Modeling Blue and Green Water Resources Availability in an Iranian Data Scarce Watershed Using SWAT
Bibliographic record
Abstract
Knowledge of the renewable water resources of a watershed is strategic information which is vital for the long term planning of water and food security. In this study, we used a Soil and Water Assessment Tool (SWAT) model in combination with the sequential uncertainty fitting algorithm (SUFI-2) to simulate the water resource components (blue and green water) in the data scarce Kohnak watershed (in southwest Iran) based on river discharges. The simulation was performed for the period from 1992 to 2009 by considering the first three years as warm up. Due to imperfect incomplete climate data, two solution methods, (1) combining CRU data with observed climate data, and (2) integrating expert knowledge in defining uncertainty parameter ranges in the calibrating period, were used to increase the model accuracy prediction. Sensitivity and uncertainty analyses were also performed to improve the model performance. Simulated water resources components of blue water flow, green water storage, and green water flow were evaluated at the subbasin scale. The results showed that with the applied solution methods, SWAT could satisfactorily predict discharge flows and water resource components in the Kohnak watershed. The spatial variabilities of blue and green waters were a function of the spatial variability of precipitation, soil depth, land cover type and slope. Both the blue and green water flows decreased from upstream to downstream. The green water storage was larger in the middle and lower subbasin. It indicates that these regions have relatively sufficient precipitation and green water resources, which are beneficial for the development of rain fed agriculture. This study showed that in data scarce watersheds, a SWAT model in combination with expert knowledge can be used as a suitable tool for water component prediction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".