Parsimonious spatial representation of tropical soils within dynamic rainfall-runoff models
Bibliographic record
Abstract
Introduction Models are used increasingly to simulate hydrological processes within tropical regions. There is now a wealth of publications addressing evaporation modelling (particularly wet-canopy evaporation) of local areas of tropical forest in, for example, Niger (Gash et al., 1997), Guyana (Jetten, 1996), Puerto Rico (Schellekens et al., 1999), Columbia (Marin et al., 2000) and Indonesia (Asdak et al., 1999; van Dijk and Bruijnzeel, 2001). Elsewhere in this volume, Roberts et al. provide an overview of evaporation processes and modelling. Other modelling studies have addressed the impact of such tropical evaporation on regional climates and global circulation (e.g. Polcher and Laval, 1994; Zeng, 1999; Zeng and Neelin, 1999; Zheng et al., 2001). New studies using time-series models are highlighting the effects of cycles in the rainfall, such as the El Nino Southern Oscillation (ENSO) on tropical evaporation, riverflow and water quality (e.g. Zeng, 1999; Chappell et al., 2001; Krishnaswamy et al., 2001; Whitaker et al., 2001; Chappell, Tych et al., this volume). Similarly, models that simulate the generation of riverflow from the rainfall received by a tropical catchment are also beginning to be applied more frequently. These models include: Metric-conceptual models of waterflow, based upon transfer functions. For example, application of the DBM modelling approach to a nested catchment system in Malaysian Borneo (Chappell et al., 1999a) and the application of IHACRES to a large Thai basin (Scoccimarro et al., 1999). Conceptual models of waterflow based upon stores and pre-determined empirical relationships. For example, application of the Nash model to Kenyan catchments (Onyando and Sharma, 1995), the Modhydrolog model to a tropical catchment (Chiew et al., 1996), the Reservoir-Water-Balance-Simulation model to Namibian catchments (Hughes and Metzler, 1998), and the HBV-96 model (discussed in Barnes and Bonell, this volume) to catchments in Zimbabwe, Tanzania and Bolivia (Liden and Harlin, 2000).[&]. © UNESCO 2005 and Cambridge University Press, 2009.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".