the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 May 2024
What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?
Abstract. Soil moisture memory (SMM), which refers to how long a perturbation in Soil Moisture (SM) can last, is critical for understanding climatic, hydrologic, and ecosystem interactions. Most land surface models (LSMs) tend to overestimate surface soil moisture and its persistency, sustaining unexpectedly large soil surface evaporation. In general, LSMs show an overestimation of long-term SMM and an underestimation of short-term SMM. This study aims to 1) identify key soil hydrological/hydraulic processes that contribute to the amount and persistence of SM and 2) improve the physical representations of soil hydrology in the widely-used Noah-MP LSM with optional schemes of soil hydrology/hydraulics. We test the effects of different processes on SMM, including soil water retention characteristics (or soil hydraulics), soil permeability, and surface ponding. We compare SMMs computed from various Noah-MP configurations against that derived from the Soil Moisture Active Passive (SMAP) Level 3 soil moisture and in-situ measurements from the International Soil Moisture Network (ISMN) from year 2015 to 2019 over the contiguous United States (CONUS). The results suggest that 1) soil hydraulics plays a dominant role, and the Van-Genuchten hydraulic scheme reduces the overestimation of the long-term surface SMM produced by the Brooks-Corey scheme, which is commonly used in LSMs; 2) explicitly representing surface ponding improves SMM accuracy for both the surface layer and the root zone; and 3) enhanced permeability through macropores improves the overall representation of soil hydraulic dynamics. The combination of schemes introduced in this study can significantly improve the long-term memory overestimation and short-term memory underestimation issues in LSMs.
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CC1: 'Comment on “What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?” by Farmani et al. (2024)', Mehdi Rahmati, 23 May 2024
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Comment on “What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?” by Farmani et al. (2024)
It was with great interest that I read this interesting article written by Farmani et al. (2024). After reviewing and reading almost all works on soil moisture memory (SMM), it must unfortunately be noted that the effects of soil properties on SMM are very rarely investigated, and it's great to see that a research group has conducted such interesting research directly on this topic. To emphasize the importance of this and all similar research looking at SMM and the link with soil properties, I may copy and paste here the part of the "The way forward" section of our review paper on SMM, which has just been published in Reviews of Geophysics (Rahmati et al. (2024); https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023RG000828):
“Finally, SMM is the result of a complex interplay of physical, biological, and hydrological processes and soil properties (Group 3) (Rahmati et al., 2023). In fact, SMM is rooted in the integrative nature of soil moisture as a water reservoir (Orth and Seneviratne, 2013), which can be influenced by multiple processes (Figure 3), including soil infiltration, soil water redistribution and storage, root water uptake, capillary rise, and drainage. This review shows that the literature, in general, considers soil depth and soil porosity (as it appears in the autocorrelation expression) to be the main soil properties controlling SMM. While we recognize the valuable contributions of previous efforts such as the SoilWat initiatives (e.g., Aliku and Oshunsanya, 2018; Andrews and Bradford, 2016; Oyeogbe and Oluwasemire, 2013), we maintain that additional consideration should be given to pore size distribution, soil mineral composition (e.g., type and amount of clay), soil organic carbon, and other such properties, as these can control water retention, hydraulic conductivity, and diffusivity and accordingly can influence SMM. In addition, the importance of “hydraulic redistribution” by roots (Dawson, 1993), which is of prominent importance during dry periods by bringing water from deep reservoirs to the near surface soil (Caldwell et al., 1998; Jackson et al., 2000), needs to be emphasized in future research. Hagemann and Stacke (2015) have already shown that hydraulic redistribution by a wide range of plant species is significant in many different biomes around the globe and has implications for SMM.”
The improper integration of soil memory (as a comprehensive concept that includes SMM, as soil moisture is only one of the carriers of memory in the soil) into LSMs has already been highlighted in another article of our group published in Nature Reviews Earth & Environment (Rahmati et al. (2023); https://www.nature.com/articles/s43017-023-00454-5) where we have already stated that LSMs neglect soil memory.
