High‐resolution soil moisture data reveal complex multi‐scale spatial variability across the United States
High‐resolution soil moisture data reveal complex multi‐scale spatial variability across the United States
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 data set 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 resolution).
heterogeneity, hyper-resolution, landscape, scaling, soil moisture, spatial variability
Vergopolan, Noemi
3c455209-3f04-4ef3-9687-d637239ec4b4
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Chaney, Nathaniel W.
bc3ca362-9e26-46af-bd26-f99983445106
Pan, Ming
5f0a6106-cf97-4213-b345-6b220f3d9bc4
Beck, Hylke E.
edbdb027-f978-47dd-a9d3-43a1cce92e9a
Ferguson, Craig R.
b9d14f97-34c8-44c2-a0d7-f97147d0063c
Torres‐rojas, Laura
56c0d075-de31-47af-93ac-387e9bef7b8f
Eigenbrod, Felix
43efc6ae-b129-45a2-8a34-e489b5f05827
Crow, Wade
f2e76407-3e83-4733-a5a8-c887e0ae05db
Wood, Eric F.
8352c1b4-4fd3-42fe-bd23-46619024f1cf
16 August 2022
Vergopolan, Noemi
3c455209-3f04-4ef3-9687-d637239ec4b4
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Chaney, Nathaniel W.
bc3ca362-9e26-46af-bd26-f99983445106
Pan, Ming
5f0a6106-cf97-4213-b345-6b220f3d9bc4
Beck, Hylke E.
edbdb027-f978-47dd-a9d3-43a1cce92e9a
Ferguson, Craig R.
b9d14f97-34c8-44c2-a0d7-f97147d0063c
Torres‐rojas, Laura
56c0d075-de31-47af-93ac-387e9bef7b8f
Eigenbrod, Felix
43efc6ae-b129-45a2-8a34-e489b5f05827
Crow, Wade
f2e76407-3e83-4733-a5a8-c887e0ae05db
Wood, Eric F.
8352c1b4-4fd3-42fe-bd23-46619024f1cf
Vergopolan, Noemi, Sheffield, Justin, Chaney, Nathaniel W., Pan, Ming, Beck, Hylke E., Ferguson, Craig R., Torres‐rojas, Laura, Eigenbrod, Felix, Crow, Wade and Wood, Eric F.
(2022)
High‐resolution soil moisture data reveal complex multi‐scale spatial variability across the United States.
Geophysical Research Letters, 49 (15), [e2022GL098586].
(doi:10.1029/2022GL098586).
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 data set 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 resolution).
Text
High-Resolution Soil Moisture Data ...
- Accepted Manuscript
More information
Accepted/In Press date: 16 July 2022
e-pub ahead of print date: 4 August 2022
Published date: 16 August 2022
Additional Information:
Funding Information:
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).
Funding Information:
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).
Publisher Copyright:
© 2022. American Geophysical Union. All Rights Reserved.
Keywords:
heterogeneity, hyper-resolution, landscape, scaling, soil moisture, spatial variability
Identifiers
Local EPrints ID: 470312
URI: http://eprints.soton.ac.uk/id/eprint/470312
ISSN: 0094-8276
PURE UUID: 9f998a4e-7580-4c5d-9fed-ce35ddd19445
Catalogue record
Date deposited: 06 Oct 2022 16:39
Last modified: 06 Jun 2024 01:54
Export record
Altmetrics
Contributors
Author:
Noemi Vergopolan
Author:
Nathaniel W. Chaney
Author:
Ming Pan
Author:
Hylke E. Beck
Author:
Craig R. Ferguson
Author:
Laura Torres‐rojas
Author:
Wade Crow
Author:
Eric F. Wood
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
