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Jan 24, 2024 153 likes •433 views

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SLIDE 8

Goals of Standardizing

The aim is to make soil moisture observations more useful in decision support, monitoring, and modeling applications, moreover; standardized dataset would ideally be available in near-real time at an hourly frequency.

Improves monitoring and enhances situational awareness.

i. Availability of timely soil moisture conditions ii. Permits a wide range of monitoring metrics (i.e., percent of drought-hours over the past week).

Eases temporal alignment with remotely sensed products (i.e., satellite and radar).

i. Non-stationary satellite passes can be periodic and short in duration. ii. Hourly resolution radar products

Allows for direct model comparisons and assimilation of soil moisture data from

research to operational uses. i. Numerical models tend to report at hourly frequency ii. Assimilation efforts routinely include variables representing conditions over specific hours (i.e., 3 or 6 hourly steps).

SLIDE 9

Climatology Examples

A 31-day sample length climatology for Bowling Green, KY and Elgin, AZ show reasonable results.

SLIDE 10

Climatology Validation

Sensitivity tests were evaluated at 14 SCAN sites to determine the appropriate sample length for hourly soil moisture climatologies, and the degree to which the most recent 7-years is representative of the longer dataset Monte Carlo simulations

3, 5, 7, 9, and 11

randomly drawn years a 1000 times.

Climatologies using

sample lengths of 1, 3, 7, 15, 31, and 45 days were computed.

Total of 30,000 Monte

Carlo runs per SCAN station.

SLIDE 11

Sensitivity Results

Sensitivity results indicate we can construct a reasonable soil moisture climatology with the last 7 years of data based on an analysis of SCAN data. The improvement in error levels off after 7-years using a 31-day sample length.

SLIDE 12

Last 7-Years

The most recent 7-year period was within 0.4 standard deviations of the randomized 7-year Monte Carlo mean.

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SLIDE 20

Hydrological Analysis

How well do volumetric and standardized soil moisture observations compare with stream flow? Delineated watersheds that drain the USCRN station and surrounding region. Pulled USGS stream gage data and identified instances of high flow; stream discharge rates exceeding the 95th percentile. The Newton, GA station is within the Ichawaynochaway Creek watershed, which drains an area of 2573 km2

SLIDE 21

USCRN Watersheds

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SLIDE 24

Overall, USCRN’s hourly standardized soil moisture product provided a better assessment of hydrological conditions from both drought and high stream flow perspectives.

Soil moisture metrics could be a leading indicator of drought trends.
Frequency of exceedance aligned well with drought severity.
Percentile were more spatially correlated than volumetric measures.
Evaluations of hydrological conditions were easier using standardized metrics

Going forward,

1. Operationalize the hourly standardized product (in progress)
2. Continue development of drought indices
3. Evaluate deeper (than 10cm) depths response to drought onset and amelioration
4. Explore strategies to integrate remotely sensed and modeled data with observations.

Summary

Ronald D. Leeper ronald.leeper@noaa.gov

SLIDE 25

USCRN soil moisture accomplishments

Soil Moisture Analyses and QC Developments
Bell, J. E., M. A. Palecki, C. B. Baker, W. G. Collins, J. H. Lawrimore, R. D. Leeper, M. E. Hall, J. Kochendorfer, T. P. Meyers,
T. Wilson, and H. J. Diamond. 2013: U.S. Climate Reference Network soil moisture and temperature observations.J.

Hydrometeorol.,14, 977-988.doi:10.1175/JHM-D-12-0146.1

Palecki, M. A., and J. E. Bell, 2013: U.S. Climate Reference Network soil moisture observations with triple redundancy:

measurement variability.Valdose Zone Journal,12, p. vzj2012.0158.doi:10.2136/vzj2012.0158

Wilson, T.B., Baker, C.B., Meyers, T.P., Kochendorfer, J., Hall, M., Bell, J.E., Diamond, H.J. and Palecki, M.A., 2016: Site-

Specific Soil Properties of the US Climate Reference Network Soil Moisture. Vadose Zone Journal, 15(11). doi: 10.2136/vzj2016.05.0047

Cooperation with satellite soil moisture retrieval efforts
Chan, S. K., R. Bindlish, P. E. O'Neill, E. Njoku, T. Jackson, A. Colliander, F. Chen, M. Burgin, S. Dunbar, J. Piepmeier, S.

