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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).

Patos Lagoon is the largest choked coastal lagoon in the world (250 km long, 40 km wide and mean depth of 5 m), with banks formed by sand and predominantly muddy bottom. The circulation is mainly driven by wind and river flow, with a minor contribution of the local microtidal regime. This lagoon is one of the most studied coastal water bodies in Brazil. The Patos Lagoon suspended particulate matter (SPM) data compendium consists of over four decades (1978–2019) of SPM data gathered from multiple research and monitoring projects. Data consist of SPM in situ samplings, remote sensing derived SPM estimates, rating curve approach for SPM estimates and SPM derived from turbidity measurements. Only measurements with a detailed description of the sampling and analysis protocols were used. The present data set offers valuable contributions as it allows the assessment of trends on the data and the short- and long-term spatial and temporal variability of SPM concentrations in an area where permanent monitoring stations are not available. From these records, the SPM concentrations ranged from 0.4 g/m3 up to 1,000 g/m3. The Patos Lagoon SPM Compendium has been made publicly available at Tavora, Fernandes and Möller (2020).

The revegetation practice is one of the most efficient ways to alleviate soil erosion and desertification. However, the land cover change can considerably disturb ecohydrological processes, particularly in arid and semiarid regions where ecosystems are fragile and suffer intense water stress. This study evaluated the effects of revegetation on the energy, water and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, Northwest China, by simulating two scenarios of shrubs-grassland and grassland ecosystem with the STEMMUS-SCOPE model. The STEMMUS-SCOPE model integrates canopy photosynthesis, fluorescence, energy balance model and soil water and heat transfer model in the soil-plant-atmosphere continuum system. The model was validated by field observations from May to September of 2016-2019, and showed good performances in simulating the energy, water and carbon fluxes. It indicated that the revegetation facilitated carbon fixation (+69.34%). Latent heat flux was the primary consumer of the available energy and was stronger in the shrubs-grassland ecosystem (+16.76%). With the remarkably increased transpiration of the shrubs-grassland ecosystem (+86.72%), revegetation intensified the soil water losses, especially the soil water content within the 0-200 cm depth (−18.97%). Moreover, the water consumption of the shrubs-grassland ecosystem tended to exceed the received precipitation over the growing seasons. These results emphasized the necessity of considering the adverse impacts of revegetation in future ecological restoration, especially the irreversible soil water depletion and imbalance of energy, water and carbon cycles.

Climate projections strongly suggest that the 2022 sweltering summer may be a harbinger of the future European climate. Climate extremes (e.g., droughts and heatwaves) jeopardize terrestrial ecosystem carbon sequestration. The construction of an open digital twin of the soil-plant system helps to monitor and predict the impact of extreme events on ecosystem functioning and could be used to recommend measures and policies to increase the resilience of ecosystems to climate-related challenges. A digital twin refers to a highly interconnected workflow, with a data assimilation framework at its core to combine observations and process-based models, meanwhile accompanied by an interactive and configurable platform that allows users to create and evaluate user-specific scenarios for scientific investigation and decision support. Creating an open digital twin means creating a digital twin following Open Science and FAIR principles, both for data and research software. In this contribution, the STEMMUS-SCOPE model was used as an example to develop an open digital twin of the soil-plant system. We suggest our recently developed open digital twin infrastructure could serve as the backbone for an interoperable framework to facilitate the digitalization of other Earth subsystems (e.g., by simply replacing the soil-plant model). In addition, we show how software not designed initially as open can be adopted to create an open digital twin using containers - standardized computational environments that can be shared, reused and that foster reproducibility.

Accurate estimation of carbon assimilation and allocation plays a significant role in the plant growth and terrestrial ecosystems. The STEMMUS-SCOPE model integrates photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, and has good performances in estimating water, energy, and carbon fluxes. However, the plant growth states (i.e., leaf area index (LAI) and plant height (PH)) are needed as inputs for running the STEMMUS-SCOPE model, and are obtained either from interpolating observations or taking as constants over the time. As a result, the physical interactions are not adequately captured between radiative transfer, plant growth and soil water movements. The objective of this study is to consider the plant growth in STEMMUS-SCOPE model via coupling a crop growth module (i.e., WOFOST module). The coupled STEMMUS-SCOPE-WOFOST model was evaluated with plant functioning measurements. The results indicate that the simulation of LAI and PH is significantly improved and consistent with the dynamic of the water stress and gross primary production (GPP). Besides, the additional generated state variables (i.e., the biomass of root, leaf, stem as well as yield) can also agree well with the observations. Finally, the interactions between the land surface fluxes, soil moisture dynamic and plant growth are all well simulated. The STEMMUS-SCOPE-WOFOST model provides a mechanistic window to link the satellite observation of solar-induced fluorescence to above- and below-ground biomass, land surface fluxes, and root zone soil moisture, in a physically consistent manner.

A solid understanding of the global water cycle and how land surface processes respond to both changes in climate and pressure due to water use is essential for society. Although Land Surface Models (LSM) and Global Hydrological Models (GHM) are able to simulate the spatiotemporal variability of the water balance relatively reliably, intercomparison studies have indicated considerable differences between the models. Each LSM and GHM present a unique set of equations, parameters and configurations that contribute to the spread of simulated hydrological responses to meteorological forcings. In order to improve our understanding of modeling uncertainties, we propose a variable importance assessment for 5 LSM/GHM (JULES, HTESSEL, PCR-GLOBWB, SURFEX and ORCHIDEE) from the EartH2Observe (E2O) project. The output of the models and the meteorological forcings were collected from the Water Resources Reanalysis Tier 2 of the E2O project, which consists of a global dataset with spatial resolution of 0.25ox0.25o. We used soil texture and land cover datasets that most resemble the inputs used by each LSM/GHM during the E2O project. The models’ outputs were used to estimate 6 hydrological indices for every land cell: Evaporation-Precipitation ratio; Runoff-Precipitation ratio; Surface Runoff-Total Runoff ratio; median Soil Moisture variation caused by a Rainfall event; median Surface Runoff caused by a Rainfall event; and Soil Moisture temporal autocorrelation. Then, we evaluate the input features (meteorological, land cover, and soil texture) importance to the hydrological indices of each model using machine learning. With the analysis we aim to examine a) How much the models differ and why? b) To what extent are the output differences related to the input features or/and to the models formulation? and c) How significant is each feature to the respective hydrological index?

