Quantifying a Reliable Framework to Estimate Hydro-Climatic Conditions via a Three-Way Interaction between Land Surface Temperature, Evapotranspiration, Soil Moisture
Bibliographic record
Abstract
Distributed hydrological models can be suitable choices for predicting the spatial distribution of water and energy fluxes if the conceptual relationships between the components are defined appropriately. Therefore, an innovative approach has been developed using a simultaneous formulation of bulk heat transfer theory, energy budgeting, and water balance as an integrated hydrological model, i.e., the Monthly Continuous Semi-Distributed Energy Water Balance (MCSD-EWB) model, to estimate land surface hydrological components. The connection between water and energy balances is established by evapotranspiration (ET), which is a function of soil moisture and land surface temperature (LST). Thus, the developed structure is based on a three-way coupling between ET, soil moisture, and LST. The LST is obtained via the direct solution of the energy balance equation, and the spatiotemporal distribution of ET is presented using the computed LST and soil moisture through the bulk transfer method and water balance. In addition to the LST computed using the MCSD-EWB model, the LST products of ERA5-Land and MODIS are also utilized as inputs. The results indicate the adequate performance of the model in simulating LST, ET, streamflow, and groundwater level. Furthermore, the developed model performs better by employing the ERA5-Land LST than by using the MODIS LST in estimating the components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".