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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.

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.

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.

Basic soil physical properties (i.e., soil texture and organic matter) and associated soil hydraulic properties (i.e., soil water retention curve and hydraulic conductivity) play an essential role in land surface models (LSMs) for estimating soil moisture. With the physical link between soil properties, LSMs and Radiative Transfer Models (RTMs), the soil physical properties can be retrieved, using a LSM coupled with a microwave L-band emission observation model in a data assimilation framework. To this purpose, this paper couples an enhanced physically-based discrete scattering-emission model with the Community Land Model 4.5 (CLM), to retreive soil physical properties using the Local Ensemble Transform Kalman Filter (LETKF) algorithm, assimilating Soil Moisture Active and Passive (SMAP) Level-1C (L1C) brightness temperature at H and V polarization ( and ) separately, assisted with in situ measurements at the Maqu site on the eastern Tibetan Plateau. Results show the improved estimate of soil properties at the topmost layer via assimilating SMAP ( H, V), as well as at profile using the retrieved top-layer soil properties and a prior depth ratio. The use of and shows varied sensitivities to retrievals of different soil compositions (i.e., sand, clay, silt) and soil moisture estimates. However, analyses show that the retrieved soil properties with fine accuracy are not sensitive factors affecting soil moisture estimates. Instead, uncertainties of CLM model structures shall be considered, such as the fixed PTFs (pedotransfer functions), the hydraulic function describing soil water retention curve and the water stress function determining root water update.

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.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS

Emission and backscattering at different frequencies have varied responses to soil physical processes (e.g., moisture redistribution, freeze-thaw) and vegetation growing/senescencing. Combing the use of active and passive microwave multi-frequency signals may provide complementary information, which can be used to better retrieve soil moisture, and vegetation biomass and water content for ecological applications. To this purpose, a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) was adopted in this study to simulate both emission (TB) and backscatter (σ0), in which the CLAP is backboned by the TorVergata model for modelling vegetation scattering, and an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering. The accuracy of CLAP was assessed by both ground-based and spaceborne measurements, and the former was from the deployed microwave radiometer/scatterometer observatory at Maqu site on an alpine meadow over the Tibetan plateau. Specifically, for the passive case, simulated TB (emissivity multiplied by effective temperature) were compared to the ground-based ELBARA-III L-band observations, as well as C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) and L-band Soil Moisture Active Passive (SMAP) observations. For the active case, simulated σ0 were compared to the ground-based scatterometer C- and L-bands observations, and C-band Sentinel and L-band Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) observations. This study is expected to contribute to improving the soil moisture retrieval accuracy for dedicated microwave sensor configurations.

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 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.

Root water uptake is an important component of the terrestrial water balance and a critical factor that influences energy, water vapor, and carbon exchange among soil, vegetation and atmosphere interfaces. However, most of the current vegetation photosynthesis models do not account for root water uptake, which compromises their applications under water stressed conditions. To address this limitation, this study integrates photosynthesis and transfer of energy, mass and momentum in the soil-plant-atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme (from soil, through root zones and plants, to atmosphere). The coupled model was evaluated with field measurement of a maize canopy. The results indicated that the simulation of land surface fluxes was significantly improved due to considering the root water uptake, especially when vegetation is experiencing sever water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root distribution, on simulating ecosystem functioning.

Quantification of the spatial and temporal behavior of soil moisture is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Unmanned Aerial Systems (UAS) offer a great potential in monitoring this parameter at sub-meter level and at relatively low cost. The standardization of operational procedures for soil moisture monitoring with UAS can be beneficial to understanding and quantify the quality of retrieved soil moisture (e.g., from different platforms and sensors).In this study, soil moisture retrieved from UAS using different retrieval algorithms was compared to collocated ground measurements. The thermal inertia model builds upon the dependence of the thermal diffusion on soil moisture. The soil thermal inertia is quantified by processing visible and near-infrared (VIS-NIR) and thermal infrared (TIR) images, acquired at two different times of a day. The temperature–vegetation trapezoidal model is also used to map soil moisture over vegetated pixels. This trapezoidal model depicts the soil moisture dependence of the surface energy balance. The comparison of the two algorithms helps define a preliminary standard procedure for retrieving soil moisture with UAS.As a case study, a typical cropland area with olive orchard, cherry and walnut trees in the region of Monteforte Cilento (Italy, Salerno) is used, where optical and thermal images and in situ data were simultaneously acquired. In the Alento observatory, long-term studies on vadose zone hydrology have been conducting across a range of spatial scales. Our findings provide an important contribution towards improving our knowledge on evaluating the ability of UAS to map soil moisture, in support of sustainable natural resources management and climate change studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, Unmanned Aerial Systems, thermal inertia, HARMONIOUS

