Urgent scientifically-informed action is needed to stabilise the Earth System amidst anthropogenic climate change. Particularly, the notable transgression of the ‘biosphere integrity’ Planetary Boundary needs to be addressed. Modern Earth System Models struggle to accurately represent regional to local-scale climate features and biodiversity aspects. Recent developments allow to tackle these issues using Artificial Intelligence.
This dissertation focuses on two main aspects: (i) deriving high spatio-temporal resolution climate data from coarser models; and (ii) integrating high-temporal-resolution climate data into Species Distribution Models. Three specific objectives were defined:
Obj1 Improving Perfect Prognosis – Statistical Downscaling methods through modern Deep Learning algorithms.
Obj2 Downscaling a high-resolution multivariate climate ensemble.
Obj3 Employ the resulting dataset to improve Species Distribution Models’ projections.
The objectives are connected to the three articles that support this cumulative dissertation. Its scope is limited to the Free State of Saxony, Germany, where local high-resolution climate data and high-quality observations of endangered vascular plant species were employed. From a broader perspective, these efforts should contribute to the overarching goal of bridging the gap between the scales of species distribution and climate models while establishing open-source, reproducible, and scalable containerised frameworks.
Recent Deep Learning algorithms were leveraged to accomplish (i). The proposed frameworks enhance previous performance of Perfect Prognosis – Statistical Downscaling approaches, while ensuring repeatability. The key near-surface variables considered are precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature. The assumptions that support the Perfect Prognosis approach were thoroughly examined, confirming the robustness of the methods. The downscaled ensemble exhibits a novel output resolution of daily 1 km, which can serve as input for multiple climate impact studies, especially for local-scale decision-making and in topographically complex regions.
Considerable methodological implementations were proposed and thoroughly analysed to achieve (ii). Despite notable limitations, Species Distribution Models are frequently used in climate change conservation planning. Thus, recent developments in climate data resolution could improve their usefulness and reliability, which have been previously constraint to coarse temporal aggregates in the projection domain. The presented framework provides fine-grained species suitability projections and satisfactory spatio-temporal transferability, albeit worrying trends. These improved projections are a step forward towards tailored conservation efforts.
Limitations of Machine Learning methods and Species Distribution Models are addressed. Substantial avenues for future improvements are thoroughly discussed. As results suggest further reduction of suitable habitats, yet another call for swift action towards low-carbon societies is made. This requires maximising climate change mitigation and adaptation measures, along with a swift transition from short-term profit-driven policies to long-term sustainable development, but primarily, a collective shift in consciousness from anthropocentric positions to ecocentric policies and societies.:Contents
Declaration of conformity........................................................ I
Abstract....................................................................... III
Zusammenfassung.................................................................. V
Resumen........................................................................ VII
Acknowledgments................................................................. IX
List of Figures................................................................. XV
List of Tables................................................................. XIX
Symbols and Acronyms........................................................... XXI
I Prelude & Foundations 1
1 Introduction................................................................... 3
1.1 Motivation – Human Impact on Earth....................................... 3
1.2 Earth System Modelling and Downscaling................................... 5
1.3 Biosphere’s Response to Recent Changes................................... 8
1.4 Species Distribution Models.............................................. 9
1.5 Objectives.............................................................. 10
1.6 Scope................................................................... 10
1.7 Outline................................................................. 10
2 Methodological Basis.......................................................... 13
2.1 Introduction to Artificial Intelligence Methods......................... 13
2.1.1 Artificial Intelligence........................................... 13
2.1.2 Machine Learning.................................................. 14
2.1.3 Deep Learning..................................................... 14