Soil water availability is a critical requirement for vegetation functioning in a water-limited regime. Vegetation takes up water from varying soil depths depending on their rooting location and soil moisture availability. The uptake depth varies spatially across climate regimes and vegetation types and temporally between seasons. Yet, a scientific consensus on the global relevance of near-surface and sub-surface soil moisture for vegetation functioning is still lacking and is the focus of this study. In particular, we calculate the correlation between the Near-Infrared Reflectance of Vegetation (NIRv) with both satellite-derived near-surface soil moisture from ESA-CCI and terrestrial water storage from GRACE. This is done globally and with monthly data during the growing season at each grid cell and accounting for the confounding effects of temperature and radiation. We analyze how these correlations vary spatially across varying vegetation types and climatic regimes, and temporally between all growing season months and particularly dry months. Finally, we repeat the analyses using Sun-induced fluorescence (SIF) data instead of NIRv. We find that NIRv and SIF correlate more strongly with near-surface soil moisture compared to terrestrial water storage in semi-arid regions with low tree cover. This suggests that the vegetation preferentially takes up water from near-surface soil moisture whenever available to meet its transpiration demand. In contrast, in regions with more tree cover and in drier regions, the correlation with terrestrial water storage is comparable to or even higher than with near-surface soil moisture. This indicates that trees can make use of their deep rooting systems to access deeper soil moisture resources, similar to vegetation in arid regions. In particularly dry months, correlations with near-surface soil moisture increase while this is even more the case with terrestrial water storage, highlighting the relevance of deeper water resources during rain-scarce periods.Overall, while direct observations of sub-surface soil moisture are scarce, this study employs different satellite-based data streams in order to estimate the relevance of near-surface versus sub-surface soil moisture for vegetation functioning. This can inform the representation of vegetation-water interactions in land surface models to support more accurate climate change projections.

1. Salt marshes fronting coastal structures, such as seawalls and dikes, may offer important ecosystem-based coastal defence by reducing the wave loading and run-up levels during storms. We question (i) how the long-term salt marsh development in the Dutch Wadden Sea relates to the tidal-flat foreshore bathymetry and (ii) how the wave run-up onto dikes, which enhances the risk of dike failure, depends on foreshore bathymetry, the presence/absence of marshes, marsh vegetation properties, tidal range and wind exposure. 2. We analysed 15 years of vegetation and bathymetry maps along the entire Dutch Wadden Sea coast, in combination with detailed process-based measurements at five locations during 3 years, to understand where salt marshes naturally form and what features determine their contribution to coastal protection. 3. The horizontal extent of marshes along the dikes remained relatively stable over the past decade. The presence of marshes was associated with higher elevations of adjacent tidal flats (above ~0.5 m NAP), while landward-directed marsh retreat was associated with surface erosion of the fronting tidal flats. 4. Wave run-up during storms was lower at sites with wider marshes and higher foreshore elevations. This was attributed to the marsh attenuation effect, which led to a reduction in wave heights at the dike toe. As the tidal range varies across the Dutch Wadden Sea, areas to the East with generally higher water levels experienced higher wave run-up. 5. Synthesis and applications. We found that (i) marshes, where present, effectively protected the dikes from wave loading and (ii) the sites where marshes typically do not develop spontaneously were the most vulnerable to high wave run-up. This catch-22 problem implies that increasing reliance on nature-based coastal defences along soft-bottom coasts may require human interventions to stimulate marsh formation at the locations where it is most needed. Alternatively, ‘hard engineering’ solutions may remain necessary where implementing nature-based solutions are either too costly, unachievable, or at the expense of other ecological values, such as causing the loss of mudflats that are important for migratory birds.

Many geological processes from oil recovery to underground CO2storage are affected by natural molecules adsorbed on rock surfaces. Yet, geochemical models tend to overlook their formation and stability, let alone existence. With a suite of analytical techniques, we address this "missing-link" and describe fundamental mechanisms for (i) the deposition of surface-active molecules in complex brines and oils on underground minerals and (ii) the desorption of heterogeneous sorbents and its dependence on aqueous composition. First, we show that organic and inorganic constituents of both formation water and crude oil form an organo-ionic surface layer on calcite. Primary modifiers are revealed as aqueous and nonaqueous polyaromatic molecules with polar and metal-binding functional groups and solubility characteristics of asphaltenes. Formed via π-stacking and ionic and hydrogen bonding interactions, the heterogeneous organo-ionic layer establishes a physical barrier between the mineral and ambient atmosphere/fluid, impacting the dissolution and wettability of rocks. Second, we investigate desorption of the organo-ionic layer in various brines under flow and static conditions. With chromatographic and spectroscopic methods (including Raman and sum-frequency generation), we show that the release of adsorbed material from carbonate surfaces encompasses key coupled reactions: (i) dissolution of "brine-soluble" asphaltenes, leading to relative interfacial enrichment of bulky "brine-insoluble" asphaltenes, (ii) nanoscale orientational changes of surface asphaltene assemblies, carbonate ions, and water molecules at the brine-rock interface, and (iii) dissolution and surface reconstruction of the carbonate mineral. Through these reactions, the "low-salinity effect" is uncovered as a two-stage desorption process: the initial release or selective extraction of "water-soluble" sorbents and subsequent delamination of residual "water-insoluble" asphaltenes from the dissolving mineral surface. Illuminating the surface reactions of geological minerals, we conclude that surface passivation by heterogeneous organo-ionic matter is not only ubiquitous in nature but also a key regulator of the interfacial chemistry, reactivity and wettability of underground rocks.

This study assesses bias error of rainfall from climate models and related error propagation effects to simulated streamflow in the Gidabo sub-basin, Ethiopia. Rainfall is obtained from a combination of four global and regional climate models (GCM-RCMs), and streamflow is simulated by means of the Hydrologiska Byråns Vattenbalansavdelning (HBV-96) rainfall-runoff model. Five bias correction methods were tested to reduce the rainfall bias. To assess the effects of rainfall bias error propagation, percent bias (PBIAS), difference in coefficient of variation (CV), and 10th and 90th percentile indicators were applied. Findings indicate that the bias of the uncorrected rainfall caused large errors in simulated streamflow. All five bias correction methods improved the HBV-96 model performance in terms of capturing the observed streamflow. Overall, the findings of this study indicate that the magnitude of the error propagation varies subject to the selected performance indicator, bias correction method and climate model.

Continental freshwater contributions are the main long-term control on the variability of suspended particulate matter (SPM) concentrations in choked lagoons. The current work aims to assess the seasonal to interdecadal variability in SPM concentrations from Patos Lagoon tributaries, as a case study towards the understanding of tributaries contributions to coastal lagoons. Thus, an interdecadal dataset (1984–2020) was collected in the region of the tributaries of Patos Lagoon (southern Brazil), integrating Landsat series (653 scenes) and in situ measurements. SPM concentrations were estimated from the scenes using a semi-analytical multiwavelength algorithm, applied in the regions of the lower course of the tributaries. Results identified SPM concentrations variability in seasonal scales, enhanced by El Niño Southern Oscillation (ENSO) in interannual time scales, also influencing in interdecadal trends. The seasonal SPM concentration variability reached 50 g.m–3 to each tributary. Higher SPM concentration values occurred from winter to spring, enhanced during El Niño periods and frequently reaching annual means higher than 100 g.m–3. Periods from summer to autumn, however, presented lower SPM concentrations, especially during La Niña periods, when the mean annual SPM concentration remained mostly below 50 g.m–3. Seasonal and interannual (ENSO) forcings combined developed configurations of high input periods (HIP) and low input periods (LIP). During the LIP, the river plumes are limited to their tributary zone of influence and the whole lagoon has reduced SPM concentrations. During the HIP, the river plumes spread over the lagoon sub-environments, and the SPM concentrations of all tributaries reach the estuarine zone and the ocean. Thus, the mentioned natural phenomena influence the river plumes spreading, the SPM concentrations in the lagoon, and the export to the Atlantic Ocean in time scales varying from seasonal to interdecadal.