Soil is an essential component in the environment and is vital for food security. It provides ecosystem services, filters water, supplies nutrients to plants, provides us with food, stores carbon, regulates greenhouse gases emissions and it affects our climate. Traditional soil survey methodologies are complicated, expensive, and time-consuming. Visible and infrared spectroscopy can effectively characterize soil properties. Spectral measurements are rapid, precise and inexpensive. The spectra contain information about soil properties, which comprises minerals, organic compounds, and water. Today, several Soil Spectral Libraries (SSLs) are being created worldwide because these datasets have a notable potential to be used as training datasets for machine learning methods that will benefit precision agriculture activity for better management of food production. Nonetheless, as SSL's are created under laboratory conditions it is not clear if it can be used to infer field conditions in situ and/or from the sky. Thus, study the relationship between RS, field spectroscopy and the laboratory measurements of soil is very important. Accordingly, this study postulates that traditional SSLs don't simulate the real spectral signatures in the field that both, satellite and airborne sensors measure as well, because they are affected by factors that are not an integral part of the soil, such as: moisture, litter, human and animal activity, plow, grass, dung, waste, etc… However, under laboratory conditions, these factors are usually removed for the preparation of SSLs. Thus, given the several SSLs available, it is necessary to evaluate the protocols that were used in these SSLs. The objective of this study is to evaluate the gap between field and laboratory spectral measurements through the analysis of the performance of spectral based models. This procedure combined two soil spectral libraries that contain 114 samples that were measured in the laboratory as well as in the field. The nature of the dataset is varied, because these samples were collected from six different fields in three countries of the Mediterranean basin: Israel, Greece and Italy. Moreover, 63 samples are mainly sandy and 51 are mainly clayey. In order to obtain optimal spectral measurements in the field, we used a new optical apparatus that simulates the sun's radiation. Next, we generated PLSR models to estimate one of the most important hydrological parameters namely “infiltration rate” that control the runoff stage, soil erosion and water storage in the soil profile. This property is strongly affected by the surface characteristics. Finally, the field based spectral model was adapted to an UAV hyperspectral sensor in order to estimate the infiltration rate from the sky. The results were successfully validated in field, and we concluded that for the estimation of the infiltration rate, SSLs must be created using surface reflectance in field because laboratory protocols can be detrimental for the performance of the dataset in question.

In the current Earth System Model (ESM), the soil water and heat transport in the land surface model (LSM) is not strongly coupled. As such, the discrepancy between the modelled land surface states and fluxes and the observed ones was mainly remedied by revising relevant simplified parameters, while the detailed physics (and/or physiography) was not necessarily consistent. While zooming in those studies over the cold region, the current ESMs do not consider the hydro-permafrost-carbon coupling. For example, the strong impact of soil moisture on spatial patterns of soil carbon stocks has been observed at sites, while the current ESMs cannot show this impact. On the other hand, soil moisture can affect the temperature sensitivity of decomposition rate and alter soil thermal dynamics significantly. To address the foregoing issues, it calls for an interdisciplinary approach to investigate soil-water-energy-plant interactions. Such approach is even more so desired for cold regions, where permafrost and seasonal frozen ground widely spread. This pressing need is mainly due to the carbon release from climate-induced permafrost thawing into the atmosphere, called as permafrost carbon feedback (PCF). It is also due to the tight coupling between hydrological processes and carbon dynamics, which, if ignored, will lead to the underestimation of global carbon turnover time by 36%. As a trial, this research coupled the detailed soil water and heat model with the biogeochemical model to investigate the mechanisms behind the impacts of enhanced soil water and heat dynamics on ecosystem functioning.

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.

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.

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.

