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The freezing front depth (zff) of annual freeze-thaw cycles is critical for monitoring the dynamics of the cryosphere under climate change because zff is a sensitive indicator of the heat balance over the atmosphere-cryosphere interface. Meanwhile, although it is very promising for acquiring global soil moisture distribution, the L-band microwave remote sensing products over seasonally frozen grounds and permafrost is much less than in wet soil. This study develops an algorithm, i.e., the brightness temperature inferred freezing front (BT-FF) model, for retrieving the interannual zff with the diurnal amplitude variation of L-band brightness temperature (ΔTB) during the freezing period. The new algorithm assumes first, the daily-scale solar radiation heating/cooling effect causes the daily surface thawing depth (ztf) variation, which leads further to ΔTB; second, ΔTB can be captured by an L-band radiometer; third, ztf and zff are negatively linear correlated and their relation can be quantified using the Stefan equation. In this study, the modeled soil temperature profiles from the land surface model (STEMMUS-FT, i.e., simultaneous transfer of energy, mass, and momentum in unsaturated soil with freeze and thaw) and TB observations from a tower-based L-band radiometer (ELBARA-III) at Maqu are used to validate the BT-FF model. It shows that, first, ΔTB can be precisely estimated from ztf during the daytime; second, the decreasing of ztf is linearly related to the increase of zff with the Stefan equation; third, the accuracy of retrieved zff is about 5-25 cm; fourth, the proposed model is applicable during the freezing period. The study is expected to extend the application of L-band TB data in cryosphere/meteorology and construct global freezing depth dataset in the future.

Soil moisture and soil temperature (SMST) are important state variables for quantifying the 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), with even fewer are available for multiple soil depths. Tibet-Obs is such a long-term, regional-scale SMST observatory in the TP that has been established 10 years ago and includes three SMST monitoring networks, i.e. Maqu, Naqu, and Ngari (including Ali and Shiquanhe), located in the cold humid area covered by grassland, 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 a later onset of freezing and an earlier start of thawing and, thus, a shorter freeze–thaw duration in both the Maqu and Shiquanhe networks. In addition, there are notable differences 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. A 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, including the ECMWF reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), Global Land Data Assimilation System version 2 Catchment Land Surface Model (GLDAS-2.1 CLSM), GLDAS-2.1 Noah, and GLDAS-2.1 variable infiltration capacity (GLDAS-2.1 VIC), indicate that none of the current model-based products can reproduce the seasonal variations and interannual trend changes in the measured SMST profile dynamics in both networks. All the products underestimate the STups at every depth, leading to an earlier onset of freezing and a later onset of thawing, which essentially demonstrates that the current models are not able to adequately simulate winter conditions on the TP. In short, the presented dataset would be valuable for evaluation and improvement in 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 (Zhang et al., 2022).

The extent and mechanisms by which precipitation extremes affect hydrological processes in the critical zone (CZ) of loess possessing a thick unsaturated zone remains poorly understood. To this end, we here employed a coupled liquid‒vapor‒heat‒airflow STEMMUS (simultaneous transfer of energy, mass, and momentum in unsaturated soil) model to investigate the impact of extreme precipitation, at both event and annual scales, on CZ hydrological processes within a semiarid loess site vegetated by apple trees on China’s Loess Plateau. At the event scale, the vapor flux was two orders of magnitude lower than the liquid water flux. However, the thermal vapor flux penetrated to depths of 200 cm, whereas the isothermal liquid water flux only infiltrated to 100 cm during the study period, implying that thermal-gradient-driven vapor transfer is an important mechanism for deep-layer recharge (DLR). At the annual scale, the DLR below 200 cm during extremely wet years was 6.5 times larger than that during extremely dry years. In contrast, extreme changes in climate had only limited impacts on evapotranspiration; the difference between extremely wet and extremely dry years averaged only 35 mm, much less than the difference in precipitation, which averaged 310 mm. However, the extremely dry years showed a higher ratio (0.58) between transpiration and evapotranspiration than did the extremely wet years (0.51). The findings reported here improve our understanding of CZ hydrological processes related to precipitation extremes in semiarid loess regions.

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

Evapotranspiration (ET) is a key variable in terrestrial water, energy and carbon cycles. ET products have proliferated. This study evaluates ten monthly, globally available products, including one product from the upscaling of in situ observations (FLUXCOM), one ensemble product (SynthesisET), four remote sensing-based products (SSEBop, MOD16, Numerical Terradynamic Simulation Group (NTSG) and PT-JPLSM) and four products from land surface models (Global Land Data Assimilation System (GLDAS), FLDAS, TerraClimate and Global Land Evaporation Amsterdam Model (GLEAM)). The assessments are conducted during the period of 2003–2013 using FLUXNET2015 eddy covariance datasets at the site scale, which are grouped by land cover, elevation and climate, and GRACE-based water balance ET at basin scales. The results indicate that all products show comparable performance and that no single product shows the best performance. FLUXCOM and GLDAS have outstanding performances at both the site and basin scales, respectively. SynthesisET tends to have suboptimal performance at both validation scales, while FLDAS, SSEBop and TerraClimate show relatively poor results. Other products reproduced ET moderately well. The metrics of the two validation methods are compared. The results indicated that the performance of products at the basin scale is usually better than that at the site scale. The anomalies in specific regions and the trends among ET products are observed at both scales. In addition, the special relationship between the products and the validation methods will affect the credibility of their assessment results. This study contributes to the assessment of the performance of products to identify proper candidates for hydrological analysis and improve the ET algorithm.