Spread across Twente and its neighbouring regions in the east of the Netherlands, a network of 20 profile soil moisture and temperature (5, 10, 20, 40, and 80 cm depths) monitoring stations was established in 2009. Field campaigns have been conducted covering the growing seasons of 2009, 2015, 2016, and 2017, during which soil sampling rings and handheld probes were used to measure the top 5 cm volumetric soil moisture content (VSM) of 28 fields near 12 monitoring stations. In this paper, we describe the design of the monitoring network and the field campaigns, adopted instrumentation, experimental setup, field sampling strategies, and the development of sensor calibration functions. Maintenance and quality control procedures and issues specific to the Twente network are discussed. Moreover, we provide an overview of open third-party datasets (i.e. land cover/use, soil information, elevation, groundwater, and meteorological observations) that can support the use and analysis of the Twente soil moisture and temperature datasets beyond the scope of this contribution. An indication for the spatial representativeness of the permanent monitoring stations is provided through comparisons of the 5 cm station measurements with the top 5 cm field-averaged VSM derived from the field campaign measurements. The results reveal in general reasonable agreements and root mean squared errors that are dominated by underestimations of the field-averaged VSM, which is particularly apparent for the grass fields and is strong after heavy rain. Further, we discuss the prospects the datasets offer to investigate (i) the reliability of soil moisture references that serve the development and validation of soil moisture products, and (ii) the water and energy exchanges across the groundwater–vadose-zone–atmosphere continuum within a lowland environment in a changing climate. The datasets discussed are publicly available at https://doi.org/10.17026/dans-znj-wyg5 (Van der Velde et al., 2022).

As lateral erosion can threaten valuable saltmarsh habitats and their capacity to protect the hinterland from waves and floods, there is a need to understand the mechanisms of erosion. We monitored lateral saltmarsh erosion at high spatiotemporal resolution during a 2.5 year period. We performed terrestrial laser scans (TLS) with centimetric spatial resolution, ca monthly, as well as before and after 2 wind events to assess morphological change of a saltmarsh cliff (

Although water table recessions associated with seasonal freezing have been widely reported, the conditions that groundwater could respond to seasonal freezing are poorly known. Based on 30 records of winter-time water table recessions reported in the literature, we find the mean water table recession increases from sand to loam to silt to clay. To obtain a quantitative understanding of the conditions of freezing-induced groundwater migration and the role of soil texture, we construct a one-dimensional SHAW model with a dynamic water table that could respond to winter-time atmospheric conditions. By simulating freezing-induced groundwater migration under a series of pre-freezing water table depths, soil textures and freezing indices, we find the extinction depth of freezing-induced groundwater migration, i.e., the limit water table depth beyond which there is negligible freezing-induced groundwater flow, increases from coarse to fine soils and increases with freezing index. Moreover, we find there is a good linear relationship between the distance from the freezing front to the water table corresponding to the extinction depth of freezing-induced groundwater migration and the distance from the land surface to the water table corresponding to the extinction depth of evaporation-induced groundwater migration for different soils. To demonstrate the importance of defining extinction depth, we also show the overestimation of freezing-induced water gain in the frozen zone by the widely used fixed water table condition when the water table depth is shallower than the extinction depth. This study has implications for future hydrologic and engineering studies in critical zones with a cold climate.

The new, two-way coupled, distributed and transient MARMITES-MODFLOW (MM-MF) model, coupling land surface and soil zone domains with groundwater, is presented. It implements model-based partitioning and sourcing of subsurface evapotranspiration (ETss) as part of spatio-temporal water balance (WB). The partitioning of ETss involves its separation into evaporation (E) and transpiration (T), while the sourcing of E and T involves separation of each of the two into soil zone (Esoil and Tsoil) and groundwater (Eg and Tg) components. The objective of that development was to understand the system dynamics of a catchment with shallow water table, through spatio-temporal quantification of water fluxes and evaluation of their importance in water balances, focusing on the Eg and Tg components of ETss. While the Eg is computed using formulation from published study, the Tg is obtained through a novel phenomenological function, based on soil moisture availability and transpiration demand driven by climatic conditions. The MM-MF model was applied in the small La Mata catchment (~4.8 km2, Salamanca Province, Spain), characterized by semi-arid climate, granitic bedrock, shallow water table and sparse oak woodland. The main catchment characteristics were obtained using remote sensing, non-invasive hydrogeophysics and classical field data acquisition. The MM-MF model was calibrated in transient, using daily data of five hydrological years, between 1st October 2008 and 30th September 2013. The WB confirmed dependence of groundwater exfiltration on gross recharge. These two water fluxes, together with infiltration and Esoil, constituted the largest subsurface water fluxes. The Eg was higher than the Tg, which is explained by low tree coverage (~7%). Considering seasonal variability, Eg and Tg were larger in dry seasons than in wet season, when solar radiation was the largest and soil moisture the most depleted. A relevant observation with respect to tree transpiration was that during dry seasons, the decline of Tsoil, associated with the decline of soil moisture, was compensated by increase of Tg, despite continuously declining water table. However, in dry seasons, T was far below the atmospheric evaporative demand, indicating that the groundwater uptake by the tree species of this study constituted a survival strategy and not a mechanism for continued plant growth. The presented MM-MF model allowed to analyze catchment water dynamics and water balance in detail, accounting separately for impacts of evaporation and transpiration processes on groundwater resources. With its unique capability of partitioning and sourcing of ETss, the MM-MF model is particularly suitable for mapping groundwater dependent ecosystems, but also for analyzing impacts of climate and land cover changes on groundwater resources.

One of the major sources of pollution affecting inland and coastal waters is related to poorly treated or untreated wastewater discharge, particularly in urbanized watersheds. The excess of nutrients, organic matter, and pathogens causes an overall deterioration of water quality and impairs valuable ecosystem services. The detection of wastewater pollution is essential for the sustainable management of inland and coastal waters, and remote sensing has the capability of monitoring wastewater contamination at extended spatial scales and repeated frequencies. This study employed satellite-derived water quality indicators and spatiotemporal analysis to assess the risk of wastewater contamination in Conceição Lagoon, a coastal lagoon in Southern Brazil. Using an analytical model, three water quality indicators were derived from Level 2A Sentinel-2 MSI images: the absorption coefficients of chlorophyll-a and detritus combined with coloured dissolved organic matter, and the backscattering coefficient of suspended solids. The temporal standardized anomalies were calculated for each water quality indicator for the period of 2019–2021, and their anomalies during a known outfall event were used to evaluate spatial variation modes. The spatial mode explaining most of the variability was used to estimate weights for the water quality indicators anomalies in a linear transformation that can indicate the risk of wastewater contamination. Results showed that the wastewater spatial mode for this region was characterized by positive anomalies of backscattering coefficient of particulate matter and absorption coefficient of detritus combined with coloured dissolved organic matter, each with a relative importance of 50%. The application of this spatiotemporal analysis was formulated as the Wastewater Contamination Index. With the aid of photographic records, and additional meteorological and water quality data, the results of the index were verified for wastewater outfall events in the study area. The methodology for constructing the proposed Wastewater Contamination Index applies to other locations and can be a valuable tool for operational monitoring of wastewater contamination.