A recent study showed a rise in total and extreme precipitation in the Netherlands over the past century. The present study attempts to characterize and attribute the seasonal variation of daily extreme precipitation events in the Netherlands. Statistical models for extreme values were used to fit daily rainfall maxima for all months during the period 1961–2014, using data from the 231 rain gauges distributed across the country. A generalized extreme value (GEV) approach was used to determine the probability distribution of extreme values and their dependency on time and the monthly North Atlantic Oscillation (NAO) index. The non-stationary models used to represent the annual cycle of the GEV parameters assumed an invariant shape parameter and harmonic functions as location and scale parameters. The best non-stationary model was selected using Akaike’s information criterion (AIC) and the log-likelihood ratio test (LRT). The results indicated that the estimates derived from the non-stationary model differed from those obtained with the aid of the stationary model, and had lower uncertainties. These non-stationary estimates were within the confidence intervals (CI) of the stationary estimates at most rain gauge stations. The non-stationary model estimated parameters with less uncertainty and with smaller CI, thus permitting more accurate representation of extreme precipitation in the Netherlands. The spatial pattern of annual mean location and scale GEV parameters was compatible with coastal, land cover (such as the wooded and heathland areas of the Veluwe region of the province of Gelderland) and orography (in the southeast of the country). The location parameter peaked over the west coast, especially on the central west coast during the summer half-year, while the centre and east of the country had the highest values during the winter half-year. The scale parameter peaked in the centre of the country during the summer, in the east in the early summer and along the west coast in the spring. The 10-year and 50-year return levels were calculated with the aid of the non-stationary model for all months. The spatial distribution of these extreme event probability clearly reflects the regional differences in the Netherlands.

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

The detection of changes in the weather of urban areas is an important issue for understanding the impact of weather on human lives. Five years of basic weather observations (2011−2015) from automatic and amateur networks across The Netherlands were used to investigate the urban effects on meteorological parameters with a focus on temperature (e.g. urban heat island [UHI]) and precipitation. Representative stations were selected based on metadata and a set of criteria for data quality control. An hourly analysis indicates that UHIs are a nocturnal phenomenon in The Netherlands and are more prominent after sunset, when they exceed 2°C. A seasonal analysis shows that UHIs occur in all seasons during the year, with the most significant UHI occurring in the summer. Furthermore, the differences in the precipitation of urban and rural areas were shown to be greatest after sunrise during the day. The maximum hourly UHI and hourly precipitation distributions were analyzed using a generalized extreme value model. The occurrences of maximum precipitation are likely to be more frequent at urban stations than at the nearby rural stations. Additionally, the distinct seasonal cycle of precipitation that is dependent on the UHI demonstrates that the maximum UHI and precipitation occurred in the summer. Our results indicate that the UHI and precipitation enhancements in Dutch urban residential areas, as obtained from the data of weather amateurs, are in agreement with the results presented in the literature, and that 7% more precipitation occurs in cities than in rural areas.

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.

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 m 3 /m 3 , and the radar-based product meets its target accuracy of 0.06 m 3 /m 3 (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive Cal/Val phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the Cal/Val Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.

The effective soil temperature (Teff) is essential for the retrieval of soil moisture information, when satellite microwave remote sensing data are used. In this investigation, a new two-layer scheme (Lv's scheme) is developed to estimate Teff considering wavelength, soil moisture, sampling depth, and soil temperature. The accuracy of the estimated Teff is verified with data collected in a field experiment at the Maqu Climate and Environment Observatory in the source region of the Yellow River. In addition to clearly defining the physical meaning of Lv's scheme, this study explains the physical meaning of Choudhury's C parameter, which is empirically determined by a least-square method. It was found that Lv's scheme does not require the fitting parameters. Further, Lv's scheme can be used to estimate Teff based on soil moisture data at surface level or any other specified soil depths, thus creating the opportunity to use observation and modeled data from different depths.

