Reviewed two papers regarding soil moisture memory: Koster and Suarez (2000) and Ghannam et al. (2017)

1. Soil Moisture
Memory
6 Nov, 2020 @ BGG 5th Meeting
Ryoko Araki

2. What is soil
moisture memory?
Quantifying
via autocorrelation
02
01 Quantifying
via persistency
03
Challenges and
opportunities
04

3. Extremely dry/wet condition
persists

4. … because of soil moisture–atmosphere feedback
Introduction
Seneviratne et al., 2010
② Decrease of actual ET
② Increase of sensible heat flux
③ Increase of temperature
④ Higher vapor deficit
① Dry soil condition

5. How long does the soil moisture
‘remember’ the anomaly state?
Introduction
Soil moisture memory
• 2-3 months (Vinnikov and Yeserkoepora, 1991’s observation in Russia)
• 2 month (Entin et al., 2000’s observation in China, Mongolia, and Illinois)
Soil moisture feedback with temperature
• Several months (Huang et al., 1996)

6. How to quantify
the time-scale of
soil moisture memory?
• Autocorrelation
• Persistency
Introduction

7. Quantification via auto-correlation
Method
・・・・・・・
https://www.youtube.com/watch?v=ZjaBn93
YPWo&t=480s

8. Quantification via auto-correlation
Method
Koster and Suarez (2000)
• Time shift = 31days
• Developed autocorrelation function for land-surface model, that includes
non-stationality and persistency of climate inputs
• Applied the method on AGCM-LSM

9. 1-month auto-correlation
9
Contribution of …
ET
Runoff
Antecedent soil moisture
Koster and Suarez (2000)
Results

10. Challenges in quantification via auto-correlation
Strength
• Autocorrelation equation can be derived from stochastic land-surface
model  prognostic analysis is possible
Weakness
• Dependency on the mean value in deriving covariance
• Ignores positive/negative value of soil moisture change/value
(pointed out in McColl et al., 2019)

11. Quantification via persistency
Determined by telegraphic
approximation, which allows
to isolate event clustering
without being influenced by
event amplitudes
Ghannam et al., (2017)

12. Quantification via persistency: Results
Method
Ghannam et al., (2017)
• Examined persistency of in-situ soil moisture data
• On-off switches of rainfall events do not coincide with
the one of soil moisture
 the precipitation features, or evaporation cycles
• Persistence rainfall during dry period is shorter than
that of the soil moisture
 some implication to soil moisture-atm feedback???
• Soil moisture values are aggregated over entire soil layer
 Analysis by different layer?

13. Challenges in quantification via persistency
Strength
• Able to investigate persistence of high-value period (wet) and low-value period (dry)
 allows investigation of seasonal soil moisture feedback and its transition
Weakness
• Only diagnostic (not able to predict the future states)

14. Conclusion
14
• Time-scale of soil moisture memory is important to understand the soil moisture
feedback processes, and to improve weather/drought prediction
• Auto-correlation analysis  prognostic analysis of soil moisture memory based on
LSM
• Persistence analysis  diagnostic investigation of seasonal soil-moisture
atmosphere feedback based on observed/simulated data

15. Discussion / question
15
• Can the autocorrelation analysis be applied to any other purposes?
• If we have better understanding of soil moisture memory, will it
improve the weather prediction?
• Can the soil moisture memory be observed at watershed scale?
• Is the map by Koster and Suarez (2000) reasonable? What pattern
do you see?
• Any point unclear?

16. References
16
Akbar, R., Short Gianotti, D. J., McColl, K. A., Haghighi, E., Salvucci, G. D., & Entekhabi, D. (2018). Estimation of Landscape Soil Water Losses
from Satellite Observations of Soil Moisture. Journal of Hydrometeorology, 19(5), 871–889. https://doi.org/10.1175/JHM-D-17-0200.1
Entin, J. K., Robock, A., Vinnikov, K. Y., Hollinger, S. E., Liu, S., & Namkhai, A. (2000). Temporal and spatial scales of observed soil moisture
variations in the extratropics. Journal of Geophysical Research, 105(D9), 11865–11877. https://doi.org/10.1029/2000JD900051
Ghannam, K., Nakai, T., Paschalis, A., Oishi, C. A., Kotani, A., Igarashi, Y., Kumagai, T. ’omi, & Katul, G. G. (2016). Persistence and memory
timescales in root-zone soil moisture dynamics. Water Resources Research, 52(2), 1427–1445. https://doi.org/10.1002/2015WR017983
Koster, R. D., & Suarez, M. J. (2001). Soil moisture memory in climate models. Journal of Hydrometeorology, 2(6), 558–570.
https://doi.org/10.1175/1525-7541(2001)002<0558:SMMICM>2.0.CO;2
McColl, K. A., He, Q., Lu, H., & Entekhabi, D. (2019). Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially
Anticorrelated at Global Scales. Journal of Hydrometeorology, 20(6), 1165–1182. https://doi.org/10.1175/JHM-D-18-0141.1
McColl, K. A., Wang, W., Peng, B., & Akbar, R. (2017). Global characterization of surface soil moisture drydowns. Geophysical.
https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072819
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., & Teuling, A. J. (2010). Investigating soil moisture–
climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3-4), 125–161. https://doi.org/10.1016/j.earscirev.2010.02.004
Vinnikov, K. Y., Robock, A., Speranskaya, N. A., & Schlosser, C. A. (1996). Scales of temporal and spatial variability of midlatitude soil moisture.
Journal of Geophysical Research, 101(D3), 7163–7174. https://doi.org/10.1029/95JD02753
Vinnikov, K. Y., & Yeserkepova, I. B. (1991). Soil Moisture: Empirical Data and Model Results. Journal of Climate, 4(1), 66–79.
https://doi.org/10.1175/1520-0442(1991)004<0066:SMEDAM>2.0.CO;2

17. 17
Model Satellite observation In-situ observation
Autocorrelation (monthly-scale) Vinnikov and
Yeserkoepora
(1991); Vinnikov et
al., (1996); Entin et
al., (2000)
Autocorrelation (weekly-scale)
Persistency (monthly-scale)
Persistency (weekly-scale)
Ghannum et al.,
(2018)
McColl et al., (2019)
Koster and Suarez.,
(2000)
McColl et al., (2017)
Akbar et al., (2018)