The dynamic change of frozen soil is crucial to land-surface modeling, carbon feedback studies, ground engineering (e.g., constructions), and microwave remote sensing. L-Band satellite missions Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) are currently exploited to characterize soil into freeze or thaw (FT) states. However, brightness temperatures (TB) at L-band contain more information besides the FT state, particularly over permafrost or seasonally frozen soil, which has not been explored via current retrieval algorithms. To examine the potential for L-band TB observations, we define an index called Profile-Averaged Frozen Soil Fraction (Ff) related to Diurnal Amplitude Variation (DAV) of TB (i.e., ΔTB) based on the optical depth of the frozen soil column. We evaluated Ff inferred from the 0th-order microwave transfer model with the SMAP L1c product, the ground-based European Space Agency L-Band Radiometer III (ELBARA-III) TB observations, and temperature profiles collected at the Maqu station in northeastern Tibet. While there is a clear relationship between Ff and ΔTB, no apparent link exists with the ice content fraction (Ffi) within a fixed-depth soil column. The proposed model certifies that the profile-averaged soil ice content Ff relates to the dynamic microwave penetration depth by math and field measurement. The model reproduces well ΔTB in Period Freezing but has problems in Period Thawing when melted surface water obstruct the microwave signals. Our findings can be used to exploit ΔTB between 6 am and 6 pm, as a typically overpassing time by the SMOS and SMAP satellites, for estimating Ff, which can be further applied in weather/climate forecasting and for improving land-surface modeling.

Freezing-induced groundwater-level decline is widely observed in regions with a shallow water table, but many existing studies on freezing-induced groundwater migration do not account for freezing-induced water-level fluctuations. Here, by combining detailed field observations of liquid soil water content and groundwater-level fluctuations at a site in the Ordos Plateau, China, and numerical modeling, we showed that the interaction of soil water and groundwater dynamics was controlled by wintertime atmospheric conditions and topographically driven lateral groundwater inflow. With an initial water table depth of 120 cm and a lateral groundwater inflow rate of 1.03 mm d−1, the observed freezing and thawing-induced fluctuations of soil water content and groundwater level are well reproduced. By calculating the budget of groundwater, the mean upward flux of freezing-induced groundwater loss is 1.46 mm d−1 for 93 d, while the mean flux of thawing-induced groundwater recharge is as high as 3.94 mm d−1 for 32 d. These results could be useful for local water resources management when encountering seasonally frozen soils and for future studies on two- or three-dimensional transient groundwater flow in semi-arid and seasonally frozen regions. By comparing models under a series of conditions, we found the magnitude of freezing-induced groundwater loss decreases with initial water table depth and increases with the rate of groundwater inflow. We also found a fixed-head lower boundary condition would overestimate freezing-induced groundwater migration when the water table depth is shallow. Therefore, an accurate characterization of freezing-induced water table decline is critical to quantifying the contribution of groundwater to hydrological and ecological processes in cold regions.

The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) was established ten years ago, which has been widely used to calibrate/validate satellite- and model-based soil moisture (SM) products for their applications to the Tibetan Plateau (TP). This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface SM dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs that consists of three regional-scale SM monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected by multiple SM monitoring sites of the three networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks. Comparisons between four spatial upscaling methods, i.e. arithmetic averaging, Voronoi diagram, time stability and apparent thermal inertia, show that the arithmetic average of the monitoring sites with long-term (i.e. ≥ six years) continuous measurements are found to be most suitable to produce the upscaled SM records. Trend analysis of the 10-year upscaled SM records using the Mann-Kendall method shows that the Maqu network area in the eastern part of the TP is drying while the Shiquanhe network area in the west is getting wet that generally follow the change of precipitation. To further demonstrate the uniqueness of the upscaled SM records in validating existing SM products for long term period (~ 10 years), comparisons are conducted to evaluate the reliability of three reanalysis datasets for the Maqu and Shiquanhe network areas. It is found that current model-based SM products still show deficiencies in representing the trend and variation of measured SM dynamics in the Tibetan grassland (i.e. Maqu) and desert ecosystems (i.e. Shiquanhe) that dominate the landscape of the TP. The dataset would be also valuable for calibrating/validating long-term satellite-based SM products, evaluation of SM upscaling methods, development of data fusion methods, and quantifying the coupling strength between precipitation and SM at 10-year scale. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/uuid:21220b23-ff36-4ca9-a08f-ccd53782e834 (Zhang et al., 2020).

In cold regions, freeze-thaw cycles play a critical role in many engineering and agricultural applications and cause soil water flow and heat transport studies to be much more complicated due to phase changes involved. A fully coupled numerical module for simulating the simultaneous movement of water, vapor, and heat during freezing-thawing periods was developed and incorporated in the Hydrus-1D software in this study. To avoid numerical instabilities caused by a sudden increase in the apparent heat capacity during a phase change, a new approach based on the available energy concept was adopted to adjust soil temperature when the freezing temperature is reached. The proposed freezing module's performance was then validated using experimental data collected at three field sites with typical seasonal freezing/thawing processes. Results showed that the model could efficiently obtain a convergent solution and that simulated soil moisture and temperature variations captured the observed data well. Driven by soil matric potential and temperature gradients, both liquid water and water vapor flowed towards the freezing front. The isothermal liquid flux was the most significant component of overall flow in most soil depths except in the frozen layer, where it decreased by 1–5 orders of magnitude from values before freezing. Instead, the thermal vapor flux was the dominant moisture transfer mechanism in the frozen layer and contributed about 10% to the ice formation. These results indicate that the model, which considers the coupled movement of water, vapor, and heat, can better describe the physical mechanisms of the hydrological cycle in the vadose zone during the freezing-thawing periods. © 2021 Elsevier B.V.

Knowing the Freeze-Thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Near-surface air temperature and land surface temperature are usually used in meteorology to infer the FT-state. However, the uncertainty is large because both temperatures can hardly be distinguished from remote sensing. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not known sufficiently well. We present a new FT-state detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. The exploitation of the daily variation signal allows for a more reliable state detection, particularly during the transitions periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98 % in summer and up to 75 % in winter. Compared to the FT state inferred indirectly from the 2-m air temperature of the ERA5-land reanalysis, the new FT algorithm has a similar performance as the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s 2-m air temperature estimate. This study is expected to extend L-band microwave remote sensing data for improved FT detection.

Anthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land-atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario-based projections of global spatially explicit LULC at 1-km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.