[1] We thank Mohanty and Yang [2013] (hereafter referred to as MY) for their comment on our paper ‘‘A simulation analysis of the advective effect on evaporation using a two-phase heat and mass flow model’’ [Zeng et al., 2011b]. We appreciate the effort by MY to look critically at our paper. We hope that the responses to their comments will help to clarify the issues they raised and to communicate better our approach and contributions to the reader. By reviewing MY’s comments, it is clear to us that we need to provide further clarification on the details of the key aspects of our paper. As shall be seen in following sections, we disagree with all of their objections. Before we issue the point-to-point rebuttal to the two key points MY raised, we want to clarify the approaches we presented in our paper. In such way, we hope to assist the reader to have a systematic view on the key aspects and make definite conclusions on its merits and validity. [2] The main objective of our paper was to identify the controlling mechanism for the advective effect on soil evaporation. When excluding soil airflow, the advective flux can lead to the underestimation of surface evaporative flux [Zeng et al., 2011a, 2011b]. To identify such mechanism, a systematic approach, including both experimental and numerical ones, should be applied. We implemented an in situ experiment and developed a two-phase heat and mass flow model to serve this purpose. With the calibrated two-phase model, the detailed driving force (e.g., matric potential gradient, soil air pressure gradients, and soil temperature gradients) and conductivity fields can be used to investigate and identify what exactly drives the underestimation error. The comparison in the modeled surface fluxes (e.g., evaporative fluxes) with and without soil airflow can identify the underestimation error. The modeled surface flux is a total flux, which is the summation of thermal and isothermal fluxes, and advective fluxes (e.g., when soil airflow being considered). Therefore, it requires a systematic view to decompose the total flux into different components, based on different driving forces. This is what we discussed at the beginning of section 3.3 in our original paper [Zeng et al., 2011b], from where we have analyzed the direct and indirect controlling mechanisms. [3] Based on the diurnal variation pattern of matric potential gradients above the depth of 50 cm, upward during the day and downward during the night, the original paper targets the isothermal flux as the direct driving factor for the underestimation error (e.g., the upward isothermal flux is higher when considering airflow than that without airflow). The inverse variation pattern of soil temperature and soil air pressure gradients, compared to the matric potential one, precludes their direct influence on the underestimation error. However, soil temperature gradient may have an indirect effect on the error (e.g., the downward thermal flux is lower when considering airflow than that without airflow). To verify the above hypothesis, the original paper compares the gradient and conductivity fields by using a normalized scale index (NSI). It is found that the indirect effect of thermal fluxes on the error is invalid because the downward thermal flux with airflow mechanism is larger than that without airflow. This is in contrast to the hypothesis. On the other hand, the original paper verifies the isothermal flux as the direct driving factor accounting for the underestimation error. The upward isothermal flux with airflow mechanism is indeed larger than that without airflow. [4] From Figure 4 of Zeng et al. [2011b], it is obvious that the advective effect is the most significant on day 2, which is chosen for analyzing the controlling mechanism behind the effect. The most significant effect, on day 2, implies the dominance of isothermal fluxes, which can be inferred by comparing the magnitude of different component fluxes [Zeng et al., 2009b]. From day 2 onward, the thermal fluxes start to dominate. As the soil temperature gradient is downward during daytime, it can be inferred that the dominance of thermal fluxes will minify the advective effect. This is because both fluxes (downward thermal and upward isothermal fluxes) will cancel each other out. This can also be inferred from the diurnal patterns of matric potential and soil temperature gradient in Figure 5 of Zeng et al. [2011b]. [5] The further analysis identifies the isothermal hydraulic conductivity as the key factor accounting for the advective effect since it is increased largely when considering airflow. One of the possible mechanisms for such increase Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, Netherlands.

[1] An analysis is carried out for two hydrologically contrasting but thermodynamically similar areas on the Tibetan Plateau, to evaluate soil moisture analysis based on the European Centre for Medium-Range Weather Forecasts (ECMWF) previous optimum interpolation scheme and the current point-wise extended Kalman filter scheme. To implement the analysis, this study used two regional soil moisture and soil temperature networks (i.e., Naqu and Maqu) on the Tibetan Plateau. For the cold-semiarid Naqu area, both ECMWF soil moisture analyses significantly overestimate the regional soil moisture in the monsoon seasons. For the cold-humid Maqu network area, the ECMWF products have comparable accuracy as reported by previous studies in the humid monsoon period. The comparisons were made among the liquid soil moisture analysis from ECMWF, the ground station's measurements and the satellite estimates from the Advanced Scatterometer sensor. The results show reasonable performances of the ECMWF soil moisture analyses (i.e., both optimum interpolation and extended Kalman filter products) and the Advanced Scatterometer level 2 products, when compared to the in situ measurements.