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.

High-quality and long-term measurements of water, energy, and carbon fluxes between the land and atmosphere are critical for eco-hydrological monitoring and land surface model (LSM) benchmarking. Eddy Covariance has become the most widely used method for theory development and LSM evaluation. On the other hand, flux tower data as measured (even after site post-processing and gap-filling based on empirical formulation) cannot be used directly for validating LSMs, and most of time, lacking physically-consistent measurement across the soil-plant-atmosphere continuum (SPAC) (e.g., other than surface fluxes, lacking the measurement of soil moisture, soil water potential, leaf water potential, fluorescence, and reflectance). Here we present high-quality and long-term fluxes and corresponding above/below-ground hydrological, physiological, photosynthetic data derived from the STEMMUS-SCOPE model simulations for PLUMBER2 project with 170 FLUXNET sites. Fluxes data from PLUMBER2 and SM data from FLUXNET2015 are used to further validate the effectiveness of the STEMMUS-SCOPE dataset. Results show that the simulated fluxes and SM dataset have reasonable agreements with the in-situ measurements, using the available global input/forcing datasets without any model tunning. This dataset adds to the existing ecosystem flux and SM network to help increase our understanding of ecosystem responses to extreme events.

Climate extremes like droughts and heatwaves impact how water, energy, and carbon move through ecosystems. Soil-water-plant-energy interactions can be represented by SCOPE (vegetation photosynthesis model) and STEMMUS (soil water and heat model). SCOPE simulates the radiative transfer of incident light and thermal and fluorescence radiation emitted by soil and plants, temperatures of leaves and soil in the sun and shade, photosynthesis and turbulent heat exchange whereas STEMMUS traces soil moisture and soil heat dynamics and root water uptake. The integrated model, “STEMMUS-SCOPE”, thus links vegetation dynamics to soil moisture and soil temperature variability. This helps to simulate evaporation, transpiration and carbon fluxes better, especially under water stress conditions. With STEMMUS-SCOPE, we can model variables like moisture levels in deeper soil (root-zone-soil moisture) and the amount of carbon that is stored underground (carbon sequestration) at a global scale. However, applying STEMMUS-SCOPE across ecosystems at a global scale faces numerical problems and computational challenges, such as numerical convergency of the model, optimization issues in calibration, and expensive computational cost. To overcome the challenges, we are developing tools for efficient computing and data handling within the context of EcoExtreML project. The project aims to improve the coupling of STEMMUS and SCOPE models, approximate the integrated model by a machine learning approach, and estimate uncertain model states and parameters using data assimilation techniques. The results of STEMMUS-SCOPE are currently prepared for 170 flux tower sites representing 1040 site-years of data with a half-hour time step across most of the world’s climate zones and representative biomes. In this talk, we will give you an overview of STEMMUS-SCOPE, show how the model can be used, and introduce EcoExtreML project. References: SCOPE: https://doi.org/10.5194/bg-6-3109-2009, https://github.com/Christiaanvandertol/SCOPE STEMMUS: https://doi.org/10.1007/978-3-642-34073-4, https://github.com/yijianzeng/STEMMUS STEMMUS–SCOPE : Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1.0.0), https://doi.org/10.5194/gmd-14-1379-2021 EcoExtreML project: Accelerating process understanding for ecosystem functioning under extreme climates with Physics-aware machine learning, https://research-software-directory.org/projects/ecoextreml, https://github.com/EcoExtreML

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.

Study region Akaki is a headwater catchment of Awash River Basin that hosts the capital city of Ethiopia, Addis Ababa. The area encompasses several agglomerated towns, water supply, and hydropower reservoirs and is characterized by a chain of mountains and floodplains. Due to basin rainfall, and the expansion of urbanized areas, the catchment is frequently affected by flooding. Study focus This study evaluates dynamic Bayesian Model Averaging (BMA) approach to improve rainfall estimation over the catchment by blending four high-resolution satellite rainfall estimate (SRE) products. Using daily data (2003–2019) observed at thirteen stations as a reference, seven statistical metrics served to assess the point and spatial scale accuracy of the rainfall estimates. New hydrological insights Main findings from this study are: (i) the blended product outperformed the individual SRE products by notably improving correlation with in-situ observed rainfall, and reducing the error of the estimated rainfall, (ii) the blended and individual SRE products performed better in the highlands than the lowlands of the catchment, and (iii) the amount of daily rainfall during the main-rainy season was mostly overestimated by the individual SRE products but was fairly estimated by the blended product. This study showed the nonexistence of surpassing individual SRE products and emphasized the blending of several products for gaining optimal results from each product.

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.

Active and passive microwave characteristics of diurnal soil freeze-thaw transitions and their relationships are crucial for developing retrieval algorithms of the soil liquid water content (θliq) and freeze/thaw state, which, however, have been less explored. This study investigates these microwave characteristics and relationships via analysis of ground-based measurements of brightness temperature (TB) and backscattering coefficients (σ⁰) in combination with simulations performed with the Tor Vergata discrete radiative transfer model. Both an L-band (1.4 GHz) radiometer ELBARA-III and a wide-band (1-10 GHz) scatterometer are installed in a seasonally frozen Tibetan meadow ecosystem to measure diurnal variations of TB and copolarized σ⁰ at both hh (σhh⁰) and vv (σ vv⁰) polarizations. Analysis of measurements collected between December 2017 and March 2018 shows that 1) diurnal cycles are observed in both TB and σ ⁰ due to the change in surface θliq caused by diurnal soil freeze-thaw transitions; 2) a negatively linear relationship is found between e and σ⁰ regardless of frequency, polarization combinations, and observation angles; 3) slopes (β ) of linearly fit equations between eH and σhh⁰ decrease with increasing observation angles of ELBARA-III, while the ones between eV and σvv⁰ increase with increasing observation angles; and 4) correlations between e and σ⁰ increase with decreasing microwave frequency of σ⁰ measurements and ELBARA-III observation angles, and magnitudes of diurnal σ⁰ cycles also increase with decreasing microwave frequency. Moreover, the calibrated Tor Vergata model shows capability to reproduce both diurnal e and σ⁰ variations as well as to quantify their relationships at different frequencies and observation angles.