2.2 Downscaling Techniques.................................................. 15
2.2.1 Dynamical Downscaling............................................. 15
2.2.2 Statistical Downscaling........................................... 15
2.2.2.1 Model Output Statistics................................... 16
2.2.2.2 Perfect Prognosis......................................... 16
2.3 Species Distribution Models: Temporal Aspects........................... 17
2.4 Computational Framework................................................. 18
2.4.1 High-Performance Computing........................................ 18
2.4.2 Containers........................................................ 18
2.5 Remarks on Reproducibility.............................................. 19
II Articles’ Synthesis 21
3 Data.......................................................................... 23
3.1 Study Area.............................................................. 23
3.2 ReKIS................................................................... 24
3.3 ERA5.................................................................... 24
3.4 CORDEX.................................................................. 24
3.5 Species Occurrences..................................................... 25
3.6 WorldClim............................................................... 26
4 Methodological Implementations................................................ 27
4.1 Advancing Statistical Downscaling....................................... 27
4.1.1 Transfer Function Calibration.................................... 27
4.1.2 Evaluation....................................................... 29
4.1.3 Repeatability.................................................... 29
4.2 Downscaling a Multivariate Ensemble..................................... 30
4.2.1 Transfer Function Adaptations.................................... 30
4.2.2 Validation....................................................... 30
4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31
4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32
4.3.1 Climate Data..................................................... 32
4.3.1.1 Predictor Sets.......................................... 32
4.3.1.2 Temporal Approaches..................................... 33
4.3.2 SDM Implementation............................................... 33
4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33
4.3.4 Meta-analysis.................................................... 34
4.3.5 Pseudo-Reality Assessment........................................ 34
4.3.6 Spatio-Temporal Transferability.................................. 34
5 Results & Discussions......................................................... 35
5.1 Advancing Statistical Downscaling....................................... 35
5.1.1 Performance Improvement.......................................... 35
5.1.2 Repeatability.................................................... 36
5.1.3 Transfer Function Suitability.................................... 38
5.2 Downscaling a Multivariate Ensemble..................................... 39
5.2.1 Transfer Function performance.................................... 39
5.2.2 Bias-Correction.................................................. 40
5.2.3 Pseudo-Reality................................................... 42
5.2.4 Projections...................................................... 43
5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45
5.3.1 Predictor Set Evaluation for H2k................................. 45
5.3.2 Temporal Approach Comparison..................................... 46
5.3.3 Spatio-Temporal Transferability.................................. 47
5.3.4 Suitability Projections.......................................... 47
III Insights 51
6 Summary....................................................................... 53
6.1 Article A1.............................................................. 53
6.2 Article A2.............................................................. 54
6.3 Article A3.............................................................. 56
7 Conclusions and Outlook....................................................... 59
References 65
Articles 81
A1 Repeatable high-resolution statistical downscaling through deep learning..... 83
A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble
in Complex Terrain............................................................. 103
A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu-
tion Model..................................................................... 127 / Um das Erdsystem angesichts des anthropogenen Klimawandels zu stabilisieren, sind Maßnahmen auf Basis wissenschaftlicher Erkenntnisse dringend erforderlich. Insbesondere muss die drastisch Überschreitung der planetaren Grenze ‘Integrität der Biosphäre’ angegangen werden. Bisher haben aber Modelle des Erdsystems Schwierigkeiten, regionale bis lokale Klimamerkmale und Aspekte der Biodiversität genau abzubilden. Aktuelle Entwicklungen ermöglichen es, diese Herausforderungen mithilfe von Künstlicher Intelligenz anzugehen.
