Extending periodic eddy covariance latent heat fluxes through tree sapflow measurements to estimate long-term total evaporation in a peat swamp forest
Bibliographic record
Abstract
Abstract. A combination of measurement and modelling was used to find a pragmatic solution to estimate the annual total evaporation (ET) from the rare and indigenous Nkazana Peat Swamp Forest (PSF) on the east coast of Southern Africa to improve the water balance estimates within the area. Total evaporation was measured during three window periods (between seven and nine days each) using an eddy covariance (EC) system on a telescopic mast above the forest canopy. Sapflow of an understory and an emergent tree was measured using a low maintenance heat pulse velocity system for an entire hydrological year (October 2009 to September 2010). An empirical model was derived, describing the relationship between the observed ET of the Nkazana PSF measured during two of the window periods (R2 = 0.92 and 0.90) which, overlapped with sapflow measurements, thereby providing hourly estimates of predicted ET of the Nkazana PSF for a year, totalling 1125 mm (while rainfall was 650 mm). In building the empirical model, it was found that including the understory tree sapflow provided no benefit to the model performance. In addition, the observed emergent tree sapflow relationship with observed ET between the two field campaigns was consistent and could be represented by a single empirical model (R2= 0.90; RMSE = 0.08 mm). During the window periods of EC measurement, no single meteorological variable was found to describe the Nkazana PSF ET satisfactorily. However, in terms of evaporation models, the hourly FAO56 Penman–Monteith equation best described the observed ET from EC during the August 2009 (R2 = 0.75), November 2009 (R2 = 0.85) and March 2010 (R2 = 0.76) field campaigns, compared to the Priestley–Taylor model (R2 = 0.54, 0.74 and 0.62 during the respective field campaigns). From the empirical model of ET and the FAO56 Penman–Monteith equation, a monthly crop factor (Kc) was derived for the Nkazana PSF providing a method of estimating long-term swamp forest ET from meteorological data. The monthly crop factor indicated two distinct periods. From February to May, it was between 1.2 and 1.4 compared with June to January, when the crop factor was 0.8 to 1.0. The derived monthly Kc values were verified as accurate (to one significant digit) using historical data measured at the same site, also using EC, from a~previous study. The measurements provided insights into the microclimate within a subtropical peat swamp forest and the contrasting sapflow of emergent and understory trees. They showed that expensive, high maintenance equipment can be used during manageable window periods in conjunction with low maintenance systems, dedicated to individual trees, to derive a model to estimate long-term ET over remote heterogeneous forests. In addition, the contrast in ET and rainfall emphasises the reliance of the Nkazana PSF on groundwater.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".