Accurate estimation of above-ground biomass (AGB) plays a significant role in characterizing crop growth status. In precision agriculture area, a widely-used method for measuring AGB is to develop regression relationships between AGB and agronomic traits extracted from multi-source remotely sensed images based on unmanned aerial vehicle (UAV) systems. However, such approach requires expert knowledges and causes the information loss of raw images. The objectives of this study are to (i) determine how multi-source images contribute to AGB estimation in single and whole growth stages; (ii) evaluate the robustness and adaptability of deep convolutional neural networks (DCNN) and other machine learning algorithms regarding AGB estimation. To establish multi-source image datasets, this study collected UAV red-green-blue (RGB), multispectral (MS) images and constructed the raster data for crop surface models (CSMs). Agronomic features were derived from the above-mentioned images and interpreted by the multiple linear regression, random forest, and support vector machine models. Then, a DCNN model was developed via an image-fusion architecture. Results show that the DCNN model provides the best estimation of maize AGB when a single type of image is considered, while the performance of DCNN degrades when sufficient agronomic features are used. Besides, the information of above three image datasets changes with various growth stages. The structure information derived from CSM images are more valuable than spectrum information derived from RGB and MS images in the vegetative stage, but less useful in the reproductive stage. Finally, a data fusion strategy was proposed according to the onboard sensors (or cost).

Anaerobics increase resistance to gas transport and microbial activity in flooded soils. This may result in the presence of aerenchyma in the roots of some wetland plants. Increased aerenchyma airspaces enable oxygen to be transported from the above-ground plant parts to the submerged roots and rhizosphere. Nevertheless, there is still a lack of studies linking field experiments and eco-hydrological modeling to the parameterization of the physiological responses of typical wetland plant species to natural flooding events. Furthermore, from the modeling perspective, the contribution of aerenchyma was not sufficiently considered. The goal of this study was to develop and apply an eco-hydrological model capable of simulating various patterns of plant physiological responses to natural flooding events based on key processes of root oxygen diffusion and aerenchyma functioning in a variably-saturated wetland soil environment. Eco-hydrological experiments were conducted accordingly, with surface water level, root-zone soil water content, soil temperature, leaf net photosynthesis rate and root morphology monitored simultaneously in situ at a site dominated by meadow species Deyeuxia angustifolia (Kom.) Y. L. Chang and invaded shrub species Salix rosmarinifolia Linn. var. brachypoda (Trautv.et Mey.) Y.L. Chou in a typical natural floodplain wetland. The results are as follows: (1) Root oxygen respiration rates are strongly correlated with leaf net photosynthesis rates of the two plant types, particularly under flooding conditions during the growing season; (2) Meadow species with a preference for wet microhabitats has a competitive advantage over first-year invading shrub species during flooding events; and (3) an aerenchyma sub-model could improve the eco-hydrological model’s accuracy in capturing plant physiological responses. These findings have the potential to contribute to the management of wetland and its restorations.

This paper coupled a unified passive and active microwave observation operator—namely, an enhanced, physically-based, discrete emission-scattering model—with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature (Formula presented.) ((Formula presented.) = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of (Formula presented.) lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of (Formula presented.) reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

The vadose zone is a zone sensitive to environmental changes and exerts a crucial control in ecosystem functioning and even more so in cold regions considering the rapid change in seasonally frozen ground under climate warming. While the way in representing the underlying physical process of the vadose zone differs among models, the effect of such differences on ecosystem functioning and its ecohydrological response to freeze–thaw cycles are seldom reported. Here, the detailed vadose zone process model STEMMUS (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) was coupled with the ecohydrological model Tethys–Chloris (T&C) to investigate the role of influential physical processes during freeze–thaw cycles. The physical representation is increased from using T&C coupling without STEMMUS enabling the simultaneous mass and energy transfer in the soil system (liquid, vapor, ice) – and with explicit consideration of the impact of soil ice content on energy and water transfer properties – to using T&C coupling with it. We tested model performance with the aid of a comprehensive observation dataset collected at a typical meadow ecosystem on the Tibetan Plateau. Results indicated that (i) explicitly considering the frozen soil process significantly improved the soil moisture/temperature profile simulations and facilitated our understanding of the water transfer processes within the soil–plant–atmosphere continuum; (ii) the difference among various representations of vadose zone physics have an impact on the vegetation dynamics mainly at the beginning of the growing season; and (iii) models with different vadose zone physics can predict similar interannual vegetation dynamics, as well as energy, water, and carbon exchanges, at the land surface. This research highlights the important role of vadose zone physics for ecosystem functioning in cold regions and can support the development and application of future Earth system models.

The Guanzhong Plain is a critical food production area in the Yellow River Basin that frequently suffers from water shortages. In this study, long-term (June 2013 to June 2018) water and energy fluxes were observed, and path analysis was conducted over an irrigated winter wheat (Triticum aestivum L.) / summer maize (Zea mays L.) rotation field to identify the controlling factors of evapotranspiration (ET). Total ET for each crop year ranged from 627 to 775 mm, with an average growing season ET of 398 mm for wheat and 310 mm for maize. There is significant seasonal variation in both ET and surface conductance (Gs). Daily ET varied from 0.0 to 6.0 mm d–1 for wheat and 0.0 to 6.7 mm d–1 for maize. The peak daily values of Gs were 29.5 mm s–1 for wheat and 19.5 mm s–1 for maize. The direct and indirect effects of environmental and biological factors—net radiation (Rn), surface conductance (Gs), saturation vapor pressure deficit (VPD), leaf area index (LAI), air temperature (Tair), and volumetric soil water content (VWC)—on ET were calculated using the path analysis method. Rn was determined to be the primary controlling factor of ET for both the summer maize and winter wheat growing seasons. Also, Gs was found to be another controlling factor that has more controlling power in the summer maize growing season than in the winter wheat season. VPD had a significant positive and direct effect on ET for both of the crop seasons, while it had a significant negative and indirect effect on ET through Gs in the summer maize season. VWC and Tair only directly affected the wheat ET. In addition, VWC had two significant paths that can indirectly affect ET through LAI and Gs. The revealed seasonal patterns and controlling factors of evapotranspiration in this agroecosystem provide a theoretical basis for optimizing water resources management of the Yellow River.