Yueh, D. Entekhabi, M. H. Cosh, T. Caldwell, J. Walker, X. Wu, A. Berg, T. Rowlandson, A. Pacheco, H. McNairn, M. Thibeault, J. Martínez-Fernández, Á. González-Zamora, M. Seyfried, D. Bosch, P. Starks, D. Goodrich, J. Prueger, M. Palecki, E. E. Small, M. Zreda, J. Calvet, W. T. Crow, and Y. Kerr, 2016: Assessment of the SMAP Passive Soil Moisture

Product. IEEE Trans. Geosci. Rem. Sens., 54 (8), 4994-5007.doi:10.1109/TGRS.2016.2561938
Coopersmith, E. J., M. H. Cosh, R. Bindlish, and J. Bell, 2015: Comparing AMSR-E soil moisture estimates to the

extended record of the U.S. Climate Reference Network (USCRN). Adv. Water Res., 85, 79-85. doi: 10.1016/j.advwatres.2015.09.003

Chan, S. K., R. Bindlish, P. O'Neill, T. Jackson, E. Njoku, S. Dunbar, J. Chaubell, J. Piepmeier, S. Yueh, D. Entekhabi, A.

Colliander, F. Chen, M.H. Cosh, T. Caldwell, J. Walker, A. Berg, H. McNairn, M. Thibeault, J. Martínez-Fernández, F. Uldall,

M. Seyfried, D. Bosch, P. Starks, C. Holifield Collins, J. Prueger, R. van der Velde, J. Asanuma, M. Palecki, E.E. Small, M.

Zreda, J. Calvet, W. T. Crow, and Y. Kerr, 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of the Environment, 204, 931-941. doi: 10.1016/j.rse.2017.08.025

Comparison to reanalysis modelled soil moisture
Leeper, R. D., Bell, J. E., Vines, C., & Palecki, M., 2017: An Evaluation of the North American Regional Reanalysis

Simulated Soil Moisture Conditions during the 2011 to 2013 Drought Period.Journal of Hydrometeorology,18, 515- 527.doi: 10.1175/JHM-D-16-0132.1

SLIDE 26

USCRN soil moisture accomplishments

Development of approaches to soil moisture record extension in time and space
Coopersmith, E. J., M. H. Cosh, and J. E. Bell, 2015: Extending the soil moisture data record of the Climate Reference

Network (CRN) and the Soil Climate Analysis Network (SCAN). Adv. Water Res., 79, 80 -90. doi: 10.1016/j.advwatres.2015.02.006

Coopersmith, E. J., Cosh, M. H., Bell, J. E., & Boyles, R., 2016: Using machine learning to produce near surface soil

moisture estimates from deeper in situ records at US Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation.Advances in Water Resources, 98, 122-131. doi: 10.1016/j.advwatres.2016.10.007.

Coopersmith, E.J., Cosh, M.H., Bell, J.E., & Crow, W., 2016: Multi-profile analysis of soil moisture within the climate

reference network.Vadose Zone Journal, 15(1).doi:10.2136/vzj2015.01.0016.

Coopersmith, E. J., Cosh, M. H., Bell, J. E., Kelly, V., Hall, M., Palecki, M. A., & Temimi, M., 2016: Deploying temporary

networks for upscaling of sparse network stations. International Journal of Applied Earth Observation and Geoinformation,52, 433-444.doi:10.1016/j.jag.2016.07.013.

Health application of USCRN soil moisture observations
Coopersmith, E. J., J. E. Bell, K. Benedict, J. Shriber, O. McCotter, and M. H. Cosh, 2017: Relating coccidioidomycosis

(valley fever) incidence to soil moisture conditions,GeoHealth, 1, 51-63 (Cover Article).doi:10.1002/2016GH000033