-Use Change.....................................................................10 2.4Basics of Scenario Methods..................................................................................................12 3.Conceptual Framework............................................................................................................15 3.1Step1: Definition of the Scenario Context...........................................................................17 3.2Step 2: Identification of Spatial and Dynamic Drivers of Land-Cover Change...............18 3.3Step 3: Scenario Formulation and Projection of Identified Drivers.................................18 3.4Step 4: Scenario-based Projections of Future Land-Cover Change.................................19 4.Implementation and Testing of the Framework..................................................................20 4.1Step 1: Definition of the Scenario Context..........................................................................20 4.2Step 2: Identification of Spatial and Dynamic Drivers for Land-Cover Change..............24 4.3Step 3: Scenario Formulation and Projection of Drivers...................................................28 4.4Step 4: Scenario-based Projections of future Land-Cover Change..................................32 5.Discussion..................................................................................................................................36 5.1Discussion of the Methods....................................................................................................36 5.2Discussion of the Empirical Results.....................................................................................42 6.Conclusions and Outlook........................................................................................................47 7.Reference List............................................................................................................................49 8.Appendix....................................................................................................................................58 8.1Position and Affiliation of the Interviewed Experts...........................................................59 8.2Suitability Maps.......................................................................................................................60 8.3Research Articles.....................................................................................................................66 8.3.1Research Article 1: Retrospective Analysis of Systematic Land-Cover Change in the......... Upper Western Bug River catchment, Ukraine.....................................................................67 8.3.2Research article 2: Cross-Sectoral Projections of Future Land-Cover Change for the........ Upper Western Bug River catchment, Ukraine.....................................................................8

Retrospective Analysis and scenario-based Projection of Land-Cover Change. The Example of the Upper Western Bug river Catchment, Ukraine