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error 3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

In order to improve the simulation accuracy of directional brightness temperature (DBT) and the retrieval accuracy of component temperature, a model considering intra-row heterogeneity to simulate the DBT angular distribution over crop canopy is proposed. At individual scale, the probability of leaf appearance is inversely proportional to the distance from central stem. On the basis of this assumption, we formulated leaf area volume density (LAVD) spatial distribution at three hierarchical scales: individual scale, row scale, and scene scale. The equations for directional gap probability and bi-directional gap probability were modified to adapt the heterogeneity of row structure. Afterwards, a straightforward radiative transfer model was built based on the gap probabilities. A set of simulated data was generated by the thermal radiosity-graphics combined model (TRGM) as the benchmark to evaluate both forward simulation and inversion ability of the new model, we compared the new DBT model against an existing model assuming row as homogeneous box. With the growth of crops, the canopy structure of row crops will gradually change from row structure to continuous canopy. The new DBT model agreed with the TRGM model much better than the homogeneous row model at the middle stage of the crop growth season. The new model and the homogeneous row model achieve similar accuracy at early stage and end stage. At the middle growth stage, the new model can improve the accuracy of soil temperature retrieval. We recommend the new DBT model as an option to improve the DBT simulation and component temperature retrieval for row-planted crop canopy. In particular, the more accurate component temperatures during the middle growth stage are fundamentally important in characterizing crop water status, evapotranspiration, and soil moisture, which are subsequently critical for predicting crop productivity.

We report a unique multiyear L-band microwave radiometry dataset collected at the Maqu site on the eastern Tibetan Plateau and demonstrate its utilities in advancing our understandings of microwave observations of land surface processes. The presented dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. Measurement cases in winter, pre-monsoon, monsoon and post-monsoon periods are presented., Measurement(s) microwave radiation • soil moisture • atmospheric wind • Solar Radiation • rain • soil • CO2/H2O flux • thermal radiation • temperature of soil Technology Type(s) Radiometry (Microwave) • Sensor Device • pyranometer • Gauge or Meter Device • particle size analyzer • pyrgeometer Sample Characteristic - Environment land Sample Characteristic - Location Tibetan Plateau Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12818261

[EN] 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., The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).

Freeze-thaw cycles play a critical role in affecting ecosystem services in arid regions. Monitoring studies of soil temperature and moisture during a freeze-thaw process can generate data for research on the coupled movement of water, vapor, and heat during the freezing-thawing period which can, in turn, provide theoretical guidance for rational irrigation practices and ecological protection. In this study, the soil temperature and moisture changes in the deep vadose zone were observed by in-situ monitoring from November 2017 to March 2018 in the Mu Us Desert. The results showed that changes in soil temperatures and temperature gradients were largest in soil layers above the 100-cm depth, and variations decreased with soil depth. The relationship between soil temperature and unfrozen water content can be depicted well by both theoretical and empirical models. Due to gradients of the matric potential and temperature, soil water flowed from deeper soil layers towards the frozen soil, increasing the total water content at the freezing front. The vapor flux, which was affected mainly by temperature, showed diurnal variations in the shallow 20-cm soil layer, and its rate and variations decreased gradually with increasing soil depths. The freeze-thaw process can be divided into three stages: the initial freezing stage, the downward freezing stage, and the thawing stage. The upward vapor flux contributed to the formation of the frozen layer during the freezing process.

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 spatiotemporal 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.

Acquiring soil hydrothermal information using process-based models is important for agricultural management in dry alpine regions where in situ data collection is difficult. However, few modeling studies have considered the soil airflow transport mechanism for arid regions especially, the presence of dry airflow in soil can affect water and heat transport. Here, we adopt an airflow-coupling hydrological model, Simultaneous Transfer of Energy, Mass, and Momentum in Unsaturated Soil (STEMMUS), to simulate soil hydrothermal processes and evapotranspiration dynamics in a dry farmland on the Tibetan Plateau (TP). The effect of airflow on the simulations was carefully assessed. Our results suggested that STEMMUS can reliably capture daily observations—the average values for the index of agreement (d) in the 20–100 cm soil profile were 0.94 for soil temperature, 0.83 for soil moisture, 0.72 for soil evaporation and 0.83 for crop evapotranspiration, respectively, during the validation period. The impacts of considering airflow transport occurred when water inputs reached 22.8 mm, showing a positive relationship with increasing precipitation/irrigation. Incorporating airflow in the model showed minor differences but basically improved the modeling precision for soil moisture (reduction in root mean squared error (RMSE) values ranging from 0 to 95.9%), evapotranspiration (reduction in RMSE values ranging from 0 to 97.8%) and evaporation (reduction in RMSE values ranging from 0 to 99.3%) following rainfall/irrigation events. These findings provide sights into the role of airflow in the complex soil physical processes, and highlight that rainfall/irrigation inputs are a major factor affecting simulations when airflow is considered.

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics., Plain Language Summary: A reanalysis is a unique set of continuous variables produced by optimally merging a numerical model and observed data. The data are merged with the model using available uncertainty estimates to generate the best possible estimate of the target variables. The framework for generating a reanalysis consists of the model, the data, and the model‐data‐fusion algorithm. The very specific requirements of reanalysis frameworks have led to the development of Earth‐compartment specific reanalysis for the atmosphere, the ocean and land. Here, we review atmospheric and oceanic reanalyzes, and in more detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic, and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Based on a review of existing achievements, we identify five major steps required to develop reanalysis for terrestrial ecosystem to shed more light on biotic and abiotic interactions. In the future, terrestrial ecosystem reanalysis will deliver continuous data streams on the state and the development of terrestrial ecosystems., Key Points: Reanalyzes provide decades‐long model‐data‐driven harmonized and continuous data sets for new scientific discoveries. Novel global scale reanalyzes quantify the biogeochemical ocean cycle, terrestrial carbon cycle, land surface, and hydrologic processes. New observation technology and modeling capabilities allow in the near future production of advanced terrestrial ecosystem reanalysis., European Union's Horizon 2020 research and innovation programme, Deutsche Forschungsgemeinschaft, U.S. Department of Energy, Emory University's Halle Institute for Global Research and the Halle Foundation Collaborative Research, NSF, NASA, Natural Environment Research Council, European Union'’s Horizon 2020 research and innovation programme, NSERC Discovery program, the Ocean Frontier Institute, and MEOPAR, Research Foundation Flanders (FWO), Helmholtz Association, NASA Terrestrial Ecosystems

