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Record W2106241302 · doi:10.5194/hessd-11-12027-2014

GRACE storage-streamflow hystereses reveal the dynamics of regional watersheds

2014· article· en· W2106241302 on OpenAlexaboutno aff
Eric A. Sproles, Scott G. Leibowitz, J. T. Reager, P. J. Wigington, J. S. Famiglietti, Sopan Patil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsSurface runoffStreamflowEnvironmental scienceHydrology (agriculture)Scale (ratio)Water storageStructural basinGroundwaterSnowmeltDrainage basinGeologyGeomorphologyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract. We characterize how regional watersheds function as simple, dynamic systems through a series of hysteresis loops. These loops illustrate the temporal relationship between runoff and terrestrial water storage using measurements from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites in three regional-scale watersheds (>150 000 km2) of the Columbia River Basin, USA and Canada. The direction of the hystereses for the GRACE signal move in opposite directions from the isolated groundwater hystereses, suggesting that regional scale watersheds require soil water storage to reach a certain threshold before groundwater recharge and peak runoff occur. While the physical processes underlying these hystereses are inherently complex, the vertical integration of terrestrial water in the GRACE signal encapsulates the processes that govern the non-linear function of regional-scale watersheds. We use this process-based understanding to test how GRACE data can be applied prognostically to predict seasonal runoff (mean R2 of 0.91) and monthly runoff (mean R2 of 0.77) in all three watersheds. The global nature of GRACE data allows this same methodology to be applied in other regional-scale studies, and could be particularly useful in regions with minimal data and in trans-boundary watersheds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.205
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

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