Scott L Painter
Biographic Data
| ID | 10510705 |
|---|---|
| NAME | Scott L Painter |
| GIVEN NAMES | Scott L |
| FAMILY NAME | Painter |
| SIGNATURE | PAINTER S L |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Permafrost thermal conditions are sensitive to shifts in snow timing
Changes in snow precipitation at high latitudes can significantly affect permafrost thermal conditions and thaw depth, potentially exposing more carbon-laden soil to microbial decomposition. A fully coupled process-based surface/subsurface thermal hydrology model with surface energy balance is used to analyze the impact of intra-annual variability in snow on permafrost thermal regime and the active layer thickness. In the four numerical scenarios…
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integra…
No prominent works on this page.
Permafrost thermal conditions are sensitive to shifts in snow timing
Changes in snow precipitation at high latitudes can significantly affect permafrost thermal conditions and thaw depth, potentially exposing more carbon-laden soil to microbial decomposition. A fully coupled process-based surface/subsurface thermal hydrology model with surface energy balance is used to analyze the impact of intra-annual variability in snow on permafrost thermal regime and the active layer thickness. In the four numerical scenarios…
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integra…
Climatology (2 works) · Environmental Science (2 works) · Geology (2 works) · Active layer (1 work) · Arctic (1 work) · Arctic and Antarctic ice dynamics (1 work) · Artificial Intelligence (1 work) · Atmospheric sciences (1 work) · Climate change (1 work) · Climate change and permafrost (1 work)