Extension of Naturalized Flow Using Linear Regression
Bibliographic record
Abstract
Naturalized flow, which is streamflow representing natural hydrology, is one of the hydrologic input datasets for Texas’ water availability models (WAMs) that are used for the water rights permitting process, regional and statewide water planning, and other water management activities in Texas. The existing naturalized flow input for most of these models covers the period extending from the 1940s to the 1990s. Given that some river basins in Texas have, in recent years, experienced record low streamflow associated with the 2010—2015 drought event, having updated naturalized flows is essential for reliable estimates of water availability from surface water sources. We find that for some river basins, or some part of a river basin, naturalized flows have a close correlation with observed flows. Furthermore, monthly naturalized flow in upstream watersheds and downstream watersheds, and vice versa, are also correlated, since streamflow under natural condition typically increases gradually downstream. Adjacent watersheds may also have similar flow characteristics. Therefore, it is relatively easy and fast to extend the WAM’s naturalized flow input using a regression method. We introduce a simple and convenient methodology for updating naturalized flows using linear regression between historical gauged flow and existing naturalized flow, between upstream and downstream naturalized flow, and between naturalized flow in adjacent watersheds. Naturalized flows updated using this methodology can be used as an auxiliary dataset for water planning purposes until the Texas Commission on Environmental Quality (TCEQ) releases official updated naturalized flow datasets for the state. We present an evaluation of the extended naturalized flows for the Canadian River Basin WAM and Sulphur River Basin WAM where the datasets used to develop the regression equations are highly correlated (correlation coefficient, R>0.93 and standard error <0.12). A comparison of extended naturalized flows for the Sulphur River Basin WAM derived using our methodology with the recently-released official update to the Sulphur River Basin WAM indicates that the average monthly mean flow is only 0.3% higher than the official average at a selected control point (i.e., model node). Simulated firm yield and overall water right reliability are within 2% of the same metrics derived using the official dataset. This result provides a validation of our dataset and justification for using such extended naturalized flows as auxiliary datasets for research and surface water resources planning in Texas.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".