Soil Moisture Memory of Land Surface Models Utilized in Major Reanalyses Differ Significantly From SMAP Observation

He, Qing; Lu, Hui; Yang, Kun

doi:10.1029/2022ef003215

Cited by 7 publications

(21 citation statements)

References 68 publications

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“…The time-lag duration between FVC and climate changes was correlated with the topographical characteristics: most of the research area received a 1-2 months' time lag between FVC and PRE and TEM changes, whereas a 6 months' time lag was detected in the conjunction area of GZP, QBM, and LOP. Previous studies have shown that soil temperature and soil moisture exhibit memory effects [59,60], and climate change can affect the temperature and water content of soil. This is likely the reason for the lagged response between vegetation and climate factors.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China

Li,

Sun,

Wang

et al. 2023

Applied Sciences

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Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year−1 from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China

Li,

Sun,

Wang

et al. 2023

Applied Sciences

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“…θ w , θ c , and θ fc refers to soil wilting point, critical point, and field capacity, respectively. Figures adapted from (He et al., 2023).…”

Section: Methodsmentioning

confidence: 99%

“…However, these improvement studies are model specific, that is to say, SM biases cannot be improved if the models already use the optimal parameterization schemes or land surface parameters. The systematical SM biases (i.e., the long‐term equilibrium SM) determined by soil texture between models should be improved through the calibration of soil texture data sets utilized in current LSMs (He et al., 2023; Teuling et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Soil Texture Maps From Satellite‐Observed Soil Moisture Drydowns and Its Implementation in Noah‐MP Land Surface Model

He,

Lu,

Yang

et al. 2024

J Adv Model Earth Syst

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Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large‐scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in‐situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite‐estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite‐optimized soil texture maps is improved at both grid and in‐situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation‐oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs).

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“…Encompassing a broader perspective, Ruscica et al (2014) assumed that anomalous soil moisture impacts the atmospheric state through complicated land surface feedback mechanisms that span across diurnal to seasonal timescales. The multifaceted nature of SMM finds expression in the explanation offered by He et al (2023), who propose two distinct but not independent descriptions: one represents the SMM as the temporal duration required for a perturbation to manifest and decay in the time domain (irreversible changes), while the second definition relates to the time taken for soil moisture to regain equilibrium following a perturbation (reversible changes). In any case, the perturbations considered so far encompass a diverse array of wet anomalies like precipitation or dry anomalies like drought.…”

Section: Conceptmentioning

confidence: 99%

“…As field capacity is used directly in the autocorrelation expression of soil moisture (Koster & Suarez, 2001;Seneviratne & Koster, 2012), it can be a good candidate for studying the effects of other soil properties on SMM. The t SMM increases with greater soil depth (Amenu et al, 2005;Asharaf & Ahrens, 2013;Douville et al, 2007;He et al, 2023;MacDonald & Huffman, 2004;Martínez-Fernández et al, 2021;Ruscica et al, 2014;Song et al, 2019;Wu et al, 2002), as deeper layers exhibit higher organic and clay contents (Martínez-Fernández et al, 2021), larger magnitudes of soil moisture spectra (Asharaf & Ahrens, 2013), and slower drying times after precipitation events. 3.…”

Section: Reviews Of Geophysicsmentioning

confidence: 99%

Soil Moisture Memory: State‐Of‐The‐Art and the Way Forward

Rahmati,

Amelung,

Brogi

et al. 2024

Reviews of Geophysics

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Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM.

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Soil Moisture Memory of Land Surface Models Utilized in Major Reanalyses Differ Significantly From SMAP Observation

Cited by 7 publications

References 68 publications

Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China

Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China

Global Optimization of Soil Texture Maps From Satellite‐Observed Soil Moisture Drydowns and Its Implementation in Noah‐MP Land Surface Model

Soil Moisture Memory: State‐Of‐The‐Art and the Way Forward

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