Development of a Global Statistical Framework for Estimating Landscape Freeze-Thaw under Changing Climate Conditions with Application to Québec
Bibliographic record
Abstract
Seasonal Freeze-Thaw (FT) dynamics are among the most important landscape processes, influencing ground thermal and hydrological characteristics across cold regions. These impacts can further affect the environmental processes, socio-economic, and cultural activities developed around the use of lands and waters. From a physical perspective and at the large spatial and regional scales, temperature and snow depth are the key drivers of variability in the timing and distribution of FT dynamics. However, climate change has significantly affected both temperature and snow depth over the past decades, and thus FT dynamics in time and space. As a result, it is of a great importance to understand and quantify the control of changing climate on FT characteristics and project future states of FT characteristics subject to future climatic projections. This knowledge and modeling capability can provide an invaluable information for agricultural activities, infrastructure design and maintenance, monitoring the ecosystem’s livelihood, and estimating land-induced greenhouse gas emissions due to permafrost degradation. This thesis provides a generic and globally relevant statistical framework to quantify the compound control of temperature and snow depth on FT dynamics over different spatiotemporal scales. A set of gridded observed temperature and snow depth data with the remotely sensed state of the frozen soil are utilized as basis for understanding the climate control on FT dynamics and their spatiotemporal variability. Using these gridded data records, it is possible to greatly overcome the limitations in using station-based data, particularly at higher latitudes. Utilizing future projections of climate models along with a rigorous data processing step and different statistical methodologies, future projections of precipitation type, snow depth, and FT can be obtained over the required spatial and temporal resolutions. Although statistical models have been widely used to address the impact of changing climate on different environmental processes, to the best of our knowledge, there was no previously known formal framework to pair the hydroclimatic data with FT patterns. In this work, bivariate and multivariate copula methodologies are used to statistically model the interdependencies between a relevant set of hydroclimatic variables and different FT characteristics and consequently quantify the control of climate on FT patterns over observed and future time episodes. By considering Québec as a case study, it is demonstrated that how this methodology can be fused with available bottom-up and top-down impact assessment approaches. In the bottom-up impact assessment approach, the response of FT characteristics to a range of feasible future climate conditions is quantified using various forms of stress tests in the form of what-if scenarios. To implement the model in the context of top-down impact assessments, a dynamic copula model with time-varying parameterization is advised and accordingly, future FT characteristics are projected conditional to future air temperature and snow depth in Québec.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".