Diese Dissertation konzentriert sich auf zwei Hauptaspekte: (i) die Ableitung von Klimadaten mit hoher räumlicher und zeitlicher Auflösung aus groberen Modellen und (ii) die Integration von Klimadaten mit hoher zeitlicher Auflösung in Modelle zur Artverbreitung. Es wurden drei konkrete Ziele definiert:
Ziel1 Verbesserung von Perfect Prognosis – Statistische Downscaling-Methoden durch moderne Deep Learning-Algorithmen
Ziel2 Downscaling eines hochauflösenden multivariaten Klimaensembles
Ziel3 Verwendung des resultierenden Datensatzes zur Verbesserung von Prognosen in Modellen zur Artverbreitung
Diese Ziele werden in drei wissenschaftlichen Artikeln beantwortet, auf die diese kumulative Dissertation sich stützt. Der Anwendungsbereich erstreckt sich auf den Freistaat Sachsen, Deutschland, wo lokale hochauflösende Klimadaten und hochwertige Beobachtungen gefährdeter Gefäßpflanzenarten verwendet wurden. In einer breiteren Perspektive tragen diese Bemühungen dazu bei, die Kluft zwischen regionalen sowie zeitlichen Skalen der Artverbreitung und Klimamodellen zu überbrücken und gleichzeitig Open-Source-, reproduzierbare und skalierbare containerisierte Frameworks zu etablieren.
Aktuelle Deep Learning-Algorithmen wurden eingesetzt, um Hauptaspekt (i) zu erreichen. Die vorgeschlagenen Frameworks verbessern die bisherige Leistung von Perfect Prognosis – Statistische Downscaling-Ansätzen und gewährleisten gleichzeitig die Wiederholbarkeit. Die wichtigsten bodennahen Variablen, die berücksichtigt werden, sind Niederschlag, Wasserdampfdruck, Strahlung, Windgeschwindigkeit sowie Maximal-, Durchschnitts- und Minimaltemperatur. Die Annahmen, die den Perfect Prognosis-Ansatz unterstützen, wurden analysiert und bestätigen die Robustheit der Methoden. Das downscaled Ensemble weist eine neuartige Auflösung von 1 km auf Tagesbasis auf, welches als Grundlage für mehrere Studien zu den Auswirkungen des Klimawandels dienen kann, insbesondere für Entscheidungsfindung auf lokaler Ebene und in topografisch komplexen Regionen.
Es wurden umfassende methodische Implementierungen vorgeschlagen und analysiert, um Hauptaspekt (ii) zu erreichen. Trotz großer Einschränkungen werden Modelle zur Artverbreitung häufig in der Klimaschutzplanung eingesetzt. Daher könnten aktuelle Entwicklungen in der Klimadatenauflösung deren Nützlichkeit und Zuverlässigkeit verbessern, die bisher auf grobe zeitliche Aggregatformen im Projektionsbereich beschränkt waren. Das vorgestellte Framework bietet feingliedrige Prognosen zur Eignung von Arten und zufriedenstellende räumlich-zeitliche Übertragbarkeit, trotz besorgniserregender Trends. Diese verbesserten Prognosen sind ein Schritt in Richtung maßgeschneiderter Naturschutzmaßnahmen.
Einschränkungen von Machine Learning-Methoden und Modellen zur Artverbreitung werden untersucht. Substanzielle Möglichkeiten zur zukünftigen Verbesserung werden ausführlich erörtert. Da die Ergebnisse darauf hinweisen, dass geeignete Lebensräume weiter abnehmen, wird erneut zum schnellen Handeln in Richtung kohlenstoffarmer Gesellschaften aufgerufen. Dies erfordert die Maximierung von Maßnahmen zur Bekämpfung des Klimawandels und zur Anpassung, zusammen mit einem raschen Übergang von kurzfristig Profitorientierten Politiken zu langfristiger nachhaltiger Entwicklung, aber vor allem zu einem kollektiven Bewusstseinswandel von anthropozentrischen Positionen zu ökozentrischen Politiken und Gesellschaften.:Contents
Declaration of conformity........................................................ I
Abstract....................................................................... III
Zusammenfassung.................................................................. V
Resumen........................................................................ VII
Acknowledgments................................................................. IX
List of Figures................................................................. XV
List of Tables................................................................. XIX
Symbols and Acronyms........................................................... XXI
I Prelude & Foundations 1
1 Introduction................................................................... 3
1.1 Motivation – Human Impact on Earth....................................... 3
1.2 Earth System Modelling and Downscaling................................... 5
1.3 Biosphere’s Response to Recent Changes................................... 8
1.4 Species Distribution Models.............................................. 9
1.5 Objectives.............................................................. 10
1.6 Scope................................................................... 10
1.7 Outline................................................................. 10
2 Methodological Basis.......................................................... 13
2.1 Introduction to Artificial Intelligence Methods......................... 13
2.1.1 Artificial Intelligence........................................... 13
2.1.2 Machine Learning.................................................. 14
2.1.3 Deep Learning..................................................... 14
