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Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine learning (ML) based systematic integration of in-situ hydrological measurements, complex environmental and climate data and satellite observation facilitate to generate the best data products to monitor and analyse the exchanges of water, energy and carbon in the Earth system at a proper space-time resolution. This study investigates the estimation of daily SSM using eight optimised ML algorithms and ten ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using the international soil moisture network (ISMN) data collected from 1722 stations distributed across the World. The result showed that K-neighbours Regressor (KNR) performs best on "test_random" set, while Random Forest Regressor (RFR) performs best on "test_temporal" and "test_independent-stations". Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSEs were below 0.1 cm3/cm3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and Random Forest Regressor (RFR) achieved a median r score of 0.6 in twelve, eleven, nine and nine climate zones, respectively, out of fifteen climate zones. The performance of ensemble models improved significantly with the median value of RMSE below 0.075 cm3/cm3 for all climate zones . All voting regressors achieved the r scores of above 0.6 in thirteen climate zones except BSh and BWh because of the sparse distribution of training stations. The metrical evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and XB performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability for predicting SSM compared to the optimised, or base ML algorithms, and indicates their huge potential applicability in estimating water cycle budgets, managing irrigation and predicting crop yields. [ABSTRACT FROM AUTHOR]

How to efficiently and physically integrate the soil water dynamics with groundwater flow processes has drawn much attention. We present a coupled soil water-groundwater model, considering the two-way feedback coupling scheme, and verified its performance using two synthetic cases (using the fully 3D variably saturated flow (VSF) model simulations as the 'reference') and one real catchment 25 case (using the groundwater table depth and soil moisture profile measurements). By the cross-validation between the observations and various model simulations, the two-way coupling approach is proven physically accurate and is applicable for large-scale groundwater flow problems. Compared to the simulation by groundwater model alone (i.e., only MODFLOW), the coupling of MODFLOW with one soil column reduced the overestimation of groundwater table simulation (taking the VSF model simulations as the reference). The results were further improved as more soil columns were used to represent the heterogeneous soil watergroundwater interactions. Compared to the HYDRUS-MODFLOW, the two-way coupling approach produces a similar spatial distribution of hydraulic heads while better performs in mimicking the temporal dynamics of groundwater table depth and soil moisture profiles. We attribute the better performance to the different coupling strategies across the soil-water and groundwater interface. It is thus suggested to adopt the two-way feedback coupling scheme, together with the moving phreatic boundary and multi-scale water balance analysis, to maintain physical consistency and reduce coupling errors. The realistic implementation of the vadose zone processes (with STEMMUS), coupling approach, and spatiotemporal heterogeneity of soil water-groundwater interactions were demonstrated critical to accurately represent an integrated soil water-groundwater system. The developed STEMMUS-MODFLOW model can be further equipped with different complexities of soil physics (e.g., coupled soil water and heat transfer, freeze-thaw, airflow processes), surface hydrology (snowfall, runoff), soil and plant biogeochemical processes, towards an integrated "from bedrock to atmosphere" modeling framework. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Detailed knowledge of its hydrogeology is paramount to enable the understanding of groundwater dynamics, which plays a vital role in headwater areas like the Tibetan Plateau. Nevertheless, due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. In this study, borehole core lithology analysis, altitude survey, soil thickness measurement, hydrogeological survey, and hydrogeophysical surveys (e.g., Magnetic Resonance Sounding – MRS, Electrical Resistivity Tomography – ERT, and Transient Electromagnetic – TEM) were conducted in the Maqu catchment within the Yellow River Source Region (YRSR). The soil thickness measurements were done in the western mountainous area of the catchment, where hydrogeophysical surveys were difficult to be carried out. The results indicate soil thicknesses are within 1.2 m in most cases, and the soil thickness decreases as the slope increases. The hydrogeological survey reveals that groundwater flows from the west to the east, recharging the Yellow River. The hydraulic conductivity ranges from 0.2 m/d to 12.4 m/d. The MRS soundings results, i.e., water content and hydraulic conductivity, confirmed the presence of unconfined aquifer in the flat eastern area. The depth of the Yellow River deposits was derived at several places in the flat eastern area based on TEM results. These survey data and results can be used to develop integrated hydrological modeling and water cycle analysis to improve a full–picture understanding of the water cycle at the Maqu catchment in the YRSR. The raw data set is freely available at https://doi.org/10.17026/dans-z6t-zpn7 (Li et al., 2020). [ABSTRACT FROM AUTHOR]

