High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States

Noemi Vergopolan; Justin Sheffield; Nathaniel W. Chaney; Ming Pan; Hylke E. Beck; Craig R. Ferguson; Laura Torres-Rojas; Felix Eigenbrod; Wade Crow; Eric F. Wood

doi:10.1029/2022gl098586

High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States

Noemi Vergopolan, Justin Sheffield, Nathaniel W. Chaney, Ming Pan, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Felix Eigenbrod, Wade Crow, Eric F. Wood

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1–100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks — a novel satellite-based surface SM dataset at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1–25 km).

Original language	English (US)
Journal	Geophysical Research Letters
DOIs	https://doi.org/10.1029/2022gl098586
State	Published - Aug 4 2022
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledged KAUST grant number(s): OSR-2017-CRG6
Acknowledgements: This work was supported by the “Modernizing Observation Operator and Error Assessment for Assimilating In-situ and Remotely Sensed Snow/Soil Moisture Measurements into NWM” project from NOAA (grant number NA19OAR4590199), the ”Understanding Changes in High Mountain Asia project” project from NASA (grant number NNH19ZDA001N-HMA), the NASA-NOAA Interagency Agreement through the High Mountain Asia program (grant number 80HQTR21T0015), the ”A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model” project from the King Abdullah University of Science and Technology (grant number OSR-2017-CRG6), and the ”Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BREC-cIA)” project from the UK Research and Innovation as part of the Global Challenges Research Fund (grant number NE/P021093/1).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

Geophysics
General Earth and Planetary Sciences

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10.1029/2022gl098586

Cite this

@article{de202ba153694e069199226755453c2d,

title = "High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States",

abstract = "Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1–100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks — a novel satellite-based surface SM dataset at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1–25 km).",

author = "Noemi Vergopolan and Justin Sheffield and Chaney, {Nathaniel W.} and Ming Pan and Beck, {Hylke E.} and Ferguson, {Craig R.} and Laura Torres-Rojas and Felix Eigenbrod and Wade Crow and Wood, {Eric F.}",

note = "KAUST Repository Item: Exported on 2022-09-14 Acknowledged KAUST grant number(s): OSR-2017-CRG6 Acknowledgements: This work was supported by the “Modernizing Observation Operator and Error Assessment for Assimilating In-situ and Remotely Sensed Snow/Soil Moisture Measurements into NWM” project from NOAA (grant number NA19OAR4590199), the ”Understanding Changes in High Mountain Asia project” project from NASA (grant number NNH19ZDA001N-HMA), the NASA-NOAA Interagency Agreement through the High Mountain Asia program (grant number 80HQTR21T0015), the ”A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model” project from the King Abdullah University of Science and Technology (grant number OSR-2017-CRG6), and the ”Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BREC-cIA)” project from the UK Research and Innovation as part of the Global Challenges Research Fund (grant number NE/P021093/1). This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2022",

month = aug,

day = "4",

doi = "10.1029/2022gl098586",

language = "English (US)",

journal = "Geophysical Research Letters",

issn = "0094-8276",

publisher = "John Wiley and Sons Inc.",

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T1 - High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States

AU - Vergopolan, Noemi

AU - Sheffield, Justin

AU - Chaney, Nathaniel W.

AU - Pan, Ming

AU - Beck, Hylke E.

AU - Ferguson, Craig R.

AU - Torres-Rojas, Laura

AU - Eigenbrod, Felix

AU - Crow, Wade

AU - Wood, Eric F.

N1 - KAUST Repository Item: Exported on 2022-09-14 Acknowledged KAUST grant number(s): OSR-2017-CRG6 Acknowledgements: This work was supported by the “Modernizing Observation Operator and Error Assessment for Assimilating In-situ and Remotely Sensed Snow/Soil Moisture Measurements into NWM” project from NOAA (grant number NA19OAR4590199), the ”Understanding Changes in High Mountain Asia project” project from NASA (grant number NNH19ZDA001N-HMA), the NASA-NOAA Interagency Agreement through the High Mountain Asia program (grant number 80HQTR21T0015), the ”A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model” project from the King Abdullah University of Science and Technology (grant number OSR-2017-CRG6), and the ”Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BREC-cIA)” project from the UK Research and Innovation as part of the Global Challenges Research Fund (grant number NE/P021093/1). This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2022/8/4

Y1 - 2022/8/4

N2 - Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1–100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks — a novel satellite-based surface SM dataset at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1–25 km).

AB - Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1–100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks — a novel satellite-based surface SM dataset at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1–25 km).

UR - http://hdl.handle.net/10754/680190

UR - https://onlinelibrary.wiley.com/doi/10.1029/2022GL098586

U2 - 10.1029/2022gl098586

DO - 10.1029/2022gl098586

M3 - Article

SN - 0094-8276

JO - Geophysical Research Letters

JF - Geophysical Research Letters

ER -

High-resolution soil moisture data reveal complex multi-scale spatial variability across the United States

Abstract

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