2.2 Downscaling Techniques.................................................. 15
2.2.1 Dynamical Downscaling............................................. 15
2.2.2 Statistical Downscaling........................................... 15
2.2.2.1 Model Output Statistics................................... 16
2.2.2.2 Perfect Prognosis......................................... 16
2.3 Species Distribution Models: Temporal Aspects........................... 17
2.4 Computational Framework................................................. 18
2.4.1 High-Performance Computing........................................ 18
2.4.2 Containers........................................................ 18
2.5 Remarks on Reproducibility.............................................. 19
II Articles’ Synthesis 21
3 Data.......................................................................... 23
3.1 Study Area.............................................................. 23
3.2 ReKIS................................................................... 24
3.3 ERA5.................................................................... 24
3.4 CORDEX.................................................................. 24
3.5 Species Occurrences..................................................... 25
3.6 WorldClim............................................................... 26
4 Methodological Implementations................................................ 27
4.1 Advancing Statistical Downscaling....................................... 27
4.1.1 Transfer Function Calibration.................................... 27
4.1.2 Evaluation....................................................... 29
4.1.3 Repeatability.................................................... 29
4.2 Downscaling a Multivariate Ensemble..................................... 30
4.2.1 Transfer Function Adaptations.................................... 30
4.2.2 Validation....................................................... 30
4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31
4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32
4.3.1 Climate Data..................................................... 32
4.3.1.1 Predictor Sets.......................................... 32
4.3.1.2 Temporal Approaches..................................... 33
4.3.2 SDM Implementation............................................... 33
4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33
4.3.4 Meta-analysis.................................................... 34
4.3.5 Pseudo-Reality Assessment........................................ 34
4.3.6 Spatio-Temporal Transferability.................................. 34
5 Results & Discussions......................................................... 35
5.1 Advancing Statistical Downscaling....................................... 35
5.1.1 Performance Improvement.......................................... 35
5.1.2 Repeatability.................................................... 36
5.1.3 Transfer Function Suitability.................................... 38
5.2 Downscaling a Multivariate Ensemble..................................... 39
5.2.1 Transfer Function performance.................................... 39
5.2.2 Bias-Correction.................................................. 40
5.2.3 Pseudo-Reality................................................... 42
5.2.4 Projections...................................................... 43
5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45
5.3.1 Predictor Set Evaluation for H2k................................. 45
5.3.2 Temporal Approach Comparison..................................... 46
5.3.3 Spatio-Temporal Transferability.................................. 47
5.3.4 Suitability Projections.......................................... 47
III Insights 51
6 Summary....................................................................... 53
6.1 Article A1.............................................................. 53
6.2 Article A2.............................................................. 54
6.3 Article A3.............................................................. 56
7 Conclusions and Outlook....................................................... 59
References 65
Articles 81
A1 Repeatable high-resolution statistical downscaling through deep learning..... 83
A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble
in Complex Terrain............................................................. 103
A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu-
tion Model..................................................................... 127 / Acción urgente científicamente informada es necesaria para estabilizar el sistema terrestre en medio del cambio climático antropogénico. En particular, la notable transgresión del límite planetario de ’integridad de la biosfera’ debe abordarse. Los modernos modelos del sistema terrestre tienen dificultades para representar con precisión las características climáticas a escala regional y local, así como los aspectos de la biodiversidad. Desarrollos recientes permiten abordar estos problemas mediante la inteligencia artificial.
