Forest ecohydrological research in the 21st century: what are the critical needs?
Bibliographic record
Abstract
Abstract Modern ecohydrologic science will be critical for providing the best information to policy makers and society to address water resource challenges in the 21st century. Implicitly, ecohydrology involves understanding both the functional interactions among vegetation, soils, and hydrologic processes at multiple scales and the linkages among upland, riparian, and aquatic components. In this paper, we review historical and contemporary ecohydrologic science, focusing on watershed structure and function and the threats to watershed structure and function. Climate change, land use change, and invasive species are among the most critical contemporary issues that affect water quantity and quality, and a mechanistic understanding of watershed ecosystem structure and function is required to understand their impacts on water quantity and quality. Economic and social values of ecosystem services such as water supply from forested watersheds must be quantified in future research, as land use decisions that impact ecohydrologic function are driven by the interplay among economic, social, political, and biological constraints. Future forest ecohydrological research should focus on: (1) understanding watershed responses to climate change and variability, (2) understanding watershed responses to losses of native species or additions of non‐native species, (3) developing integrated models that capitalize on long‐term data, (4) linking ecohydrologic processes across scales, and (5) managing forested watersheds to adapt to climate change. We stress that this new ecohydrology research must also be integrated with socio‐economic disciplines. Published in 2011. This article is a US Government work and is in the public domain in the USA.
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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".