Paper Harvest Report
Date range: May 23, 2026
2 top-tier papers selected out of 19 total publications
Today’s Highlights
Machine learning predictions across 1.8 million US rivers show that floodplain residence times average 3.4× longer than in-channel, with over 31% of basin water potentially exchanging with the floodplain during large floods — reshaping our understanding of basin-scale mixing. Meanwhile, convection-permitting simulations of the devastating July 4, 2025 Central Texas storms reveal that antecedent wet soils enhanced rainfall, but warm Gulf of Mexico SST anomalies actually suppressed it by disrupting the Great Plains low-level jet.
Table of Contents
Top-Tier Journal Papers
River‐Floodplain Exchange Influences Basin‐Scale Mixing During Large Fluvial Flood Events
Authors: Craig B. Brinkerhoff, Peter A. Raymond, Colin J. Gleason
Journal: Geophysical Research Letters · DOI: 10.1029/2026gl122190
Matched topics: river, flood
River‐floodplain exchange occurs when high flows inundate the floodplain and exchange water bi‐directionally between substrates and communities. While well‐understood locally, the full spatial heterogeneity of exchange event duration (the residence time) and magnitude (the exchange flux, or discharge) remains poorly constrained, limiting our understanding of the role that underlying structural connectivity and hydrologic regime plays in basin‐scale mixing. Here, we use machine learning to predict river‐floodplain exchange duration and magnitude for over 1.8M rivers in the contiguous United States. We find residence time is on average 3.4 times longer in the floodplain than the river, with that difference decreasing as both event and river size increase. Further, more than 31% of basin water may exchange with the floodplain during large floods. We confirm that the cumulative effect of reach‐scale exchange influences basin‐scale mixing and subsequent biogeochemical processing, though the nature of that influence will be region, river, and constituent specific.
Influence of Surface Conditions on the 04 July 2025 Extreme Storms in Central Texas
Authors: Edward K. Vizy, Kerry H. Cook
Journal: Geophysical Research Letters · DOI: 10.1029/2026gl123271
Matched topics: coastal
The impactful 04 July 2025 Central Texas extreme rainfall event is examined to understand how surface conditions influence storm development. Utilizing convection‐permitting model simulations, we evaluate the sensitivity of this event to Gulf of Mexico sea surface temperature anomalies (SSTAs) and antecedent soil moisture distributions. The precursor wet soil conditions enhanced storm rainfall, whereas warm coastal and central Gulf SSTAs suppressed rainfall through perturbations of the low‐level circulation, including the Great Plains low‐level jet, which modified moisture transport and moisture convergence. When compared with climatological conditions, SST and soil moisture anomalies produced a rainfall reduction, indicating SST forcing dominated the combined response. These results suggest that this extreme storm would have produced higher rainfall totals had SSTs been closer to their recent climatological average.
Statistics
| Metric | Count |
|---|---|
| Journals searched | 11 |
| Total papers fetched | 19 |
| Passed deterministic filter | 3 |
| After LLM relevance filtering | 2 |
| Rejected (not relevant) | 1 |
| AI for Science items picked | 0 |
Papers by journal
| Journal | Papers |
|---|---|
| Geophysical Research Letters | 2 |
Filtering Criteria
Topics: hydrology, hydrologic model, river, runoff, streamflow, reservoir, water management, flood, drought, seasonal, land surface model, climate change, hydropower, surface water, irrigation, earth system model, estuary, coastal, freshwater discharge, river plume, ocean biogeochemistry, marine heatwave, paleohydrology, paleoclimate, Quaternary, Holocene, Pleistocene, fluvial geomorphology, river terrace, loess, drainage network, river capture, landscape evolution, luminescence dating
Fields: engineering, environmental science, computer science, geology, geography