Endorheic freshwater lakes can be vital water resources for sustaining large populations. However, their land-locked nature can lead to overexploitation and long-term sediment accumulation, reducing water storage and quality. Lake Naivasha supports a rapidly expanding population and agricultural industry. Therefore, maintaining good water storage and quality within this endorheic lake is crucial for the Kenyan economy and population. The lake has a long history of level fluctuations and the region is considered to be suffering from a chronic imbalance between water supply and demand. This study quantifies the sediment deposition rate and its impact on Lake Naivasha's water levels and volume, using inexpensive remote sensing techniques that could be easily replicated for future monitoring. Evidence of sedimentation in the northern area averaging 23 mm yr−1 was identified, which is likely annually displacing between 40.2 – 576 × 103 m³ of water. The volume displaced each year is equivalent to the water required to sustain between 40 – 1152 people. These results imply that current abstraction management, based purely upon lake level readings that govern a ‘traffic lights’ system, are detrimental to the long-term survival of the lake. The results also imply that lake health is decreasing. We recommend that future monitoring of this water resource and all endorheic lakes consider measurements of available water volume in combination with lake level data using the remote sensing methods we describe.

Suspended Particulate Matter (SPM) plays an important role in controlling water quality, particularly in turbid estuaries. SPM may be impacted by changes in weather and climate, including potential changes in the frequency or intensity of extremes. Yet, the linkages between extreme events in wind and river discharge, and particularly the role these have on local dynamics and spatial patterns of estuarine SPM are, to date, largely unknown. This study investigates the effects that wind and river discharge have on SPM in a turbid estuary. It uses atmospherically corrected remotely sensed reflectances from Landsat-5, -7, and -8. From these data, we establish a 37-year-long time series, from 1984 to 2020, of satellite-derived SPM for the Scheldt Estuary. SPM was estimated using an algorithm applied to the near-infrared band and subsequently compared to in situ SPM data. Additionally, a time series of river discharge and wind speed were used to assess the frequency and severity of extreme events of wind speed and river discharge. In general, statistically significant but weak relationships between SPM and river discharge and between SPM and wind speed were observed. SPM correlated with river discharge and wind in large parts of the estuary, indicating the important role of these drivers in the entire estuarine system. Our study demonstrates how synoptic satellite snapshots can be combined with in situ time series of drivers, such as river discharge and wind, to capture where these drivers relate to (and likely affect) SPM within an estuary. However, our study demonstrates an inability to capture SPM during windstorms both from in situ and satellite data. We discuss the challenges and limitations of assessing the effects of extreme events from satellite and in situ SPM. We recommend the deployment of complementary moored turbidimeters for continuous observations at two strategic locations, as indicated by our spatial study, to improve the ability to capture the effects of extreme events in both space and time.

This paper characterizes and compares three types of small-scale irrigation scheme practices in Northern Ethiopia. A multidisciplinary survey approach, collecting information on socioeconomic, biophysical, and institutional aspects of irrigation by the smallholder farmers, was used to investigate and compare aspects of land, water use, and crop productivity, including farmer income and livelihood sustainability. The study was conducted in the Zamra catchment, a sub-basin of the large Tekeze river basin and Nile basin tributary. Three common small-scale irrigation scheme types, i.e., traditional diversion, modern diversion, and dam (reservoir) based irrigation, were compared using four pilot survey areas. From the total of 618 farmer households in the study areas, 242 farmers were selected using stratified random sampling and participated in the survey and research. More than 100 input data were collected from the farmers related to the biophysical, socioeconomic, and institutional factors affecting their work practice and livelihood. Focus group discussions were conducted with elders, the water users association committee, and women-headed households. Descriptive statistics and multivariate analysis were used for quantitative analysis. The result indicates a significant difference between the three irrigation schemes. One important conclusion of this study was that the explanatory value of a single factor (e.g., biophysical), as commonly done in irrigation research and assessment, was seldom sufficient to explain water use, crop yield, and farmer income. Institutional and/or socioeconomic drivers also played an important role in the entire farming practice, income generation, and livelihood of the farmers. This study highlighted the value-added of the multidisciplinary approach (socioeconomic, biophysical, and institutional) for the evaluation of small-scale irrigation practices and livelihood analysis of agricultural smallholders in climate-affected regions, such as the Northern Ethiopian highlands.

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.

Mangrove forests are increasingly valued as wave-attenuating buffers in coastal flood defence strategies. However, as mangroves are vulnerable to wave-induced erosion, this raises the question, how can the stability of these protective mangrove forests be promoted? To address this question, we investigate how mangrove dynamics in a microtidal system can be related to different types of foreshores. We used remote sensing to investigate mangrove fringe stability over multiple years in relation to intertidal mudflat width (i.e., emerged at low tide) and the presence stability of cheniers, which are sand bodies on top of muddy foreshores that are characteristic for eroding coastlines. In addition, we investigated local and short-term foreshore effects by measuring wave propagation across two cross-shore transects, one with a mudflat and chenier and one with a deeper tidal flat foreshore. The satellite images (Sentinel-2) revealed that mangrove dynamics over multiple years and seasons were related to chenier presence and stability. Without a chenier, a mudflat width of 110 m (95%CI: 76–183 m) was required to make mangrove expansion more likely than mangrove retreat. When a stable chenier was present offshore for two years or more, a mudflat width of only 16 m (95%CI: 0–43 m) was enough to flip chances in favor of mangrove expansion. However, mangrove expansion remained heavily influenced by seasonal changes, and was highly event driven, succeeding only once in several years. Finally, although mudflat width was a direct driver of mangrove expansion, and could be targeted as such in coastal management, our field measurements demonstrated that cheniers also have an indirect effect on mangrove expansion. These sand banks significantly reduce wave height offshore, thereby likely creating favorable conditions for mudflat accretion landward, and thus mangrove habitat expansion. This makes stabilization - and possibly also the temporary creation - of cheniers an interesting target for mangrove conservation and restoration.

International audience; Recently, large efforts have been made to develop surface soil moisture (SSM) products based on observations from passive microwave L-band satellites at the global scale. Despite vast previous efforts to assess satellite SSM products based on in situ data, the performance of L-band SSM products remains still little known over the tropical areas including the rainforests. To close this knowledge gap, a comprehensive evaluation of five L-band SSM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, and MTDCA, as well as the recently developed SMAP INRAE-BORDEAUX (SMAP-IB) was conducted in tropical areas from 2015 to 2020. The investigation was implemented by using in situ observations, and expanded by the Triple Collocation Analysis (TCA) and double instrumental variable (IVd) methods. The results revealed that all the five L-band SSM products show a good capacity to estimate SSM in most moderately vegetated areas of the tropical areas while they exhibit some uncertainties in dense vegetation (e.g., rainforests). The SSM climatology of the five L-band products in rainforests agreed generally well with that of in situ measurements especially for SMOS-IC (R = 0.75), followed by SMAP-IB (R = 0.72). Notably, the newly developed SMAP-IB shows satisfactory performance in the tropics with the highest R of 0.73 and the smallest unbiased root mean square difference (ubRMSD) of 0.041 m3/m3 from the study based on in situ SM data, and R of >0.81 from the combined TCA/IVd method in most vegetation conditions. SMAP-DCA SSM demonstrates comparable performance to SMAP-SCA-V (R = 0.66) from the in situ based analysis. This study is expected to deepen our understanding on the skill of L-band SSM products in tropical areas and further promote possible upgrades in algorithms and applications in these regions.