Land-cover and land-use change are highly dynamic and contribute to changes in the water balance. The most common changes are urbanisation, deforestation and desertification. This dissertation deals with the topic of projecting land cover (LC) into the near future with the help of the scenario technique. The aim of the thesis is the projection of the urban and rural land-cover change (LCC) till 2025. Two research questions are addressed in this work: (1) Which integrated concept can be developed to combine different methods to project urban and rural LCC into the future based on past LCC? (2) Is it possible to implement the developed concept and does the implementation deliver plausible results? To answer the research questions, a 4-step concept is adopted which serves as workflow for projecting the LCC: (i) the definition of the scenario context, and with that the definition of the study area, (ii) the identification of spatial and dynamic drivers for LCC, consisting of spatial drivers that are location-dependent, such as slope or soil type, and dynamic drivers of LCC, such as demographic and economic development, (iii) scenario formulation and projection of identified drivers, and (iv) scenario-based projections of future LCC, which means its quantitative and spatial modification (demand and allocation). For implementation and testing, the Upper Western Bug River catchment in Ukraine serves as the study site. The extent of the study area reaches from the source of the Western Bug to the Dobrotvir gauging station and is thus entirely located in Ukraine. This presents the first step of the developed concept of the projection of LCC. The existing geo-database for implementation is scarce. LC data is available for the territory of the EU (e.g. CORINE Land Cover) but not for Ukraine. Therefore, the implementation of the second step had to focus on the derivation of LC data for three-time steps to get the basis for the LCC. A classification of satellite scenes of Landsat and SPOT are done for the time steps 1989, 2000, and 2010. The two decades show a huge development of LCC. The increase of ‘artificial surface’ and unmanaged ‘grassland’ is visible with the decrease of ‘arable land’ and ‘forests’. An extended statistical analysis considering the systematic LCC reveals stable transition pathways, which in turn are the basis of the projection of future land cover. This refers to the second step of the concept: change detection. One transition pathway is that ‘arable land’ is not used and converted in settlement areas, but rather changes into ‘grassland’. With the derived LC and the analysed LCC as a basis of the work, the search for spatial and dynamic drivers start at the third time step. A list of dynamic drivers is first compiled, via literature research, and then tested for effect on LCC with statistical analyses. The dynamic driving forces are the ‘Gross Domestic Product’ (GDP) and ‘population development’. Spatial driving forces are laws/planning practices, fertility, slope, distance to the city Lviv, settlements, roads, or rivers. As a result, population development has an effect on the change in the LC class to 'artificial surface' from 'grassland' and 'arable land' from ‘grassland'. The implementation of the third step is done with the help of four storylines where the overall development of the dynamic drivers are included towards 2025. With that it is possible to project them into the future. The fourth step includes the calculation of the demand for each LC class with the projected dynamic drivers. The areas that have a high probability to change into another LC class are determined in suitability maps (allocation) which are derived by translating the transition pathways into GIS algorithms including the spatial driving forces. The class of 'artificial surface' changes the most under scenario A until 2025 and less under scenario D — the sustainable scenario. The LC class 'arable land' decreases in scenario A and B, but has the strongest development in scenario D. The LC class of unmanaged ‘grassland’ is quite stable under scenario A and B, but decreases in C and D. The results of systematic changes in ‘arable land’ that changes into ‘grassland’ are different compared to developments in other countries like Germany. The protection and conservation of arable land is not seen as strongly in other Eastern European countries as it is in the Upper Western Bug River catchment. In turn, the identified spatial and dynamic drivers fit other studies in Eastern Europe. The applied concept of projecting LCC with these steps are highly flexible for implementation in other study sites. However, the volume of work can differ within the steps because of the available databases. In Ukraine the available LCC data was not detailed enough to carry out a future projection. So, a main part of the work is dedicated to the derivation of past LC for different time steps. The involvement of regional experts helped to gain detailed knowledge of processes of LCC. The advantage of the presented concept with the mixture of quantitative (e.g. satellite analyses, statistical analyses) and qualitative methods can overcome methodological knowledge gaps. In addition, the retrospective analyses, as starting points, for the projection of future LCC carves out the site-specific allocation of change.:Acknowledgements..............................................................................................................................III Abstract..................................................................................................................................................IV Zusammenfassung..............................................................................................................................VII Contents..................................................................................................................................................X Abbreviations......................................................................................................................................XIV 1.Introduction.................................................................................................................................1 1.1Background................................................................................................................................1 1.2Objectives and Research Questions.......................................................................................3 1.3Structure....................................................................................................................................3 2.Basics of the Work......................................................................................................................5 2.1Land Cover, Land Use and Land-cover Change....................................................................5 2.2Projection of Land-Cover Change...........................................................................................6 2.3Drivers of Land-Cover and Land-Use Change.....................................................................10 2.4Basics of Scenario Methods..................................................................................................12 3.Conceptual Framework............................................................................................................15 3.1Step1: Definition of the Scenario Context...........................................................................17 3.2Step 2: Identification of Spatial and Dynamic Drivers of Land-Cover Change...............18 3.3Step 3: Scenario Formulation and Projection of Identified Drivers.................................18 3.4Step 4: Scenario-based Projections of Future Land-Cover Change.................................19 4.Implementation and Testing of the Framework..................................................................20 4.1Step 1: Definition of the Scenario Context..........................................................................20 4.2Step 2: Identification of Spatial and Dynamic Drivers for Land-Cover Change..............24 4.3Step 3: Scenario Formulation and Projection of Drivers...................................................28 4.4Step 4: Scenario-based Projections of future Land-Cover Change..................................32 5.Discussion..................................................................................................................................36 5.1Discussion of the Methods....................................................................................................36 5.2Discussion of the Empirical Results.....................................................................................42 6.Conclusions and Outlook........................................................................................................47 7.Reference List............................................................................................................................49 8.Appendix....................................................................................................................................58 8.1Position and Affiliation of the Interviewed Experts...........................................................59 8.2Suitability Maps.......................................................................................................................60 8.3Research Articles.....................................................................................................................66 8.3.1Research Article 1: Retrospective Analysis of Systematic Land-Cover Change in the......... Upper Western Bug River catchment, Ukraine.....................................................................67 8.3.2Research article 2: Cross-Sectoral Projections of Future Land-Cover Change for the........ Upper Western Bug River catchment, Ukraine.....................................................................8

Retrospektive Analyse des systematischen Landbedeckungswandels im Einzugsgebiet des oberen Westlichen Bug, Ukraine