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

L-band passive microwave remote sensing is one of the most effective methods to map the global soil moisture distribution, yet, at which soil depth satellites are measuring is still inconclusive. Recently, with the Lv’s multilayer soil effective temperature scheme, such depth information can be revealed in the framework of the zeroth-order incoherent model when soil temperature varies linearly with soil optical depth. In this paper, we examine the relationships between soil temperature microwave sensing depth, penetration depth, and soil effective temperature, considering the nonlinear case. The soil temperature sensing depth often also named penetration depth is redefined as the depth where soil temperature equals the soil effective temperature. A method is developed to estimate soil temperature sensing depth from one pair of soil temperature and moisture measurement at an arbitrary depth, the soil surface temperature, and the deep soil temperature which is assumed to be constant in time. The method can be used to estimate the soil effective temperature and soil temperature sensing depth.

Knowledge of the response of extreme precipitation to urbanization is essential to ensure societal preparedness for the extreme events caused by climate change. To quantify this response, this study scales extreme precipitation according to temperature using the statistical quantile regression and binning methods for 231 rain gauges during the period of 1985–2014. The positive 3%–7% scaling rates were found at most stations. The nonstationary return levels of extreme precipitation are investigated using monthly blocks of the maximum daily precipitation, considering the dependency of precipitation on the dewpoint, atmospheric air temperatures, and the North Atlantic Oscillation (NAO) index. Consideration of Coordination of Information on the Environment (CORINE) land-cover types upwind of the stations in different directions classifies stations as urban and nonurban. The return levels for the maximum daily precipitation are greater over urban stations than those over nonurban stations especially after the spring months. This discrepancy was found by 5%–7% larger values in August for all of the classified station types. Analysis of the intensity–duration–frequency curves for urban and nonurban precipitation in August reveals that the assumption of stationarity leads to the underestimation of precipitation extremes due to the sensitivity of extreme precipitation to the nonstationary condition. The study concludes that nonstationary models should be used to estimate the return levels of extreme precipitation by considering the probable covariates such as the dewpoint and atmospheric air temperatures. In addition to the external forces, such as large-scale weather modes, circulation types, and temperature changes that drive extreme precipitation, urbanization could impact extreme precipitation in the Netherlands, particularly for short-duration events.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the active-only model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for $\sigma _{\mathrm{ HH}}^{o} $ , $\sigma _{\mathrm{ VV}}^{o} $ , $T_{\mathrm {BH}}$ , and $T_{\mathrm {BV}}$ , respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.

Oxygen isotope ratios ( δ 18 O) are the most commonly used parameters recorded in paleoclimate archives since they link different natural archives via the water cycle. Tree-ring δ 18 O ( δ 18 OTR) has been widely used for hydroclimate reconstructions in the Himalaya. However, few of them record temperature signals, which are dominant in Himalaya ice-core δ 18 O. We hypothesize that the “precipitation amount effect” due to the South Asian Summer Monsoon (SASM) may overprint temperature signals in δ 18 OTR series. The purpose of this study is to investigate whether temperature signals could be found in the δ 18 OTR in locations where the influence of SASM is weak. We developed a 105-yr δ 18 OTR chronology from the northern slope of the western Himalaya which greatly blocks the SASM. Our δ 18 OTR clearly shows stronger correlations with temperature (dominant winter and weak summer) than summer precipitation signals. It also agrees well with summer soil moisture δ 18 O simulated by the global isotope model LMDZ4 (r = 0.72, 1979–2010). In LMDZ4, low winter temperature was found to increase winter snowfall and subsequent snow melt, and thus to increase the contribution of winter snowfall to soil moisture in summer at the expense of summer precipitation. Since winter snowfall is more depleted than summer precipitation, this leads to lower summer soil moisture δ 18 O. The temperature signals found in our δ 18 OTR series are consistent with those found in the Dasuopu ice-core δ 18 O. This implies that δ 18 OTR series from the southwest Tibetan Plateau (TP), with a weak monsoon, hold great potential to capture temperature signals. Climate interpretations of δ 18 O proxies in the Himalaya largely depend on the influence of seasonal water from the dominant atmosphere circulation systems of the westerlies or monsoon. The δ 18 O proxies from the monsoon-affected region have a higher potential for the reconstruction of boreal summer hydroclimate, whereas δ 18 O proxies from westerly-affected sites have a higher potential for temperature reconstructions.

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while $in \,\,situ$ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions ( $r^{2} = 0.59$ for gravimetric method; $r^{2} = 0.65$ for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis.

Terrestrial ecosystems play a critical role in the global carbon cycle and the feedbacks of carbon cycle will significantly impact future climate change. It's worth noting that semi-arid biomes in the Southern Hemisphere have driven the global carbon sink anomaly over the past 30 years. However, how does the desert/grassland biome transition zone, a part of arid and semi-arid biomes, respond to climate change and anthropogenic activities in carbon use efficiency (CUE) is still unclear. Therefore, based on the CUE of terrestrial ecosystem estimated by the moderate resolution imaging spectroradiometer (MODIS) data from 2001 to 2017, the spatial and temporal characteristics of CUE in Ningxia province, a typical desert/grassland biome transition zone, were studied. The main driving factors in climate and ecosystem were also investigated by partial correlation analysis. Results showed that the CUE of terrestrial ecosystems in desert/grassland biome transition zone is higher than 0.5, a constant value of CUE defined in many ecological models. However, the CUE varies with the ecosystem types even when they are located in the same climatic zone. There is a decreasing trend of annual CUE in the period of 2001–2017 and most of them will persistently decrease in future at pixels scales, which could be mainly caused by the land use change. Comparing the habitat conditions, we found the lower canopy density and water stress could increase the CUE in the same ecosystem, which indicates the plant could increase their efficiency of transforming carbon from the atmosphere to terrestrial biomass in adverse environment. Finally, the CUE significantly correlated to net primary productivity (NPP) and autotrophic respiration (Ra) in ecosystem processes, meanwhile water stress (lower precipitation) and heat stress (higher temperature) could increase the CUE, but the temperature has variable impacts in different ecosystem.