Soil information (e.g. soil texture and porosity) from existing soil datasets over the Tibetan Plateau (TP) is claimed to be inadequate and even inaccurate for determining soil hydraulic properties(SHP) and soil thermal properties (STP), hampering the understanding of the land surface process. As the soil varies across three dominant climate zones (i.e. arid, semi-arid, and semi-humid) over the TP, the associated SHP/STP is expected to vary correspondingly. To obtain an explicit insight into the soil hydro-thermal consistency for land surface modelling over the TP, in situ and laboratory measurements of over 40 soil property profiles were obtained across the climate zones. Results show that porosity and SHP/STP differ across the climate zones and strongly depend on soil texture. In particular, it is proposed that gravel impact on porosity and SHP/STP are both considered in the arid zone and in deep layers of the semi-arid zone. Parameterization schemes for porosity, SHP and STP are investigated and compared with measurements taken. This reveals that the porosity determined by the bulk density scheme is the most applicable for the TP. To determine the SHP, including soil water retention curves and hydraulic conductivities, the pedotransfer functions (PTFs) developed by Cosby et al. (1984) (for the Clapp-Hornberger model) and the continuous Wösten et al. (1999) (for the van Genuchten-Mualem model) are recommended. The STP parameterization scheme proposed by Farouki (1981) based on the mode of De Vries (1963) performed better across the TP than other schemes. Using the parameterization schemes mentioned above, the uncertainties of five existing regional and global soil datasets and their derived SHP/STP over the TP are quantified through comparison with in situ and laboratory measurements. [ABSTRACT FROM AUTHOR]

The article discusses satellite missions like the Heat Capacity Mapping Mission (HCMM) and Global Monitoring for Environment and Security (GMES) program which perform remote sensing of land-atmosphere interface characteristics including precipitation, heat fluxes and soil moisture for climate modelling studies. According to the author, these programs provide systematic data for Earth observation science.

Effective soil temperature Teff is one of the basic parameters in passive microwave remote sensing of soil moisture. At present, dedicated satellite soil moisture monitoring missions use the L-band as the operating frequency. However, Teff at the L-band is strongly affected by soil moisture and temperature profiles. Recently, a two-layer scheme and a corresponding multilayer form have been developed to accommodate such influences. In this study, the soil moisture/temperature data collected and simulated by the Noah land surface model across the Maqu Network are used to verify the newly developed schemes. There are two key findings. Firstly, the new two-layer scheme is able to assess which site provides relatively higher accuracy when estimating Teff. It is found that, on average, nearly 20% of the Teff signal cannot be captured by the Maqu Network, in the currently assumed common installation configuration. This knowledge is important, since the spatial averaged brightness temperature (a function of Teff) is used to determine soil moisture. Secondly, the developed method has made it possible to identify that the optimal mounting depths for the observation pair are 5 cm and 20 cm for calculating Teff at the center station in the Maqu Network. It has been suggested that the newly developed method can provide an objective way to configure an optimal soil moisture/temperature network and improve the representativeness of the existing networks regarding the calculation of Teff. [ABSTRACT FROM AUTHOR]

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products. [ABSTRACT FROM AUTHOR]

Soil moisture and soil temperature (SMST) are important state variables for quantifying exchange of heat and water between land and atmosphere. Yet, long-term regional-scale in-situ SMST measurements are scarce on the Tibetan Plateau (TP), even fewer are available for multiple soil depths. "Tibet-Obs" is such a long-term regional-scale SMST observatory in the TP established 10 years ago that includes three SMST monitoring networks, i.e., Maqu, Naqu, and Ngari (including Ali and Shiquanhe), located in the cold humid area covered by short grasses, the cold semiarid area dominated by tundra, and the cold arid area dominated by desert, respectively. This paper presents a long-term (~10 years) SMST profile dataset collected from the Tibet-Obs, which includes the original in-situ measurements at a 15-min interval collected between 2008 and 2019 from all the three networks and the spatially upscaled data (SMups and STups) for the Maqu and Shiquanhe networks. The quality of the upscaled data is proved to be good with errors that are generally better than the measured accuracy of adopted SMST sensors. Long term analysis of the upscaled SMST profile data shows that the amplitudes of SMST variations decrease with increasing soil depth, and the deeper soil layers present later onset of freezing and earlier start of thawing and thus shorter freeze-thaw duration in both Maqu and Shiquanhe networks. In addition, there are notably differences noted between the relationships of SMups and STups under freezing conditions for the Maqu and Shiquanhe networks. No significant trend can be found for the SMups profile in the warm season (from May to October) for both networks that is consistent with the tendency of precipitation. Similar finding is also found for the STups profile and air temperature in the Shiquanhe network during the warm season. For the cold season (from November to April), a drying trend is noted for the SMups above 20 cm in the Maqu network, while no significant trend is found for those in the Shiquanhe network. Comparisons between the long-term upscaled data and five reanalysis datasets indicate that none of current model-based products can reproduce the seasonal variations and inter-annual trend changes of measured SMST profile dynamics in both networks. All the products underestimate the STups at every depth, leading to earlier onset of freezing and later onset of thawing, which essentially demonstrates the current model are not able to adequately simulate winter conditions on the TP. In short, the presented dataset would be valuable for evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/20141567.v1. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σº) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson's correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites. [ABSTRACT FROM AUTHOR]