GRACE storage-runoff hystereses reveal the dynamics of regional watersheds
Bibliographic record
Abstract
Abstract. We characterize how regional watersheds function as simple, dynamic systems through a series of hysteresis loops using measurements from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites. These loops illustrate the temporal relationship between runoff and terrestrial water storage in three regional-scale watersheds (> 150 000 km2) of the Columbia River Basin, USA and Canada. The shape and size of the hysteresis loops are controlled by the climate, topography, and geology of the watershed. The direction of the hystereses for the GRACE signals moves in opposite directions from the isolated groundwater hystereses. The subsurface water (soil moisture and groundwater) hystereses more closely resemble the storage-runoff relationship of a soil matrix. 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 Nash-Sutcliffe Efficiency of 0.91) and monthly runoff during the low flow/high demand month of August (mean Nash-Sutcliffe Efficiency 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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".