Using a Hydrological Model to Understand the Hydrological Processes in a Mosaic Tropical Peatland Landscape of Pulau Padang, Indonesia
Bibliographic record
Abstract
Tropical peatlands play an important role in addressing the climate and nature functions. In these ecosystems, hydrology strongly controls their geomorphology, ecology, and carbon cycle. More frequent and severe droughts driven by climate extremes (e.g. El Niño Southern Oscillation and the Indian Ocean Dipole events) may alter their local hydrology. In addition, growing dependencies on tropical peatlands due to population growth and economic development has resulted in land-cover change. Alteration in the hydrological processes under changing climate and land-cover may have crucial implications on tropical peatlands, but such impacts remain poorly understood. In this context, we used a coupled MIKE SHE and MIKE Hydro River model to represent the hydrological processes within Pulau Padang (~1,100 km2), a peat-dominated island in the eastern coast of Sumatra, Indonesia. The island is a mosaic landscape of peat swamp forest, smallholder area, and industrial plantation. We collected a comprehensive vegetation and peat properties data from field measurements, supported by high-resolution digital terrain model derived from airborne LiDAR, for the model setup. We calibrated and validated the model against observed groundwater level and stream flow data distributed across the island. Finally, we also evaluated the impacts of land-cover change trajectory in the island by comparing the water balance components (i.e. evapotranspiration, runoff, and storage change) for different hydroclimatic extremes (i.e. El Niño and La Nina) under its current condition (baseline year of 2016) to that of its past (25-year look back period) and future (50-year trajectory) conditions. This research should contribute to advance the understanding of the landscape scale hydrological processes in tropical peatlands under land-cover change trajectory, which are important to provide scientific basis for stakeholders involved in guiding responsible peatland management practices. This presentation will discuss the modeling approach and preliminary results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".