Finally, although I do not put myself in the shoes of the reviewers of this paper and therefore leave the technical comments to them, I have only one concern that I thought might be overlooked and would be better to mention, namely that in lines 164 to 174, you mentioned several metrics for quantifying SMM (of course you can find more, as listed in our review) and then stated in lines 175 to 177 that "These methods provide insights into the magnitudes of water and energy flux exchanges between land surface and atmosphere, indicating that shorter SMM durations can lead to more intense feedback and larger flux exchanges". However, I think that such an insight cannot be adopted so easily. After reviewing almost all available work on SMM, I can only say that the SMM timescale merely indicates the duration or time window within which the current state of the soil moisture causes feedback to the land surface process. However, we cannot judge from the SMM timescale how strong this feedback will be. As we mentioned in our review, future research on SMM should examine the strength of SMM in addition to its timescale. So far, the only criterion to investigate the strength of the feedback is the autocorrelation value itself. All the other criteria you have listed here only quantify the SMM timescale, of course, mostly based on the autocorrelation, which certainly cannot say anything about the strength of the feedback. In short, the SMM timescale only defines the active period of memory (see the following figure, which is copied from Figure 1 of our review paper), not its strength. I may be wrong, but this is how I can understand it, even from a mathematical point of view.
To summarize, I would say that an extreme event (exogenous or endogenous, whatever it is) leads to Soil Memory (as a whole, which includes SMM), which is only a descriptive phenomenon to describe the process as a whole: a phenomenon that occurs in the soil (or we can call it an emergent property of the soil, so emerges in soil) that describes how and why information is fed into the soil after a single event or series of events, how the information is stored, and transferred across the time axis, and what mechanisms are involved and how they affect the variables, fluxes, and functioning of the future system. However, when it comes to quantifying it, we can assume three different characteristics, including timescale, strength, and legacy effects:
Soil Memory Timescale: the time period in which the soil can remember these effects. If the carrier is known (e.g., soil moisture, soil carbon, etc.) and we can measure it as a time series, then the memory timescale can be quantified by the time lag at which the autocorrelation of such a time series falls below its e-fold — or we can apply other methods like Hybrid Stochastic‐Deterministic Model suggested by McColl et al. (2019), which is also used by Farmani et al. (2024); if the carrier of memory has no time series origin (like change in soil structure or pore size distribution), then other methods should be used for this quantification, such as the metrics used in paleopedology, I think.
Strength of Soil Memory: As used in the literature (e.g., Orth et al., 2013), this quantifies the strength of the drivers of Soil Memory. In this way, we can acknowledge and discuss that this memory is based on changes in atmospheric forcings, management factors, or soil properties and mechanisms. In the case of memory carriers with time series origin, it can be quantified by the value of autocorrelation at each time step from 1 (the day after the event) to the Memory Timescale.
Soil Legacy: This is the value of the impact of extreme events on the functioning, fluxes, and variables of the system after extreme events (which is probably of your interest when you talk about the strength of the feedback). For example, the change (positive or negative) in the fluxes of the system (soil respiration, CO2 emission, etc.) in time steps after the occurrence of the extreme event. The legacy will certainly be stronger if we study it in close proximity to the event. As the temporal distance increases, the legacy decreases, and the impact is almost zero after a time corresponding to the time scale for memory. According to the literature, legacy can be quantified by comparing the state of the target variable or flux of the system at any time after an extreme event with the long-term average before the occurrence of that extreme event. Thus, it can be positive (e.g., an increase in CO2 emissions after the extreme event) or negative (a decrease in carbon storage after the extreme event).
Best,
Mehdi Rahmati
Agrosphere Institute IBG‐3,
Forschungszentrum Jülich GmbH,
Jülich, Germany
References:
Aliku, O. and Oshunsanya, S. O.: Assessment of the SOILWAT model for predicting soil hydro-physical characteristics in three agro-ecological zones in Nigeria, International Soil and Water Conservation Research, 6, 131-142, 2018.
Andrews, C. M. and Bradford, J. B.: SOILWAT: A Mechanistic Ecohydrological Model for Ecosystem Classification and Prediction, World Conference on Natural Resource Modeling. , 2016.
Caldwell, M. M., Dawson, T. E., and Richards, J. H.: Hydraulic lift: consequences of water efflux from the roots of plants, Oecologia, 113, 151-161, 1998.
Dawson, T. E.: Hydraulic lift and water use by plants: implications for water balance, performance and plant-plant interactions, Oecologia, 95, 565-574, 1993.
Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M., and Niu, G.-Y.: What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?, EGUsphere, 2024, 1-28, 2024.
Hagemann, S. and Stacke, T.: Impact of the soil hydrology scheme on simulated soil moisture memory, Climate Dynamics, 44, 1731-1750, 2015.
Jackson, R. B., Sperry, J. S., and Dawson, T. E.: Root water uptake and transport: using physiological processes in global predictions, Trends in plant science, 5, 482-488, 2000.
McColl, K. A., He, Q., Lu, H., and Entekhabi, D.: Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales, Journal of Hydrometeorology, 20, 1165-1182, 2019.
Orth, R., Koster, R. D., and Seneviratne, S. I.: Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model, Journal of Hydrometeorology, 14, 1773-1790, 2013.
