Incorporação do Impacto da Rede de Reservatórios Superficiais Artificiais de Caráter Intranual na Modelagem Hidrológica Chuva-Vazão
Bibliographic record
Abstract
A hydrologic model’s ability to describe the process- \nes encompassing the transformation of precipitation into \nflow rates depends on the assumptions, structure and for- \nmulations it uses. Models of the spatially distributed type \nare very attractive. However, the mathematical simplifica- \ntions used may generate errors when the represented basin's \nfeatures are very heterogeneous due to natural characteris- \ntics or to anthropic activity. The river regime in the semi- \narid Northeast of Brazil has undergone changes due to the \nconstruction of superficial artificial reservoirs, most of which are small reservoirs of an intra-annual nature. \nThose small reservoirs make modeling difficult. \nIn this paper we propose a change to the structure of the \nSMAP model aiming to explicitly incorporate the represen- \ntation of small damming. This representation is done by \ninserting a reservoir into the mathematical model with the \noperating characteristics which are representative of small \ndamming behavior. The dimension of that reservoir is a \ngradable parameter (h RPA) aiming to identity the effect of \nthis intra-annual regularization. \nThe proposed model, SMAP-RPA, consists of expanding the \nSMAP model in its monthly version. Therefore, determin- \ning the results from the RPA component improves the mod- \nel’s performance and makes it more realistic when compared \nto SMAPm's performance. As a result, in cases where the \ndams in the hydrographic basin do not produce significant \nvariation in the hydrograph, the “hRPA” parameter is null \nand the SMAP-RPA operation is the same as in SMAPm. \nAiming to ascertain the proposed model's validity, we de- \nveloped a case study by formulating two scenarios accord- \ning to the flow rate series and the characteristics of 18 hy- \ndrographic basins located in the state of Ceará. The results \nsupport the efficiency of small damming representation and \nthe ensuing efficiency improvement in the adjustments to \nobserved and calculated series. In this paper we have also \nnoticed an important scale effect. The basins which were \naffected most by small damming were those with a drainage \narea of less than 5,000 km2. The impact of this small \ndamming on larger scale basins is not detected by the mod- \nel, which makes the “h RPA” parameter null
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".