The Influence of Topography on the Global Terrestrial Water Cycle
Bibliographic record
Abstract
Abstract Topography affects the distribution and movement of water on Earth, yet new insights about topographic controls continue to surprise us and exciting puzzles remain. Here we combine literature review and data synthesis to explore the influence of topography on the global terrestrial water cycle, from the atmosphere down to the groundwater. Above the land surface, topography induces gradients and contrasts in water and energy availability. Long‐term precipitation usually increases with elevation in the mid‐latitudes, while it peaks at low‐ to mid‐elevations in the tropics. Potential evaporation tends to decrease with elevation in all climate zones. At the land surface, topography is expressed in snow distribution, vegetation zonation, geomorphic landforms, the critical zone, and drainage networks. Evaporation and vegetation activity are often highest at low‐ to mid‐elevations where neither temperature, nor energy availability, nor water availability—often modulated by lateral moisture redistribution—impose strong limitations. Below the land surface, topography drives the movement of groundwater from local to continental scales. In many steep upland regions, groundwater systems are well connected to streams and provide ample baseflow, and streams often start losing water in foothills where bedrock transitions into highly permeable sediment. We conclude by presenting organizing principles, discussing the implications of climate change and human activity, and identifying data needs and knowledge gaps. A defining feature resulting from topography is the presence of gradients and contrasts, whose interactions explain many of the patterns we observe in nature and how they might change in the future.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".