Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

Tian, Jie; Han, Zhibo; Bogena, Heye; Huisman, Johan Alexander; Montzka, Carsten; Zhang, Baoqing; He, Chansheng

doi:10.5194/hess-24-4659-2020

Cited by 25 publications

(13 citation statements)

References 74 publications

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“…The estimation accuracy shows a trend of gradually decreasing from shallow to deep layers (Table S4 in Supporting Information S1). The accuracy here is similar to previous studies (Tian et al., 2020).…”

Section: Methodssupporting

confidence: 92%

“…Errors in the soil moisture method are mainly due to deformation and soil moisture. Because of few soil moisture observations in the deep layers and increasing error with depth in the ExpF method (Tian et al., 2020), errors in predicting soil moisture for deep layers can result. Furthermore, the model only considers the relationship between the volume change due to the water‐ice phase change, and the magnitude of the seasonal displacement.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have found that each site will have a different optimal characteristic duration (

{T}_{\mathrm{o}\mathrm{p}\mathrm{t}}

). In addition, the use of a regional generalization in the upper Heihe River is better than using a global generalized value as a site‐specific value (Tian et al., 2020). Therefore, we calculate the simulated values of

{T}_{\mathrm{o}\mathrm{p}\mathrm{t}}

between 1 and 60 for all stations at different depths, compare them with the deeper measured data, and calculate the value with the highest Nash‐Sutcliffe efficiency coefficient as the

{T}_{\mathrm{o}\mathrm{p}\mathrm{t}}

at that depth.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau

Peng

Frauenfeld

et al. 2023

JGR Earth Surface

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In cold regions characterized by perennially frozen soil, climate warming has caused permafrost degradation, which is manifested by surface deformation and deepening of the active layer. Interferometric synthetic aperture radar (InSAR) is a common method to obtain large‐scale surface deformation, especially in mountainous terrain where it is difficult to install a large number of monitoring sites. Here, we used Sentinel‐1A SAR data acquired from March 2017 to March 2021 to monitor surface deformation and estimate active layer thickness (ALT) using two methods—temperature‐based and soil moisture‐based—in the permafrost regions of the Heihe River Basin on the northeast Qinghai‐Tibetan Plateau. We find that the seasonal deformation amplitude ranges 10–60 mm, and the annual mean vertical deformation trend ranges from −40 to 30 mm/a. We verify the ALT estimates with observations and find that the method based on soil moisture has higher accuracy and is therefore more applicable in this particular study area. The ALT gradually decreases from the southeast to the northwest, ranging from 0.3 to 6.5 m, with an average value of 2.47 m. These results contribute to better quantifying the response of permafrost change and to understanding the spatial distribution of ALT on a large scale, serving as guidance for engineering and other applications.

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Section: Methodssupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

“…Previous studies have found that each site will have a different optimal characteristic duration (

{T}_{\mathrm{o}\mathrm{p}\mathrm{t}}

{T}_{\mathrm{o}\mathrm{p}\mathrm{t}}

between 1 and 60 for all stations at different depths, compare them with the deeper measured data, and calculate the value with the highest Nash‐Sutcliffe efficiency coefficient as the

{T}_{\mathrm{o}\mathrm{p}\mathrm{t}}

at that depth.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau

Peng

Frauenfeld

et al. 2023

JGR Earth Surface

Self Cite

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show abstract

“…Earlier studies explored the coupling strength between surface SM and surface air temperature using several analytical techniques and model simulation experiments (Miralles et al 2012, Seneviratne et al 2013, Dong andCrow 2018, Ganeshi et al 2023). Similarly, the coupling strength between surface SM and subsurface SM was explored using the crosscorrelation analysis (Tian et al 2020, Xu et al 2022.…”

Section: Analytical Techniquesmentioning

confidence: 99%

Understanding the soil water dynamics during excess and deficit rainfall conditions over the core monsoon zone of India

Goswami,

Mujumdar,

Singh

et al. 2023

Environ. Res. Lett.

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Observations of soil moisture (SM) during excess and deficit monsoon seasons between 2000 to 2021 present a unique opportunity to understand the soil water dynamics (SWD) over core monsoon zone (CMZ) of India. This study aims to analyse SWD by investigating the SM variability, SM memory (SMM), and the coupling between surface and subsurface SM levels. Particularly intriguing are instances of concurrent monsoonal extremes, which give rise to complex SWD patterns. Usually, it is noted that a depleted convective activity and persistence of higher temperatures during the pre-monsoon season leads to lower SM, while monsoon rains and post-monsoon showers support the prevalence of higher SM conditions. The long persistent dry spells during deficit monsoon years enhances the Bowen ratio (BR) due to the high sensible heat fluxes. On the other hand, the availability of large latent heat flux during excess monsoon and post-monsoon seasons tend to decrease the BR. This enhancement or reduction in BR is due to evapotranspiration (ET), which influences the SWD by modulating the surface—subsurface SM coupling. The surface and subsurface SM coupling analysis for CMZ exhibits significant distinction in the evolution of wet and dry extremes. SM variations and persistence time scale is used as an indicator of SMM, and analysed for both surface and subsurface SM observation levels. Evidently, subsurface SM exhibits remarkably prolonged memory timescales, approximately twice that of surface SM. Furthermore, we dissect SWD linked to wet and dry extremes by analysing annual soil water balance at a local site in Pune, India. Our findings reveal that ET and deep drainage on annual scale are modulated largely by number of break events during the monsoon season. In essence, our study underscores the significance of surface–subsurface SM observations in unravelling the intricate tapestry of SWD.

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“…We agree that the potential of data-driven models, including ANN models, are not currently being fully exploited and that traditional models can be used to improve datadriven models for hydrological applications. To date, there have been a few examples of integrating traditional and data-driven models that can be found in the literature, such as the estimation of root-zone soil moisture from different soil profiles [59], meteorological forcings based on data-driven models [60], and the delineation of groundwater potential zones by an ANN model [61]. Numerous challenges remain unsolved in hydrology but integrating the two types of model may lead to interesting improvements [62].…”

Section: Comparison With Physically Based Modelsmentioning

confidence: 99%

Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs

Jougla

Leconte

2022

Water

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In hydrological modelling, artificial neural network (ANN) models have been popular in the scientific community for at least two decades. The current paper focuses on short-term streamflow forecasting, 1 to 7 days ahead, using an ANN model in two northeastern American watersheds, the Androscoggin and Susquehanna. A virtual modelling environment is implemented, where data used to train and validate the ANN model were generated using a deterministic distributed model over 16 summers (2000–2015). To examine how input variables affect forecast accuracy, we compared streamflow forecasts from the ANN model using four different sets of inputs characterizing the watershed state—surface soil moisture, deep soil moisture, observed streamflow the day before the forecast, and surface soil moisture along with antecedent observed streamflow. We found that the best choice of inputs consists of combining surface soil moisture with observed streamflow for the two watersheds under study. Moreover, to examine how the spatial distribution of input variables affects forecast accuracy, we compared streamflow forecasts from the ANN using surface soil moisture at three spatial distributions—global, fully distributed, and single pixel-based—for the Androscoggin watershed. We show that model performance was similar for both the global and fully distributed representation of soil moisture; however, both models surpass the single pixel-based models. Future work includes evaluating the developed ANN model with real observations, quantified in situ or remotely sensed.

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Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

Cited by 25 publications

References 74 publications

Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau

Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau

Understanding the soil water dynamics during excess and deficit rainfall conditions over the core monsoon zone of India

Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs

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