MODELING LAND SURFACE HETEROGENEITY IN LAND SURFACE AND REGIONAL CLIMATE MODELS
Bibliographic record
Abstract
We all live on Earth’s land surface. The state of and changes to land surface conditions can strongly alter surface energy and water balance, eventually affecting the weather and climate. An essential component in regional climate models and Earth system models, the land surface provides lower boundary conditions, which are critical both for weather forecasting and projecting the future climate. This research advances knowledge in representing land surface heterogeneity, including the energy-water-carbon cycle and land surface feedback to the regional climate in Central North America, where land use and hydrological conditions are complex. An extensive area of fine-scale surface heterogeneity, this region includes the U.S. corn belt agricultural land and wetlands that dominate the landscape in the Prairie Pothole Region (PPR) across the Northern Great Plains and Canadian Prairies. This study highlights two distinct landscapes—wetlands and croplands—for their dominance in the region, important roles in land-atmosphere interaction, and unique characteristics impacted by human activities. In addition, advances in high-resolution convection-permitting models provide a unique opportunity to investigate these interactions, especially to explicitly resolve land surface heterogeneity. \n\nThis thesis first investigates the soil moisture conditions of the land and their feedback to extreme temperatures during heatwave events in a long-term high-resolution convection-permitting simulation. Second, a joint crop-irrigation simulation is conducted, which shows the capability of land surface models (LSMs) to estimate crop phenology and biomass and irrigation, the key impacts of human decisions. Third, the thesis explores the shallow groundwater dynamics and the hydrological cycle in the PPR under current and future climate change scenarios; fourth, the soil moisture conditions from the current and future climate are used to statistically estimate the future distribution of the prairie wetlands. Finally, a surface wetland scheme is developed to represent spatial wetland extents and dynamic wetland storage in the PPR. This scheme is incorporated into an LSM (Noah-MP) and regional climate model (Weather Research & Forecasting model) to study its impacts on energy-water balance and feedback to the regional climate. This research allows potential future research on the wetland-climate feedback at a local/regional scale and on the potential on-farm benefits of wetland retention and restoration. This research has critical implications for understanding the land and climate interactions in this unique and complex terrain and has potential to help human beings to develop a sustainable lifestyle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".