Orth, R. and Seneviratne, S. I.: Propagation of soil moisture memory to streamflow and evapotranspiration in Europe, Hydrology and Earth System Sciences, 17, 3895-3911, 2013.
Oyeogbe, A. and Oluwasemire, K.: Evaluation of SOILWAT model for predicting soil water characteristics in southwestern Nigeria, International Journal of soil science, 8, 58, 2013.
Rahmati, M., Amelung, W., Brogi, C., Dari, J., Flammini, A., Bogena, H., Brocca, L., Chen, H., Groh, J., Koster, R. D., McColl, K. A., Montzka, C., Moradi, S., Rahi, A., Sharghi S., F., and Vereecken, H.: Soil Moisture Memory: State-Of-The-Art and the Way Forward, Reviews of Geophysics, 62, e2023RG000828, 2024.
Rahmati, M., Or, D., Amelung, W., Bauke, S. L., Bol, R., Franssen, H. J. H., Montzka, C., Vanderborght, J., and Vereecken, H.: Soil is a living archive of the Earth system, Nature Reviews Earth & Environment, 4, 421-423, 2023.
Citation: https://doi.org/10.5194/egusphere-2024-1256-CC1 -
AC1: 'Reply on CC1', Mohammad Farmani, 28 May 2024
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Dear Mehdi Rahmati,
Thank you for your comments and for providing information about SMM.
The mentioned lines will be corrected in the original manuscript.
Best regards,
Mohammad
Citation: https://doi.org/10.5194/egusphere-2024-1256-AC1
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AC1: 'Reply on CC1', Mohammad Farmani, 28 May 2024
reply
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RC1: 'Comment on egusphere-2024-1256', Anonymous Referee #1, 21 Jun 2024
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It’s my pleasure to review this manuscript on NoahMP model. The study utilized the Noah-MP land surface model, implementing five different soil hydraulic parameterization schemes, including two soil hydraulic models, a dual-permeability infiltration scheme, and variations in surface ponding depth. By integrating soil moisture datasets from SMAP and ISMN, the study explored the effects of these schemes on Soil Moisture Memory (SMM). It was found that using the Van-Genuchten parameterization and dual-permeability infiltration scheme improved the simulation of SMM. Considering surface ponding extended soil moisture infiltration, thereby improving moisture conditions in the surface and root zone, leading to increased long-term memory and decreased short-term memory. Conversely, using macropores reduced SMM. In general, the manuscript is well written and fully within the scope of HESS. While I still have some concerns before the recommendation of its publication.
Major:
- When designing the five different Noah-MP model scenarios, how did the authors determine the specific parameter values (such as the ponding depth threshold)? Were these parameter values selected based on previous literature, sensitivity analyses, or other considerations?
- In Figure 4, the four VGM schemes exhibit greater variability in wet regions, unlike Figure 6 where their PDF distributions are generally consistent in both arid and wet regions. Ji et al. (2023) enhanced the synergistic effect of high-resolution soil dataset and model parameterizations. Here, this discrepancy may be also caused by differences in how each VGM scheme incorporates local soil moisture dynamics and interactions with vegetation, leading to varying responses across different hydrological regimes.
Reference: Ji, P., Yuan, X., & Jiao, Y. (2023). Synergistic effects of high-resolution factors for improving soil moisture simulations over China. Water Resources Research, 59, e2023WR035513.
3、When comparing different model scenarios with SMAP and ISMN data, the study primarily focuses on the median of SMM. However, the shape and range of SMM distributions could also provide valuable information. Analyzing discrepancies between model-generated SMM distributions and observational data can offer a more comprehensive assessment of model performance.
Minor
- Lines 399-447, The article extensively describes various parameterization schemes of the Noah-MP model, but could further discuss the underlying physical mechanisms of these schemes to help readers better understand their impact on SMM.
- Lines 466-467“The MF_CH experiment displays a spatial pattern that contrasts with the SMAP data, with a longer memory in the arid western regions but a shorter memory in the wet northeastern regions ” What could be the reasons for this spatial distribution?
- While discussing the limitations of ISMN data, the paper mentions the issue of scale differences between point measurements and grid-scale data.I suggested to added some discussions on the high-resolution or hyper-resolution, which might be an efficiency way to solve the scale mismatch between observation and simulation.
- Lines 712-713:” ... processes that influence SMM and to address the commonly observed overestimation/underestimation of long-term/short-term SMM in LSMs.” I suggest to add some implications of these findings for future research on SMM.
Citation: https://doi.org/10.5194/egusphere-2024-1256-RC1
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