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, soil thickness measurement, an altitude survey, hydrogeological surveys, 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 hydrogeological surveys reveal that groundwater flows from the west to the east, recharging the Yellow River. The hydraulic conductivity ranges from 0.2 to 12.4 m d−1. The MRS sounding results, i.e. water content and hydraulic conductivity, confirmed the presence of an unconfined aquifer in the flat eastern area. Based on TEM results, the depth of the Yellow River deposits was derived at several places in the flat eastern area, ranging from 50 to 208 m. The soil thickness measurements were done in the western mountainous area of the catchment, where hydrogeophysical and hydrogeological surveys were difficult to be carried out. The results indicate that most soil thicknesses, except on the valley floor, are within 1.2 m in the western mountainous area of the catchment, and the soil thickness decreases as the slope increases. These survey data and results can contribute to integrated hydrological modelling and water cycle analysis to improve a full-picture understanding of the water cycle at the Maqu catchment in the YRSR. The raw dataset is freely available at https://doi.org/10.17026/dans-z6t-zpn7 (Li et al., 2020a), and the dataset containing the processed ERT, MRS, and TEM data is also available at the National Tibetan Plateau Data Center with the link https://doi.org/10.11888/Hydro.tpdc.271221 (Li et al., 2020b).

When ground-leaving radiance from low-temperature surface is similar to downwelling radiance from the sky, the singular values arise in the retrieved land surface emissivity (LSE) at some specific spectral bands. In addition, too many singular values eventually cause temperature/emissivity separation algorithms to fail. To reduce the occurrence of these singular points, we formulate two indices, including the land-atmosphere radiance contrast index (LACI) and neighbor band contrast index (NBCI). LACI characterizes the contrast between surface radiance and sky downwelling radiance. NBCI characterizes the contrast between the radiance at neighboring bands. These two indices are used as filters to select bands which participate in the iterative spectrally smooth calculation. Thus, we modify the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm for low-temperature surfaces. Two methods have been used to evaluate the modified algorithm. First, numerical experiments are conducted to evaluate if the modified algorithm can accurately retrieve the ``true'' LSE from the simulated data. Second, an artificial low-temperature surface cooled by liquid nitrogen is measured to validate the modified algorithm. The results show that the modified algorithm can effectively avoid singular values, and behaves much better than the original algorithm with errors of less than 0.01 in retrieved emissivity when applied to low-temperature regions, while the modified algorithm brings limited improvement in retrieved temperature.

The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa‐WPR) data at a 10‐day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa‐WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long‐term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa‐WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa‐WPR is performing well, but with some noticeable overestimation. The ETIa‐WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa‐WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa‐WPR is overestimating at low ETIa‐WPR and underestimating when ETIa is high. In irrigated areas, ETIa‐WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa‐WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa‐WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management.

Frozen ground covers a vast area of the Earth's surface and it has important ecohydrological implications for cold regions under changing climate. However, it is challenging to characterize the simultaneous transfer of mass and energy in frozen soils. Within the modeling framework of Simultaneous Transfer of Mass, Momentum, and Energy in Unsaturated Soil (STEMMUS), the complexity of the soil heat and mass transfer model varies from the basic coupled model (termed BCM) to the advanced coupled heat and mass transfer model (ACM), and, furthermore, to the explicit consideration of airflow (ACM–AIR). The impact of different model complexities on understanding the mass, momentum, and energy transfer in frozen soil was investigated. The model performance in simulating water and heat transfer and surface latent heat flux was evaluated over a typical Tibetan plateau meadow site. Results indicate that the ACM considerably improved the simulation of soil moisture, temperature, and latent heat flux. The analysis of the heat budget reveals that the improvement of soil temperature simulations by ACM is attributed to its physical consideration of vapor flow and the thermal effect on water flow, with the former mainly functioning above the evaporative front and the latter dominating below the evaporative front. The contribution of airflow-induced water and heat transport (driven by the air pressure gradient) to the total mass and energy fluxes is negligible. Nevertheless, given the explicit consideration of airflow, vapor flow and its effects on heat transfer were enhanced during the freezing–thawing transition period.

Accurate observations and simulations of soil moisture phasal forms are crucial in cold region hydrological studies. In the seasonally frozen ground of eastern Tibetan Plateau, water vapor, liquid, and ice coexist in the frost‐susceptible silty‐loam soil during winter. Quantification of soil ice content is thus vital in the investigation and understanding of the region's freezing‐thawing processes. This study focuses on the retrieval of soil ice content utilizing the in situ soil moisture (i.e., liquid phase) and cosmic ray neutron measurements (i.e., total water including liquid and ice), with Observing System Simulation Experiments. To derive the total soil water from neutron counts, different weighting methods (revised, conventional, and uniform) for calibrating the cosmic‐ray neutron probe (CRNP) were intercompared. The comparison showed that the conventional nonlinear method performed the best. Furthermore, to assimilate fast neutrons using the particle filter, the STEMMUS‐FT (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) model was used as the physically based process model, and the COSMIC model (Cosmic‐ray Soil Moisture Interaction Code) used as the observation operator (i.e., forward neutron simulator). Other than background inputs from disturbed initializations in the STEMMUS‐FT, model uncertainties were predefined to assimilate fast neutrons. We observed that with enough spread of uncertainties, the updated states could mimic the CRNP observation. In all setups, assimilating CRNP measurements could enhance total soil water analyses, which consequently led to the improved detection of soil ice content and therefore the freezing thawing‐process at the field scale.

This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future.

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. The measured soil property dataset is available at http://doi.org/10.4121/uuid:61db65b1-b2aa-4ada-b41e-61ef70e57e4a.