Esta disertación se enfoca en dos aspectos principales: (i) derivar datos climáticos de alta resolución espacio-temporal a partir de modelos más gruesos; y (ii) integrar datos climáticos de alta resolución temporal en modelos de distribución de especies. Se definieron tres objetivos específicos:
Obj1 Mejorar los métodos de pronóstico perfecto – reducción de escala estadística mediante algoritmos modernos de aprendizaje profundo.
Obj2 Generar un conjunto climático multivariado de alta resolución.
Obj3 Emplear el conjunto de datos resultante para mejorar las proyecciones de los modelos de distribución de especies.
Los objetivos están vinculados a los tres artículos que respaldan esta disertación acumulativa. Su alcance se limita al Estado Libre de Sajonia, Alemania, donde se emplearon datos climáticos locales de alta resolución y observaciones de alta calidad de especies de plantas vasculares en peligro de extinción. Desde una perspectiva más amplia, estos esfuerzos deberían contribuir a la meta general de cerrar la brecha entre las escalas de la distribución de especies y los modelos climáticos, mientras que se establecen marcos de trabajo contenedorizados de código abierto, reproducibles y escalables.
Algoritmos recientes de aprendizaje profundo fueron aprovechados para lograr (i). Los marcos de trabajo propuestos mejoran el rendimiento previo de los métodos de pronóstico perfecto – reducción de escala estadística, al tiempo que garantizan la repetibilidad. Las variables clave de la superficie cercana consideradas son precipitación, presión de vapor de agua, radiación, velocidad del viento, así como la temperatura máxima, media y mínima. Se examinaron meticulosamente las suposiciones que respaldan el método de pronóstico perfecto, confirmando la robustez de las propuestas. El conjunto reducido de escala exhibe una novedosa resolución diaria de 1 km, el cual puede servir como insumo para múltiples estudios de impacto climático, especialmente para la toma de decisiones a nivel local y en regiones topográficamente complejas.
Se propusieron y analizaron minuciosamente considerables implementaciones metodológicas para lograr (ii). A pesar de sus notables limitaciones, los modelos de distribución de especies son utilizados con frecuencia en la planificación de la conservación debido al cambio climático. Por lo tanto, los desarrollos recientes en la resolución de datos climáticos podrían mejorar su utilidad y confiabilidad, ya que antes se limitaban a agregados temporales gruesos en el caso de las proyecciones. El marco de trabajo presentado proporciona proyecciones de idoneidad de especies detalladas y una transferibilidad espacio-temporal satisfactoria, aunque con tendencias preocupantes. Estas proyecciones mejoradas son un paso adelante en los esfuerzos de conservación a la medida.
Se abordan las limitaciones de los métodos de aprendizaje automático y de los modelos de distribución de especies. Se discuten a fondo posibilidades sustanciales para futuras mejoras. Dado que los resultados sugieren una mayor reducción de hábitats adecuados, se hace otro llamado a la acción rápida hacia sociedades bajas en carbono. Esto requiere maximizar las medidas de mitigación y adaptación al cambio climático, junto con una transición rápida de políticas orientadas a beneficios a corto plazo hacia un desarrollo sostenible a largo plazo, pero principalmente, un cambio colectivo de conciencia, desde posiciones antropocéntricas hacia políticas y sociedades ecocéntricas.:Contents
Declaration of conformity........................................................ I
Abstract....................................................................... III
Zusammenfassung.................................................................. V
Resumen........................................................................ VII
Acknowledgments................................................................. IX
List of Figures................................................................. XV
List of Tables................................................................. XIX
Symbols and Acronyms........................................................... XXI
I Prelude & Foundations 1
1 Introduction................................................................... 3
1.1 Motivation – Human Impact on Earth....................................... 3
1.2 Earth System Modelling and Downscaling................................... 5
1.3 Biosphere’s Response to Recent Changes................................... 8
1.4 Species Distribution Models.............................................. 9
1.5 Objectives.............................................................. 10
1.6 Scope................................................................... 10
1.7 Outline................................................................. 10
2 Methodological Basis.......................................................... 13
2.1 Introduction to Artificial Intelligence Methods......................... 13
2.1.1 Artificial Intelligence........................................... 13
2.1.2 Machine Learning.................................................. 14
