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The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented.

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

Soil moisture is an essential variable in Earth surface modeling. Two dedicated satellite missions, the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), are currently in operation to map the global distribution of soil moisture. However, at the longer L-band wavelength of these satellites, the emitting behavior of the land becomes very complex due to the unknown deeper penetration depth. This complexity leads to more uncertainty in calibration and validation of satellite soil moisture product and their applications. In the framework of zeroth-order incoherent microwave radiative transfer model, the soil effective temperature is the only component that contains depth information and thus provides the necessary link to quantify the penetration depth. By means of the multi-layer soil effective temperature (Lv’s T e f f ) scheme, we have determined the relationship between the penetration depth and soil effective temperature and verified it against field observations at the Maqu Network. The key findings are that the penetration depth can be estimated according to Lv’s T e f f scheme with the assumption of linear soil temperature gradient along the optical depth; and conversely, the soil temperature at the penetration depth should be equal to the soil effective temperature with the same linear assumption. The accuracy of this inference depends on to what extent the assumption of linear soil temperature gradient is satisfied. The result of this study is expected to advance understanding of the soil moisture products retrieved by SMOS and SMAP and improve the techniques in data assimilation and climate research.

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 (σ°) 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.

Effective soil temperature T e f f 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, T e f f 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 T e f f . It is found that, on average, nearly 20% of the T e f f 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 T e f f ) 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 T e f f 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 T e f f .

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.

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]

Revegetation is one of the most effective ways to combat desertification and soil erosion in semiarid and arid regions. However, the perturbation of ecohydrological processes revegetation remains to be studied, especially its effect on the complex interaction between the hydrological processes and vegetation growth under water stress. This study evaluated the effects of revegetation on the energy, water and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, Northwest China, by simulating two vegetated scenarios (shrubs-grassland ecosystem and grassland ecosystem) using STEMMUS-SCOPE model. The model was validated by field observations from May to September of 2016-2019. The simulated energy, water and fluxes in 2016 and 2019 were used to evaluate the difference between two vegetated scenarios. Higher leaf area index and root water uptake of C3 shrubs (Caragana Intermedia) resulted in increased carbon fixation (+ 82 %) and transpiration (+ 99 %) in the shrubs-grassland ecosystem compared to C3 grassland ecosystem. In both scenarios, turbulent energy was dominated by latent heat flux, which was stronger in the shrubs-grassland ecosystem (+ 13 %). With the remarkable increase in transpiration, revegetation induced soil water losses, especially the soil water content within the 0-200 cm soil depth (− 19 %), and exaggerated the excess of water consumption over the received precipitation. These results emphasize the importance of accounting for energy and water budget in water-limited ecosystems during ecological restoration, to prevent soil water depletion. As an example, the consequence of increased transpiration should be further examined. [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]

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 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.

Different methods for assessing evapotranspiration (ET) can significantly affect the performance of land surface models in portraying soil water dynamics and ET partitioning. An accurate understanding of the impact a method has is crucial to determining the effectiveness of an irrigation scheme. Two ET methods are discussed: one is based on reference crop evapotranspiration (ET0/ theory, uses leaf area index (LAI) for partitioning into soil evaporation and transpiration, and is denoted as the ETind method; the other is a one-step calculation of actual soil evaporation and potential transpiration by incorporating canopy minimum resistance and actual soil resistance into the Penman-Monteith model, and is denoted as the ETdir method. In this study, a soil water model, considering the coupled transfer of water, vapor, and heat in the soil, was used to investigate how different ET methods could affect the calculation of the soil water dynamics and ET partitioning in a crop field. Results indicate that for two different ET methods this model varied concerning the simulation of soil water content and crop evapotranspiration components, but the simulation of soil temperature agreed well with lysimeter observations, considering aerodynamic and surface resistance terms improved the ETdir method regarding simulating soil evaporation, especially after irrigation. Furthermore, the results of different crop growth scenarios indicate that the uncertainty in LAI played an important role in estimating the relative transpiration and evaporation fraction. The impact of maximum rooting depth and root growth rate on calculating ET components might increase in drying soil. The influence of maximum rooting depth was larger late in the growing season, while the influence of root growth rate dominated early in the growing season. [ABSTRACT FROM AUTHOR]

Quantifying the human effects on water resources plays an important role in river basin management. In this study, we proposed a framework, which integrates the Gravity Recovery and Climate Experiment (GRACE) satellite estimation with macroscale hydrological model simulation, for detection and attribution of spatial terrestrial water storage (TWS) changes. In particular, it provides valuable insights for regions where ground-based measurements are inaccessible. Moreover, this framework takes into account the feedback between land and atmosphere and innovatively put forward several suggestions (e.g., study period selection, hydrological model selection based on soil moisture-climate interactions) to minimize the uncertainties brought by the interaction of human water use with terrestrial water fluxes. We demonstrate the use of the proposed framework in the Yangtze River basin of China. Our results show that, during the period 2003-2010, the TWS was continually increasing in the middle and south eastern reaches of the basin, at a mean rate of about 3 cm yr-1. This increment in TWS was attributed to anthropogenic modification of the hydrological cycle, rather than natural climate variability. The dominant contributor to the TWS excess was found to be intensive surface water irrigation, which recharged the water table in the middle and south eastern parts of the basin. Water impoundment in the Three Gorges Reservoir (TGR) is found to account for nearly 20% of the human-induced TWS increment in the region where the TGR is located. The proposed framework gives water managers/researchers a useful tool to investigate the spatial human effects on TWS changes. [ABSTRACT FROM AUTHOR]

The concept of enhanced vapor transfer in unsaturated soils has been questioned for its reliance on soil temperature gradient, which leads to consideration of other mechanisms of vapor transfer, e.g., advective vapor transfer due to soil air pressure gradient. Although the advective flux is an important portion of evaporation, there is a lack of knowledge of its effect on evaporation. In order to assess the dependence of evaporation on the soil air pressure gradient, a vertical one-dimensional two-phase heat and mass flow model is developed that fully considers diffusion, advection, and dispersion. The proposed model is calibrated with field measurements of soil moisture content and temperature in the Badain Jaran Desert. The proposed model is then used to investigate the advective effect in both low- and high-permeability soils. The advective effect is reflected by underestimating evaporation when the airflow is neglected and is more evident in the tow-permeability soil. Neglecting airflow causes an underestimation error of 53.3% on the day right after a rainfall event in the low-permeability soil (7.9 × 10-4 cm s-1) and 33.3% in the high-permeability soil (2 × 10-3 cm s-1). The comparisons of driving forces and conductivities show that the isothermal liquid flux, driven by the soil matnc potential gradient, is the main reason for the underestimation error. [ABSTRACT FROM AUTHOR]