The process of evaporation interacts with the soil, which has various comprehensive mechanisms. Multiphase flow models solve air, vapour, water, and heat transport equations to simulate non-isothermal soil moisture transport of both liquid water and vapor flow, but are only applied in non-vegetated soils. For (sparsely) vegetated soils often energy balance models are used, however these lack the detailed information on non-isothermal soil moisture transport. In this study we coupled a multiphase flow model with a two-layer energy balance model to study the impact of non-isothermal soil moisture transport on evaporation fluxes (i.e., interception, transpiration, and soil evaporation) for vegetated soils. The proposed model was implemented at an experimental agricultural site in Florida, US, covering an entire maize-growing season (67 days). As the crops grew, transpiration and interception became gradually dominated, while the fraction of soil evaporation dropped from 100% to less than 20%. The mechanisms of soil evaporation vary depending on the soil moisture content. After precipitation the soil moisture content increased, exfiltration of the liquid water flow could transport sufficient water to sustain evaporation from soil, and the soil vapor transport was not significant. However, after a sufficient dry-down period, the soil moisture content significantly reduced, and the soil vapour flow significantly contributed to the upward moisture transport in topmost soil. A sensitivity analysis found that the simulations of moisture content and temperature at the soil surface varied substantially when including the advective (i.e., advection and mechanical dispersion) vapour transport in simulation, including the mechanism of advective vapour transport decreased soil evaporation rate under wet condition, while vice versa under dry condition. The results showed that the formulation of advective soil vapor transport in a soil-vegetation-atmosphere transfer continuum can affect the simulated evaporation fluxes, especially under dry condition.

The Coordinating Earth Observation Data Validation for Reanalysis for Climate Services project (CORE-CLIMAX) aimed to substantiate how Copernicus observations and products can contribute to climate change analyses. CORE-CLIMAX assessed the European capability to provide climate data records (CDRs) of essential climate variables (ECVs), prepared a structured process to derive CDRs, developed a harmonized approach for validating essential climate variable CDRs, identified the integration of CDRs into the reanalysis chain, and formulated a process to compare the results of different reanalysis techniques. With respect to the Copernicus Climate Change Service (C3S), the systematic application and further development of the CORE-CLIMAX system maturity matrix (SMM) and the spinoff application performance metric (APM) were strongly endorsed to be involved in future implementations of C3S. We concluded that many of the current CDRs are not yet sufficiently mature to be used in reanalysis or applied in climate studies. Thus, the production of consistent high-resolution data records remains a challenge that needs more research urgently. Extending ECVs to close climate cycle budgets (e.g., essential water variables) is a next step linking CDRs to sectoral applications.

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 anthropogenic revegetation for combating desertification in China has been a long-term strategy that has induced shrub encroachment in desert steppe and regionally notable greening. Although the benefits of reversing desertification, preventing erosion, and providing biomass have been recognized, the effects of anthropogenic revegetation on water and carbon cycles, the critical process of terrestrial ecosystem, are still poorly understood. This study evaluates the effects of anthropogenic revegetation on water and carbon cycles in a typical desert steppe located in Yanchi County, Ningxia Province, Northwest China, by simulating two scenarios of grassland and shrub with the Biome-BGC model. Continuous CO2 and H2O flux measurements from 2016 to 2019 were used to validate the performance of Biome-BGC. Results showed that the anthropogenic revegetation significantly intensified the carbon cycle and increased carbon storages in the ecosystem and transformed the desert steppe ecosystem from a weak carbon source into a strong carbon sink. At the same time, the anthropogenic revegetation critically increased the water consumption of the ecosystem and decreased the soil water storage. The extreme consequence would be that the water resource will exceed the sustainable limit of ecosystems in the context of precipitation as the only water supply. Such undesired outcomes should be given more attention in state and local ecological restoration programs. © 2021 Elsevier B.V.

Soil freeze–thaw transition monitoring is essential for quantifying climate change and hydrologic dynamics over cold regions, for instance, the Third Pole. We investigate the L-band (1.4 GHz) microwave emission characteristics of soil freeze–thaw cycle via analysis of tower-based brightness temperature ( $T_{{{{\mathrm {B}}}}}^{p}$ ) measurements in combination with simulations performed by a model of soil microwave emission considering vertical variations of permittivity and temperature. Vegetation effects are modeled using Tor Vergata discrete emission model. The ELBARA-III radiometer is installed in a seasonally frozen Tibetan grassland site to measure diurnal cycles of L-band $T_{{{{\mathrm {B}}}}}^{p}$ every 30 min, and supporting micrometeorological as well as volumetric soil moisture ( $\theta $ ) and temperature profile measurements are also conducted. Soil freezing/thawing phases are clearly distinguished by using $T_{{{{\mathrm {B}}}}}^{p}$ measurements at two polarizations, and further analyses show that: 1) the four-phase dielectric mixing model is appropriate for estimating permittivity of frozen soil; 2) the soil effective temperature is well comparable with the temperature at 25 cm depth when soil liquid water is freezing, while it is closer to the one measured at 5 cm when soil ice is thawing; and 3) the impact on $T_{{{{\mathrm {B}}}}}^{p}$ caused by diurnal changes of ground permittivity is dominating the impact of changing ground temperature. Moreover, the simulations performed with the integrated Tor Vergata emission model and Noah land surface model indicate that the $T_{{{{\mathrm {B}}}}}^{p}$ signatures of diurnal soil freeze–thaw cycle is more sensitive to the liquid water content of the soil surface layer than the in situ measurements taken at 5 cm depth.

Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, including their value to users, to establish the relation of trust between providers of climate data and information and various downstream users. The climate data and information products (i.e., from satellite, in-situ and reanalysis) shall be fully traceable, adequately documented and uncertainty quantified and can provide sufficient guidance for users to address their specific needs and feedbacks. This paper discusses details on how to apply the quality assurance framework to deliver timely assessments of the quality and usability of Essential Climate Variable (ECV) products. It identifies an overarching structure for the quality assessment of single product ECVs (i.e., consists of only one single variable), multi-product ECVs (i.e., more than one single parameter), thematic products (i.e., water, energy and carbon cycles), as well as the usability assessment. To support a traceable climate service, other than rigorously evaluating the technical and scientific quality of ECV products, which represent the upstream of climate services, how the uncertainty propagates into the resulting benefit (utility) for the users of the climate service needs to be detailed.