During droughts, soil evaporation is often constrained by water vapor transport through an air-dry soil layer (DSL). Fick?s water vapor diffusion is widely regarded as the only process for such transport; however, field studies conducted in arid and semi-arid conditions showed measured evaporation rates higher than those predicted by diffusion. Therefore, transport processes other than diffusion could be relevant. To study the evaporation through a DSL, the same lysimeter column with 70 cm thick DSL as earlier applied in laboratory in Balugani et al. (2021), was installed in the field in Spain applying an original weighing setup to measure evaporation. The correlation between the measured evaporation and possible drivers of the water vapor transport were evaluated. With the DSL thickness of 70 cm in 2012 and 12 cm in 2015, the lysimeter recorded similar groundwater evaporation rates: 1.25 mm d-1 and 1.05 mm d-1, respectively; these rates were much larger than the laboratory recorded rates (0.3 mm d-1) and those estimated in this study using Hydrus1D accounting for non-isothermal liquid water fluxes and water vapor diffusion. The main forcing driver of the field lysimeter evaporation was the soil profile temperature fluctuation, which concealed other less important forcing factors, i.e. atmospheric pressure fluctuations and diffusion. A multivariate regression model to estimate evaporation was proposed, based on the profile temperature fluctuation, that, when added to the atmospheric pressure fluctuations, yielded reliable estimates of the cumulative evaporation measured in both 2012 and 2015. This article is protected by copyright. All rights reserved.

Improving and homogenizing time and space reference systems on Earth and, more specifically, realizing the Terrestrial Reference Frame (TRF) with an accuracy of 1 mm and a long-term stability of 0.1 mm/year are relevant for many scientific and societal endeavors. The knowledge of the TRF is fundamental for Earth and navigation sciences. For instance, quantifying sea level change strongly depends on an accurate determination of the geocenter motion but also of the positions of continental and island reference stations, such as those located at tide gauges, as well as the ground stations of tracking networks. Also, numerous applications in geophysics require absolute millimeter precision from the reference frame, as for example monitoring tectonic motion or crustal deformation, contributing to a better understanding of natural hazards. The TRF accuracy to be achieved represents the consensus of various authorities, including the International Association of Geodesy (IAG), which has enunciated geodesy requirements for Earth sciences. Moreover, the United Nations Resolution 69/266 states that the full societal benefits in developing satellite missions for positioning and Remote Sensing of the Earth are realized only if they are referenced to a common global geodetic reference frame at the national, regional and global levels. Today we are still far from these ambitious accuracy and stability goals for the realization of the TRF. However, a combination and co-location of all four space geodetic techniques on one satellite platform can significantly contribute to achieving these goals. This is the purpose of the GENESIS mission, a component of the FutureNAV program of the European Space Agency. The GENESIS platform will be a dynamic space geodetic observatory carrying all the geodetic instruments referenced to one another through carefully calibrated space ties. The co-location of the techniques in space will solve the inconsistencies and biases between the different geodetic techniques in order to reach the TRF accuracy and stability goals endorsed by the various international authorities and the scientific community. The purpose of this paper is to review the state-of-the-art and explain the benefits of the GENESIS mission in Earth sciences, navigation sciences and metrology. This paper has been written and supported by a large community of scientists from many countries and working in several different fields of science, ranging from geophysics and geodesy to time and frequency metrology, navigation and positioning. As it is explained throughout this paper, there is a very high scientific consensus that the GENESIS mission would deliver exemplary science and societal benefits across a multidisciplinary range of Navigation and Earth sciences applications, constituting a global infrastructure that is internationally agreed to be strongly desirable., Earth, Planets and Space, 75 (1), ISSN:1343-8832, ISSN:1880-5981

Reef-forming species form integral aspects of coastal ecosystems, but are rapidly degrading world-wide. To mitigate these declines, nature managers increasingly rely on the restoration of habitat-structuring, reef-forming species by, for example, introducing artificial reefs that may directly function as complex reef habitat. Since the use of biodegradable structures to restore biogenic reefs is becoming a popular technique, its effectiveness as reef habitat must be assessed. Therefore, we examine the trophic complexity on experimental large-scale biodegradable artificial reefs using food web network analysis. We placed biodegradable artificial reefs on soft-sediment intertidal flats in the Dutch Wadden Sea in a large-scale (~650 m) and 2.5-year-long experiment. We compared food web networks and biodiversity indicators between biodegradable reefs and bare controls and quantified species composition inside and near the artificial reef community to assess the expansion of the reef community. During 2.5 years, we observed that artificial reefs changed food web networks compared to bare controls: in species richness (+76%), link density (the number of interactions per species; +15%) and the fraction of basal species (species of lowest trophic level; +40%), but lowered the connectance: the realized fraction of all possible links between species (−33%). Their effects on food web networks increased over time with a higher species richness (+22%) and more complex food web (link density +13%) on the artificial reef 2.5 years after deployment compared to 1.5 years. However, the effects of the reefs did not extend beyond the reef structures; the species composition and biodiversity of macrozoobenthos near the reefs were comparable to the control. Synthesis and applications. This study shows that biodegradable artificial reefs offer an effective tool for the restoration of food web complexity and biodiversity of intertidal soft-sediment systems. However, application needs to be carefully considered as the reef-building species did not expand beyond our structures, despite the ambitious spatial extent of this experiment. Therefore, we recommend restoration practitioners to design artificial reefs in such a way that they generate ecosystem connectivity (facilitation of higher trophic levels) and biogeomorphological effects on a landscape scale (reef expansion beyond the structures).

Water quality monitoring is essential for effective water resources management. Present state of the art satellite sensors provide high spatial and temporal resolution required for operational monitoring of key water quality variables in coastal and inland waters. Nonetheless, most of the retrieval models currently available require regional tuning and are therefore not applicable to other geographic areas and not useful in detecting anomalies. This study introduces an operational model to process Sentinel-2 images from any part of the world and derive water quality indicators based on the analytical solution of the two-stream radiative transfer equations. The potential of the model for monitoring distinct water bodies is demonstrated with two case studies in Brazil and Kenya. Scientific and technical improvements to the model can provide additional robustness and usability.