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.

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 .

GCOM-W AMSR2 provides continuity following AMSR-E and the opportunity to generate a global long-term satellite soil moisture data record from the same instrument type. Various soil moisture products are being developed using AMSR observations. The JAXA soil moisture along with the Single Channel Algorithm (SCA) product were evaluated using in situ observations from different geographical domains. Both the JAXA and SCA soil moisture estimates capture the overall climatological features and the overall spatial structure of the two products is similar. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture. The SCA performs well over low and moderately vegetated areas. This study focuses on the development of the AMSR2 soil moisture product. Validation results using in situ observations from diverse climate and land cover conditions will be presented.

Located in western Inner Mongolia, the Badain Jaran Desert is the second largest desert in China and consists of a regular series of stable megadunes, among which over 70 permanent lakes exist. The unexpected lakes in desert attracted research interests on exploring the hydrological process under this particular landscape; however, a very few literatures exist on the diurnal and spatial variation of the drying front in this area, which is the main issue in the desert hydrological process to characterize the movement of water in soil. In order to understand the drying front in the Badain Jaran Desert, a field campaign was conducted by the observations of soil physical parameters and micrometeorological parameters. With the field data, the performance of a vadose zone soil water balance model, the HYDRUS, was verified and calibrated. Then, the HYDRUS was used to produce the spatial and temporal information of coupled water, water vapour and heat transport in sand to characterize the variation pattern of the drying front before, during and after the rainfall. Finally, the deepest drying front was applied to determine the effective infiltration, which is defined as the amount of soil water captured by the sand beneath the deepest drying front by infiltrating water of an incident rainfall event.

Different evapotranspiration (ET) schemes can affect significantly the performance of land surface models in capturing the soil water dynamics and ET partitioning over various land cover and climates, the accurate understanding of which is crucial to determine the effective irrigation. In this study, a land model considering the coupled transfer of water, vapor and heat in the soil, with two alternative ET schemes, was used to investigate how the coupled mechanism can affect the soil water dynamics in a crop field and how the ET partitioning was influenced. There are two different evapotranspiration (ET) schemes, one is based on reference crop evapotranspiration (ET0) and use LAI to partition into soil evaporation and transpiration, denoted as the ETind scheme; the other is one-step calculation of actual soil evaporation and potential transpiration by incorporating canopy minimum resistance and actual soil resistance into Penman–Monteith model, denoted as the ETdir scheme. Results indicated that the coupled model with the two different ET schemes differed in simulating soil water content and crop evapotranspiration components while agreed well for the simulation of soil temperature. Considering the aerodynamic and surface resistance terms made the ETdir scheme better in simulating soil evaporation especially after irrigations. Furthermore, the results of different crop growth scenarios indicated that the uncertainty in LAI played an important role in estimating the relative transpiration and evaporation fraction. The soil drying seemed to intensify the disturbance of maximum rooting depth and root growth rate in calculating ET components. The former was more important at the late growing season while the latter dominated at the early growing season.

Snow cover simulation is a complex task in mountain regions because of its highly irregular distribution. GIS-based calculations of snowmelt–accumulation models must deal with nonnegligible scale effects below cell size, which may result in unsatisfactory predictions depending on the study scale. Terrestrial photography, whose scales can be adapted to the study problem, is a cost-effective technique, capable of reproducing snow dynamics at subgrid scale. A series of high-frequency images were combined with a mass and energy model to reproduce snow evolution at cell scale (30 m × 30 m) by means of the assimilation of the snow cover fraction observation dataset obtained from terrestrial photography in the Sierra Nevada, southern Spain. The ensemble transform Kalman filter technique is employed. The results show the convenience of adopting a selective depletion curve parameterization depending on the succession of accumulation–melting cycles in the snow season in these highly variable environments. A reduction in the error for snow depth to 50% (from 463.87 to 261.21 mm and from 238.22 to 128.50 mm) is achieved if the appropriate curve is selected.