The validation of global remote sensing data comprises multiple methods including comparison to field measurements, cross-comparisons and verification of physical consistency. Physical consistency and cross-comparisons are typically assessed for all pixels of the entire product extent, which requires intensive computing. This paper proposes a statistically representative sampling approach to reduce time and efforts associated with validations of remote sensing data having big data volume. A progressive sampling approach, as typically applied in machine learning to train algorithms, combined with two performance measures, was applied to estimate the required sample size. The confidence interval (CI) and maximum entropy probability distribution were used as indicators to represent accuracy. The approach was tested on 8 continental remote sensing-based data products over the Middle East and Africa. Without the consideration of climate classes, a sample size of 10,000–100,000, dependent on the product, met the nominally set CI and entropy indicators. This corresponds to

Traditionally, effective soil temperature (Teff) has been considered to be a secondary intermediate variable in microwave radiative transfer theory. However, its impact on microwave radiometry is comparable to that of vegetation cover, soil surface roughness and dielectric constant. Teff is defined as the weighted temperature of the emitting layers, where the weighting involved depends on the soil moisture profile. In this study, we evaluate the suitability of various models for estimating Teff using temperature and moisture profiles obtained from a land surface model. MERRA-Land (the Modern-Era Retrospective analysis for Research and Applications-Land) soil moisture profiles and temperature profiles were used to reproduce global Teff data sets with single parameter schemes (e.g. the Beta Soil Moisture Active Passive, SMAP scheme), two-parameter Choudhury's schemes (e.g. the current SMAP's scheme), two-parameter Wigneron schemes (e.g. the current Soil Moisture and Ocean Salinity scheme, SMOS), and multilayer Teff schemes (e.g. Lv′s scheme). The results show that differences in Teff between these schemes are usually

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.

This paper analyses the effect of the spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three performance indicators—adequacy (i.e., sufficiency of water use to meet the crop water requirement), equity (i.e., fairness of irrigation distribution), and productivity (i.e., unit of physical crop production/yield per unit water consumption)—are evaluated in five irrigation schemes for three spatial resolutions—250 m, 100 m, and 30 m. Each scheme has varying plot sizes and distributions, with average plot sizes ranging from 0.2 ha to 13 ha. The datasets are derived from the United Nations Food and Agricultural Organization (FAO) water productivity through open access of remotely sensed–derived data (the Water Productivity Open Access Portal—WaPOR) database. Irrigation indicators performed differently in different aspects; for adequacy, all three resolutions show similar spatial trends for relative evapotranspiration (ET) across levels for all years. However, the estimation of relative ET is often higher at higher resolution. In terms of equity, all resolutions show similar inter-annual trends in the coefficient of variation (CV); higher resolutions usually have a higher CV of the annual evapotranspiration and interception (ETIa) while capturing more spatial variability. For productivity, higher resolutions show lower crop water productivity (CWP) due to higher aboveground biomass productivity (AGBP) estimations in lower resolutions; they always have a higher CV of CWP. We find all resolutions of 250 m, 100 m, and 30 m suitable for inter-annual and inter-scheme assessments regardless of plot size. While each resolution shows consistent temporal trends, the magnitude of the trend in both space and time is smoothed by the 100 m and 250 m resolution datasets. This frequently results in substantial differences in the irrigation performance assessment criteria for inter-plot comparisons; therefore, 250 m and 100 m are not recommended for inter-plot comparison for all plot sizes, particularly small plots (

The scarcity of water and the growing global food demand has fevered the debate on how to increase agricultural production without further depleting water resources. Crop water productivity (CWP) is a performance indicator to monitor and evaluate water use efficiency in agriculture. Often in remote sensing datasets of CWP and its components, i.e. crop yield or above ground biomass production (AGBP) and evapotranspiration (ETa), the end-users and developers are different actors. The accuracy of the datasets should therefore be clear to both users and developers. We assess the accuracy of remotely sensed CWP against the accuracy of estimated in-situ CWP. First, the accuracy of CWP based on in-situ methods, which are assumed to be the user's benchmark for CWP accuracy, is reviewed. Then, the accuracy of current remote sensing products is described to determine if the accuracy benchmark, as set by in-situ methods, can be met with current algorithms. The percentage error of CWP from in-situ methods ranges from 7% to 67%, depending on method and scale. The error of CWP from remote sensing ranges from 7% to 22%, based on the highest reported performing remote sensing products. However, when considering the entire breadth of reported crop yield and ETa accuracy, the achievable errors propagate to CWP ranges of 74% to 108%. Although the remote sensing CWP appears comparable to the accuracy of in-situ methods in many cases, users should determine whether it is suitable for their specific application of CWP. peerReviewed

Accurate representing freeze‐thaw (FT) process is of great importance in cold region hydrology and climate studies. With the STEMMUS‐FT model (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil), we investigated the coupled water and heat transfer in the variably‐saturated frozen soil and the mechanisms of water phase change along with both evaporation and freeze‐thaw process, at a typical meadow ecosystem on the Tibetan Plateau. The STEMMUS‐FT showed its capability of depicting the simultaneous movement of soil moisture and heat flow in frozen soil. The comparison of different parameterizations of soil thermal conductivity indicated that the de Vries parameterization performed better than others in reproducing the hydrothermal dynamics of frozen soils. The analysis of water/vapor fluxes indicated that both the liquid water and vapor fluxes move upward to the freezing front and highlighted the crucial role of vapor flow during soil freeze‐thaw cycles as it connects the water/vapor transfer beneath the freezing front and above the evaporation front. The liquid/vapor advective fluxes make a negligible contribution to the total mass transfer. Nevertheless, the interactive effect of soil ice and air can be found on the spatial and temporal variations of advective fluxes in frozen soils