Preprints
https://doi.org/10.5194/hess-2024-298
https://doi.org/10.5194/hess-2024-298
08 Nov 2024
 | 08 Nov 2024
Status: this preprint is currently under review for the journal HESS.

Integrating remotely sensed surface water dynamics into hydrologic signature modelling

Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, William Keenan, and Wayana Dolan

Abstract. Extreme flow conditions in rivers have far-reaching environmental and economic consequences. The retention of surface water in lakes, wetlands, and floodplains can potentially modify the timing, duration, and magnitude of flow. However, efforts to explore the impact of surface water storage on discharge regimes have been limited in geographic extent. In this analysis, we calculated six hydrologic signatures, reflecting flashiness and high and low flow conditions, at 72 gaged watersheds across the conterminous United States. In addition to traditionally considered variables representing climate, land cover, topography, and soil, we incorporated a novel remote sensing (Sentinel-1 & 2) approach to study the contribution of surface water storage dynamics when modelling spatial variability in hydrologic signatures using random forest models. While climate variables explained much of the variability in the hydrologic signatures, models for five of the six signatures showed some degree of improvement in model performance when landscape characteristics were added with adjusted R2 improving 1.75 to 11.69 % and Akaike information criterions improving 0.24 % to 6.69 %. Automated variable selection can be indicative of the relative importance of certain variables over others. Using a forward selection process, five of the six signature models selected remotely sensed inundation variables with all five variables showing a significant (p<0.01) contribution to the respective model. More semi-permanent and permanent inundation within the floodplain (i.e., lakes along rivers), for example, was associated with lower wet season and annual flashiness. Further, greater seasonal floodplain inundation extent was associated with increases in peak flows, so that floodplain water storage was relevant to both flashiness and high flow signatures. Additionally, spatial variability in the amount of semi-permanent and permanent non-floodplain water significantly contributed to explaining spatial variability in the baseflow index. These findings suggest that surface water storage dynamics may help explain variability in streamflow signatures. Watershed management will benefit from an improved understanding of how surface water storage influences stream behaviour.

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Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, William Keenan, and Wayana Dolan

Status: open (until 28 Dec 2024)

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  • CC1: 'Comment on hess-2024-298', Nima Zafarmomen, 09 Nov 2024 reply
Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, William Keenan, and Wayana Dolan

Data sets

Data release for integrating remotely sensed surface water dynamics in hydrologic signature modelling M. K. Vanderhoof, P. Nieuwlandt, H. E. Golden, C. R. Lane, J. R. Christensen, W. Keenan, and W. Dolan https://www.sciencebase.gov/catalog/item/652027f4d34e44db0e2e43b4

Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, William Keenan, and Wayana Dolan

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Short summary
Streamflow signatures can help characterize a watershed’s response to meteorological conditions. We explored if surface water storage-related variables, which are typically excluded from streamflow signature analyses, may help explain spatial variability in streamflow signatures. We found that remotely sensed surface water storage extent and duration were correlated with and explained a portion of the variability in many of the hydrologic signatures across the 72 streamflow gages.