The detection of solar induced chlorophyll fluorescence (SIF) in the field with spectrometers is based on the depth of the solar Fraunhofer or oxygen absorption lines in the upwelling radiance compared to that in the downwelling irradiance. This relative depth enables the differentiation of SIF from the reflected radiation. Recent studies have shown that if oxygen bands are used to retrieve SIF from tower-based measurements, then atmospheric correction is required. This study presents a band shape fitting (BSF) approach to retrieve both the relative optical path length (deepening) and SIF (infilling) from field measurements at the same time, using information in the measured spectral shape of the O2 feature. This approach is an alternative to using radiative transfer process models for estimating atmospheric transmittance. The method was applied to measurements taken from 100 m elevation above a forest, yielding plausible results for SIF in the O2A and O2B bands. The sensitivity to combined atmospheric and instrument characteristics prohibits application at much greater distances from the surface.

This study concerns the effects of considering vertically heterogeneous canopy structure when modeling microwave scattering from grassland with the Tor Vergata (TVG) model, which uses the matrix doubling method (MDM). The TVG model was extended with the M-volume approach to accommodate height-dependent variation in structure for every scatterer type. Used approach was to reproduce 1 – 10 GHz backscatter for all linear polarization combinations from an alpine meadow measured by a ground-based scatterometer with both the default (1-volume) and the M-volume approach with 3 volumes. Measured in-situ vegetation parameters were used to constrain the model. We found that both models were able to reproduce the angle-dependent backscattering for C- and X-band, measured on two afternoons, and the 31-day average measured radar return power for L-, S-, C-, and X-band within, or close to, the measurement uncertainty. Our analysis proved inconclusive on whether the 1- or 3-volume approach worked better for the considered grassland, but did show that the 3-volume approach allows for more flexibility in reproducing the actual angle-dependent backscattering for multiple frequencies, a flexibility that may prove necessary when more scattering angles are considered. Furthermore, predictions of the bistatic scattering coefficient at higher frequencies (C- and X-band) were significantly different between both models. For X-band with hh polarization differences up to 3 dB were found for the specular direction. We conclude that considering vertical heterogeneity of vegetation canopy structure with MDM leads to significantly different results than with the vertically homogeneous canopy approximations typically used.

Effects of anoxia and subsequent reaeration on formation of hydrogen peroxide, and activities of catalase (EC 1.11.1.6) and guaiacol peroxidase (EC 1.11.1.7) were studied in wheat and rice plants that are contrasting in their tolerance to hypoxia. It was found that anoxia and reaeration, especially long-term, strongly increased the H2O2 production in hypoxia-intolerant wheat plants. Meanwhile, such treatment did not significantly entail peroxide production in rice, which is tolerant to oxygen lack. In these tolerant plants, the activities of catalase, as well as intra- and extracellular guaiacol peroxidases, rapidly increased under anoxia and the following oxidative stress. By contrast, wheat did not display stimulation of catalase activity; activation of its peroxidase occurred only in the shoots after a short anoxia and long reaeration. The increased or stable activities of catalase and peroxidase in the rice seedlings, under anoxia and reaeration, were inhibited by cycloheximide and, to smaller extent, actinomycin D. In rice, the stimulation of catalase activity was accompanied by the increased expression of the corresponding genes, in particular, OsCatB and OsCatС. The activities of apoplastic peroxidases were suppressed by brefeldin A. Electrophoresis did not reveal the emergence of new isoforms of guaiacol peroxidase under anoxia and post-anoxic aeration. Meanwhile, the activities of the majority of preexisting isoforms in rice and individual isoforms in wheat were considerably stimulated under these conditions. It is suggested that, in the tolerant plants subjected to anoxia and post-anoxic aeration, the activation of apoplastic peroxidases fulfills an adaptive function decomposing hydrogen peroxide in the cell wall and preventing its penetration into the cytosol. Under long-term reaeration, detoxification of ROS in the rice cytosol may be favored by activation of catalase and intracellular peroxidase. Unlike rice, the absence of activation of the antioxidant enzymes in the wheat cell wall allows ROS to penetrate inside the cell where they are deactivated by cytoplasmic forms of guaiacol peroxidase. The efficient work of antioxidant enzymes under anoxia, followed by post-anoxic aeration, prevents ROS accumulation in the tolerant plant and, thus, represents an important adaptation to both lack of oxygen and subsequent oxidative stress.

Due to the high cost of the large-scale measurement of heavy metals, the use of statistical land models and techniques is one of the proper ways to study their distribution and level of pollution. The study area, is agricultural lands of south Tehran in Iran. Municipal wastewater is often used for irrigation agricultural lands under surface irrigation method. To study the distribution of heavy metals including copper, nickel, and lead, the ordinary kriging method in the GIS environment was used. In addition, one-dimensional HYDRUS modeling of water flow and heavy metals in the soil environment was simulated up to a depth of 50 cm for 210 days and the concentration of heavy metals in the depth was simulated. Distribution of lead element in soil surface with spherical model showed that its variation was in the range of 20–70 mg/kg. These values were 50–60 mg/kg for copper and 30 mg/kg for nickel. Investigation of heavy metal concentrations in soil profiles using the HYDRUS-1D model showed that the major accumulation of heavy metals occurred in the surface layer of soil at a depth of 0–15 cm that was higher than the permissible level.

Increased Greenland ice sheet melting has an impact on global mean and regional sea level rise and the ocean circulation. In this study, we explore whether Greenland melting signatures found in ocean model simulations are visible in observations from radar altimetry, satellite gravimetry and Argo floats. We have included Greenland freshwater flux (GF) in the global Finite-Element-Sea ice-Ocean Model (FESOM) for the years 1993–2016. The reference run is computed by excluding Greenland freshwater input. These experiments are performed on a low resolution (ca. 24 km) and a high resolution (ca. 6 km) eddy-permitting mesh. For comparison with the model experiments, we use different observational data, such as Argo floats, satellite observations, and reanalyses. We find that surface GF maps into signatures in temperature and salinity down to about 100 m in the surroundings of Greenland. The simulated melting signatures are particularly visible in steric heights in Baffin Bay and Davis Strait. Here, we find an improvement of the mean square error of up to 30% when including GF. For the Nordic part of the Nordic Seas, however, we find no improvement when including GF. We compare steric heights with reanalysis data and a new setup of the inversion method from gravimetric and altimetric satellite data. We cannot confirm that the GF signatures on variables such as temperature and salinity are visible in the observations on the time scales considered. However, we find that increased model resolution often causes larger improvements than occur due to including the simulated melting effect.

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ, data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R2 0.36 and RMSE 2.5 t ha−1. This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields.