2.1.3 Deep Learning..................................................... 14
2.2 Downscaling Techniques.................................................. 15
2.2.1 Dynamical Downscaling............................................. 15
2.2.2 Statistical Downscaling........................................... 15
2.2.2.1 Model Output Statistics................................... 16
2.2.2.2 Perfect Prognosis......................................... 16
2.3 Species Distribution Models: Temporal Aspects........................... 17
2.4 Computational Framework................................................. 18
2.4.1 High-Performance Computing........................................ 18
2.4.2 Containers........................................................ 18
2.5 Remarks on Reproducibility.............................................. 19
II Articles’ Synthesis 21
3 Data.......................................................................... 23
3.1 Study Area.............................................................. 23
3.2 ReKIS................................................................... 24
3.3 ERA5.................................................................... 24
3.4 CORDEX.................................................................. 24
3.5 Species Occurrences..................................................... 25
3.6 WorldClim............................................................... 26
4 Methodological Implementations................................................ 27
4.1 Advancing Statistical Downscaling....................................... 27
4.1.1 Transfer Function Calibration.................................... 27
4.1.2 Evaluation....................................................... 29
4.1.3 Repeatability.................................................... 29
4.2 Downscaling a Multivariate Ensemble..................................... 30
4.2.1 Transfer Function Adaptations.................................... 30
4.2.2 Validation....................................................... 30
4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31
4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32
4.3.1 Climate Data..................................................... 32
4.3.1.1 Predictor Sets.......................................... 32
4.3.1.2 Temporal Approaches..................................... 33
4.3.2 SDM Implementation............................................... 33
4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33
4.3.4 Meta-analysis.................................................... 34
4.3.5 Pseudo-Reality Assessment........................................ 34
4.3.6 Spatio-Temporal Transferability.................................. 34
5 Results & Discussions......................................................... 35
5.1 Advancing Statistical Downscaling....................................... 35
5.1.1 Performance Improvement.......................................... 35
5.1.2 Repeatability.................................................... 36
5.1.3 Transfer Function Suitability.................................... 38
5.2 Downscaling a Multivariate Ensemble..................................... 39
5.2.1 Transfer Function performance.................................... 39
5.2.2 Bias-Correction.................................................. 40
5.2.3 Pseudo-Reality................................................... 42
5.2.4 Projections...................................................... 43
5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45
5.3.1 Predictor Set Evaluation for H2k................................. 45
5.3.2 Temporal Approach Comparison..................................... 46
5.3.3 Spatio-Temporal Transferability.................................. 47
5.3.4 Suitability Projections.......................................... 47
III Insights 51
6 Summary....................................................................... 53
6.1 Article A1.............................................................. 53
6.2 Article A2.............................................................. 54
6.3 Article A3.............................................................. 56
7 Conclusions and Outlook....................................................... 59
References 65
Articles 81
A1 Repeatable high-resolution statistical downscaling through deep learning..... 83
A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble
in Complex Terrain............................................................. 103
A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu-
tion Model..................................................................... 127
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:91339 |
| Date | 16 May 2024 |
| Creators | Quesada Chacón, Dánnell |
| Contributors | Bernhofer, Christian, Reyer, Christopher P.O., Karger, Dirk N., Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | info:eu-repo/grantAgreement/Europäischer Sozialfonds/Landesinnovationspromotion/100380876//LIP/KAKO2019_TUD, info:eu-repo/grantAgreement/Freistaat Sachsen/Landesinnovationspromotion/100380876//LIP/KAKO2019_TUD |
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