Článek popisuje přístup a empirické výsledky systematických změn v povodí horního Západního Bugu na Ukrajině. Z teledat satelitu Landsat a SPOT je určen zemský povrch v období 1989, 2000 a 2010. Každý časový úsek je přitom zastoupen třemi obrázky z různých ročních období s cílem integrovat vývoj vegetace a opravit klasifikaci pomocí metody maximální věrohodnosti. Jako výsledek bylo detektováno celkem šest tříd: zastavěná plocha (městská), listnatý les, jehličnatý les, zemědělská půda, zeleň a vodstvo. Po učení povrchu v jednotlivých časových úsecích jsou zkoumány změny vzniklé za poslední dvě desetiletí. Pozorovaný pokles a růst je statisticky analyzován, aby mohly být stanoveny systematické a náhodné změny zemského povrchu ve zkoumané oblasti. Výsledky ukazují, že orná půda není přeměněna na zastavěné plochy. Orná půda se mění v travnaté porosty a naopak. Tato systematická změna je velmi silná. V klasifikaci lesních porostů jde především o vzájemnou záměnu, přičemž více dochází k přeměně lesů jehličnatých na listnaté.W artykule przedstawiono podejście do tematyki ukierunkowanych zmian pokrycia terenu oraz empiryczne wyniki badań przeprowadzonych w zlewni górnego Zachodniego Bugu na Ukrainie. Pokrycie terenu zostało określone przy pomocy danych Teledetekcji Landsat i SPOT dla lat 1989, 2000 i 2010. Każdy krok czasowy reprezentowany jest przy pomocy trzech zdjęć satelitarnych służących uchwyceniu rozwoju roślinności oraz poprawie klasyfikacji metodą nawiększej wiarygodności. W rezultacie wyodrębniono sześć klas: teren zabudowany (miejski), las liściasty, las iglasty, grunty orne, łąki oraz wody. Na podstawie badań pokrycia terenu dla poszczególnych etapów czasowych określono zmiany w nim zachodzące na przestrzeni dwóch dekad. Obserwowany spadek i wzrost analizowanych klas poddany został statystycznej analizie w celu identyfikacji ukierunkowanych i losowych zmian pokrycia terenu na badanym obszarze. Wyniki pokazują, że grunty orne nie zostają przekształcane w tereny zabudowane. Grunty orne zamienia się w łąki i odwrotnie.The paper presents the approach and empirical findings of a study on systematic land-cover change in the upper Western Bug River catchment in Ukraine. Landsat and SPOT images as remote sensing data are used for land-cover0 classification for the time steps 1989, 2000 and 2010. Thereby, three inner-annual scenes represent the vegetation development for each time step and facilitate classification with the Maximum Likelihood Classifier. Six classes are detected: artificial surface, broad-leaved and coniferous forests, arable land, grassland and water bodies. After this step, land-cover change detection over two decades is conducted. The observed against the expected gross loss and gross gain are statistically analyzed to identify the systematic and random land-cover changes in the study region. Results show that arable land changes not into artificial surface. Arable land changes into grassland and vice versa. This systematic change is very strong. The forest classes interchange whereat broad-leaved forest gains more from coniferous forest in the last decade.Der Artikel zeigt die Herangehensweise und empirische Ergebnisse des systematischen Landbedeckungswandels im Einzugsgebiet des oberen Westlichen Bugs in der Ukraine. Aus Fernerkundungsdaten der Satelliten Landsat und SPOT wird die Landbedeckung für die Zeitschritte 1989, 2000 und 2010 bestimmt. Dabei wird jeder einzelne Zeitschritt mithilfe von drei innerjährlichen Bildern repräsentiert, um die Vegetationsentwicklung zu berücksichtigen und die Klassifikation mit dem Maximum Likelihood Classificator zu verbessern. Als Ergebnis wurden sechs Klassen detektiert: versiegelte bebaute Fläche (städtisch), Laubwald, Nadelwald, Ackerfläche, Grünland und Wasser. Nach der Ermittlung der Landbedeckung in den einzelnen Zeitschritten wird der Wandel über die zwei Jahrzehnte untersucht. Der beobachtete Rückgang und Zuwachs wird statistisch analysiert, um den systematischen und zufälligen Landbedeckungswandel für das Untersuchungsgebiet zu identifizieren. Die Ergebnisse zeigen, dass Ackerfläche nicht in bebaute Fläche umgewandelt wird. Ackerfläche geht in Grünland über und umgekehrt. Dieser systematische Wandel ist sehr stark ausgeprägt. In den beiden Waldklassen gibt es hauptsächlich einen wechselseitige Nutzungsänderung, wobei in den letzten zehn Jahren mehr Nadelwald in Laubwald umgewandelt wird

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue

Geospatial input data for the PALM model system 6.0: model requirements, data sources, and processing

The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized format. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This article shows how input data sets for three German cities were derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data

Prognostic implications of NOTCH1 and FBXW7 mutations in adult acute T-lymphoblastic leukemia

NOTCH1 mutations are found in more than 50 % of patients with T-lineage acute lymphoblastic leukemia (TALL), and have been associated with a favorable outcome in pediatric T-ALL. FBXW7 mutations are found in a small subset of T-ALL, sometimes overlapping with the presence of NOTCH1 mutations. In this study, Baldus and colleagues investigated the prognostic impact of NOTCH1 and FBXW7 mutations in adult T-ALL. See related perspective article on page 1338