Many operational applications, including agriculture and water resources management, local climate studies, flash flood prediction and forecasting that directly affect human livelihoods, need accurate high-resolution rainfall information. High-resolution rainfall is also vital in research applications such as evaluating satellite rainfall products to inform new satellite rainfall observation missions. However, despite its great value, rainfall’s intricate characteristics, such as intermittency and spatial variation, make accurate rainfall estimation challenging. There are existing techniques to measure rainfall, but each has an inherent limitation. For instance, rain gauges measure accurate rainfall from a point and are thus limited in their ability to measure the spatial state of rainfall. The weather radars are capable of accurate spatial rainfall information, but among other limiting factors, they are expensive to acquire, operate and maintain. Satellites are capable of global rainfall observations, but their estimates are often inaccurate. Moreover, their observations, usually averaged over squared kilometres, are not always ideal for all applications, e.g., farmers whose plots are only a few hundred squared meters. Over a decade ago, a new technique was introduced that utilises telecom service operators' extensive mobile phone infrastructure for rainfall observations. The microwave signals beamed from one telephone tower to the other are significantly affected by rain. For this reason, researchers studied the fluctuations in the signal level recorded at the receiving tower and showed that these systems can estimate accurate rainfall; suggesting that each microwave link (MWL)–a pair of connected towers–can serve to gauge rainfall along two connecting mobile phone towers in near real-time. Albeit opportunistic and low-cost rainfall estimation technique, the problem is that the estimation accuracy is affected by many factors, including the variation of raindrop sizes along the link and the fact that the link’s antenna wetting during and after rainfall introduces additional uncertainties in the retrieved rainfall estimates. Moreover, the density of the MWL network is arbitrary and often biased towards densely populated areas. This characteristic may complicate their use for retrieving spatial rainfall patterns, especially in areas with low network densities. At the same time, geostationary (GEO) meteorological satellites, such as the Meteosat Second Generation (MSG), frequently observe the earth’s atmosphere with wide spectral range radiometers capable of detecting rapidly developing storms, raining areas and estimating rainfall intensities. Several studies have extensively studied the data with traditional rainfall measurement techniques such as rain gauges and weather radars for rainfall observation. However, combing the GEO satellite and MWL data for rainfall observation has gained very little attention, while such an alternative could benefit many places that lack traditional rainfall observation systems. For this reason, the main objective of this study was to investigate the MWL and the MSG satellite data for high spatiotemporal resolution rainfall detection and estimation using data from Central and Western Kenya, where traditional rainfall-observing systems are often lacking or sparse. This main objective was achieved based on 4 specific objectives, which form the basis of 4 individual research studies. Firstly, it was investigated if the MWL-MSG data combination could improve rain rate estimation and detection. We investigated the MWL’s capability to estimate accurate rain rates in the study area using gauge rainfall data. Then, the MWL’s rain rates were studied with the MSG satellite data using a conceptual model in which clouds with high cloud top optical thickness and particle effective radius have high rainfall probabilities and rain rates. Regarding the MWL’s rainfall estimation capability, the results confirmed the robustness of the MWL rainfall estimation technique concluded by many past studies. Studying the MWL’s rain rate with the MSG satellite data revealed unique spectral characteristics of daytime, night-time, raining and non-raining data. Eventually, descriptive statistics derived from the satellite spectral characteristics successfully detected rainfall on individual MWL in the study area. However, daytime rain detection, which uses reflectance data, was better than night-time detection, which uses infrared (IR) data, owing to the better rain information about cloud optical and the effective radius contained in the reflectance data. Following the successful combination of the MWL-MSG data for rainfall observation, the subsequent investigation developed an improved rain area detection system to improve the MWL’s rainfall estimates and map rainfall in the study area. This investigation evaluated multiple parametric rain detection models derived from MSG’s reflectance and IR data using a conceptual model similar to the previous research. Additionally, we developed a new technique that uses rain area-specific gradient parameters to improve detected rain areas by correcting the number and sizes of the detected rain area. While comparing the rain area technique with one of the most successful satellite rainfall products, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG), the results corroborate in terms of the spatial dynamics of the detected rain areas and rates. Next, we improved the MWL’s rainfall estimates for the first time in a new MSG technique that uses the rain area detection system developed in the previous study. In this investigation, we developed a new technique for wet-dry classification and baseline level estimation using the MSG-based rain area information. A new wet path length (wpl) parameter, representing the length of the raining MWL, was also developed based on the MSG-based rain area. This technique’s wet-dry classification and baseline level estimation results agreed well with conventional technique results. The wpl parameter also remarkably improved the high rainfall intensities, which suggests that spatial rainfall variability over the MWL remains essential information to be considered in the MWL’s rain rate retrieval. Finally, a new technique was developed for high spatiotemporal rainfall estimation from MSG’s cloud top properties and MWL rainfall intensities. This technique trains the random forest (RF) machine learning algorithm with the MWL‘s rainfall estimates to estimate rainfall from the MSG data. The results are convincing and promising. When compared to gauge rainfall data, the techniques’ retrieval errors were comparable to errors found when comparing GPM IMERG and the European Organisation for the Exploitation of Meteorological Satellites Multi-sensor Precipitation Estimate (EUMETSAT MPE) rainfall intensities to gauge data. The spatial dynamics of the retrieved rainfall intensities also agreed well with these satellite products. The technique’s advantage lies in retrieving high spatiotemporal resolution rainfall intensities regardless of the rainfall type. Overall, this study demonstrates the great potential of using the MWL-MSG data for rainfall detection and estimation. In particular, the benefit of this rainfall observation alternative to areas with sparse or lacking conventional ground rainfall monitoring systems but growing MWL network and geostationary satellite (with capabilities like MSG) coverage may be invaluable.

Soil moisture content (SMC) information at field scale could have important applications in agricultural and regional water management. This study presents an operationally applicable scheme for SMC retrieval over meadows from synthetic aperture radar (SAR) backscatter (σ0) observations. We parameterized the vegetation scattering and absorption model developed at the Tor Vergata University of Rome (TV) and the integral equation method (IEM) surface scattering model for grass-covered soil surfaces. Leaf area index (LAI) estimates from a Sentinel-2 product provide field-scale vegetation information, as is demonstrated by validation against in situ measurements. The SMC retrieval scheme is applied with field-averaged Sentinel-1 σ0 observations from November 2015 to November 2018 and evaluated on 21 meadows against adjacent in situ station measurements, without (IEM) and with a vegetation correction (TV-IEM). Masking the IEM and TV-IEM SMC retrievals for dense vegetation conditions improves their performance, but this is a trade-off with the number of retrievals. By setting the SMC retrievals that exceed the upper retrieval limit of 0.75 m3 m−3 during the wet period to the maximum SMC, the performance metrics improve to mean Pearson correlation coefficients of 0.55 for IEM and 0.64 for TV-IEM, root mean square deviations (RMSD) of 0.14 m3 m−3 for IEM and 0.13 m3 m−3 for TV-IEM, and RMSDs relative to the range of the SMC references of 24 % for both IEM and TV-IEM. The slightly better SMC retrieval performance with TV-IEM is caused by invalid SMC retrievals under dense vegetation conditions, and the performance metrics for IEM and TV-IEM are practically equal by considering the same retrieval–reference pairs. The IEM and TV-IEM retrieval performances are also similar to the performances of two other Sentinel-1 based products at field scale. They are, on average, outperformed by NASA's Soil Moisture Active Passive (SMAP) 9 km and 36 km products evaluated at field scale, but these products are expected to deviate if larger regional differences are present and in field-specific situations.

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.