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Accurate soil moisture measurements are key for optimising field irrigation and for the precise monitoring of water flow in soil profiles or large watersheds. In recent decades, various soil moisture sensors (SMSs) have been widely used, and several studies have been conducted to evaluate the performance of the SMS technologies under different soil and field conditions. The soluble salt content in soil can affect the sensitivity of SMSs because of their influence on soil permittivity. In this field study, we evaluated different types of SMSs in gypsiferous and salt-affected soils located at two agricultural sites in western Iraq. Two WaterScout capacitance sensors, SM100 and SMEC300, were evaluated in addition to the Watermark granular matrix sensor. Soil moisture measurements by the gravimetric method were compared with SMS readings. Bulk electrical conductivity (ECb) ranged between 3.3–8.1 and 5.6–10.3 dS m−1 for sites 1 and 2 respectively, during the experiment. The results showed that site-specific calibration of the sensors significantly increased their accuracy. At Site 1, the RMSE values for the Watermark, SM100, and SMEC300 sensors changed from 3.48, 2.68, and 1.96 before calibration to 1.25, 0.91, and 0.90, respectively. For site 2, the RMSE values changed from 3.78, 3.09, and 2.34, to 1.37, 1.03, and 0.95, respectively. After the site-specific calibration, the capacitance sensors were sufficiently accurate for scientific measurements. The granular matrix sensors should only be used for field irrigation management if a high-accuracy level is not required and only after site-specific calibration. • SMSs estimate soil permittivity as an indicator to soil moisture content. • Salinity influences the accuracy of SMSs due to the impact on dielectric properties. • In situ calibration of SMSs are important for the reliability of the measurements. • Our study focuses on performance evaluation of different SMSs in salt-affected soils. [ABSTRACT FROM AUTHOR]

In the past few years, small‐scale (2 m) prepolarized surface nuclear magnetic resonance (SNMR) has gained increasing interest in the research community. As recent studies demonstrated, the application of a strong prepolarization field enhances the SNMR signal of coils with a footprint <1 m2 up to a level that even enables investigations in urban areas. In particular, it is expected that this noninvasive method provides the soil moisture distribution in the upper 2 m of the subsurface in the near future. However, until now all field experiments have been carried out on water reservoirs only, in an approach to test and implement this rather new technique into the field of SNMR applications. We present the first prepolarized SNMR measurement on a real soil and demonstrate the general feasibility of this technique to qualitatively and quantitatively detect soil moisture in the upper first 0.5 m. Our soil moisture measurements are validated by independent time domain reflectometry data. To complement the field experiments with numerical simulations, we adapted the underlying SNMR spin dynamics simulations and account for prepolarization switch‐off effects in the forward modeling of the SNMR excitation. Core Ideas: Small‐scale surface nuclear magnetic resonance experiment was used to investigate the vadose zone.Prepolarization was used to significantly enhance the SNMR signal quality.This is the first irrigation experiment that quantifies the soil moisture with prepolarized SNMR. [ABSTRACT FROM AUTHOR]

The spatial variation of soil moisture over very small areas (<100 m2) can have nonlinear impacts on cycling and flux rates resulting in bias if it is not considered, but measuring this variation is difficult over extensive temporal and spatial scales. Most studies examining spatial variation of soil moisture were conducted at hillslope (0.01 km2) to multi‐catchment spatial scales (1000 km2). They found the greatest variation at mid wetness levels and the smallest variation at wet and dry wetness levels forming a concave down relationship. There is growing evidence that concave down relationships formed between spatial variation of soil moisture and average soil moisture are consistent across spatial scales spanning several orders of magnitude, but more research is needed at very small, plot scales (<100 m2). The goal of this study was to characterise spatial variation in shallow soil moisture at the plot scale by relating the mean of measurements collected in a plot to the standard deviation (SD). We combined data from a previous study with thousands of new soil moisture measurements from 212 plots in eight catchments distributed across the US Mid‐Atlantic Region to (1) test for a generalisable mean–SD relationship at plot scales, (2) characterise how landcover, land use, season, and hillslope position contribute to differences in mean–SD relationships, and (3) use these generalised mean–SD relationships to quantify their impacts on catchment scale nitrification and denitrification potential. Our study found that 98% of all measurements formed a generalised mean–SD relationship like those observed at hillslope and catchment spatial scales. The remaining 2% of data comprised a mean–SD relationship with greater spatial variation that originated from two riparian plots reported in a previous study. Incorporating the generalised mean–SD relationship into estimates of nitrification and denitrification potential revealed strong bias that was even greater when incorporating mean–SD observations from the two riparian plots with significantly greater spatial variation. [ABSTRACT FROM AUTHOR]

Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies. [ABSTRACT FROM AUTHOR]

The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWSs). However, it is still unclear which source of information – numerical models or in situ measurements – is of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019, using a physically based 1D soil moisture transfer model. A common parameterization set was defined for all sites, except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and receiver operating characteristic analysis (ROC) was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different model parameterizations, to various distances between simulation sites and landslides and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes, and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage. [ABSTRACT FROM AUTHOR]

This article shows the feasibility of a high-frequency RFID passive sensor tag equipped with sensors based on a thin-film circuit on a Si substrate for the measurement of soil moisture. Three tags embedding different sensing systems compliant with the energy harvesting requirements were developed. The used sensors were previously characterized with the aim of tailoring an adequate electronic front-end. All the typical solutions for soil moisture detection were implemented: a fringe capacitance sensor, a soil impedance sensor, and finally a humidity sensor based on functionalization with TiO2 nano particles. The obtained results prove that the conceived solution is satisfactory and that the sensors have good sensing properties. The proposed system can be applied within the Internet of Things (IoT) framework in manifold monitoring scenarios of soil moisture sensing. [ABSTRACT FROM AUTHOR]

A 3-year study was conducted to determine the irrigation requirements for field tomato production in southwestern Ontario, Canada. Irrigation requirements were ascertained using both a weather-based crop water model, and data from soil moisture sensors. The equivalent irrigation depth applied based on real-time soil moisture measurements among four soil moisture treatments ranged from 102 to 168 mm, 85 to 190 mm, and 58 to 196 mm for 2008, 2009, and 2010, respectively. Using the crop simulation model—AquaCrop, the net irrigation water requirement (NIWR) was estimated as 163–236 mm over the 3-year period. The model overestimated the NIWR compared to the real-time sensor-based measurements. A comparison between these two approaches indicated that a soil moisture depletion of ≤ 45% available water content (AWC) resulted in adequate moisture to meet crop water requirements, and correspondingly produce higher crop yields. From this study, we conclude that real-time soil moisture sensors proved to be very effective for scheduling irrigation of processing tomatoes in Eastern Canada and leads to higher water savings. [ABSTRACT FROM AUTHOR]

Several indices are used for drought identification and quantification. In this paper, the new Standardized Palmer Drought index (SPDI) was introduced and then the drought condition of Chaharmahal-Va-Bakhtiari Province was studied using this index. For this study, 11 synoptic, climatology, and evaporation meteorology stations were selected. Essential information in this investigation includes monthly temperature, monthly precipitation, and soil moisture measurement. To estimate SPDI, moisture departure, was first calculated on a monthly time scale. Then, converted to cumulative moisture departure values in different time scales including 3, 6, 9, 12, and 24 months. The best statistical distribution (GEV) was then fitted to cumulative departure. These values were then standardized to have the SPDI. The results showed that, as soil moisture affects SPDI estimation, it will be more valid for analyzing and monitoring drought conditions, especially for agricultural drought. Also, the results showed that 2000, 2001, and 2008 years were the driest time in this Province from 1988 to 2012. Moreover, drought frequency was found out in the western half of the Province more than in the other parts. [ABSTRACT FROM AUTHOR]

The design and construction of a waste rock pile influences water infiltration and may promote the production of contaminated mine drainage. The objective of this project is to evaluate the use of an active fiber optic distributed temperature sensing (aFO‐DTS) protocol to measure infiltration and soil moisture within a flow control layer capping an experimental waste rock pile. Five hundred meters of fiber optic cable were installed in a waste rock pile that is 70 m long, 10 m wide, and was covered with 0.60 m of fine compacted sand and 0.25 m of non‐reactive crushed waste rock. Volumetric water content was assessed by heating the fiber optic cable with 15‐min heat pulses at 15 W/m every 30 min. To test the aFO‐DTS system 14 mm of recharge was applied to the top surface of the waste rock pile over 4 h, simulating a major rain event. The average volumetric water content in the FCL increased from 0.10 to 0.24 over the duration of the test. The volumetric water content measured with aFO‐DTS in the FCL and waste rock was within ±0.06 and ±0.03, respectively, compared with values measured using 96 dielectric soil moisture probes over the same time period. Additional results illustrate how water can be confined within the FCL and monitored through an aFO‐DTS protocol serving as a practical means to measure soil moisture at an industrial capacity. [ABSTRACT FROM AUTHOR]

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national‐scale coordination for soil moisture monitoring in the United States. Core Ideas: Soil moisture is a critical yet underrepresented land surface variable.Soil moisture data collection is undergoing rapid growth and innovation.We present a strategy for a nationally coordinated monitoring network. [ABSTRACT FROM AUTHOR]

Although cosmic‐ray neutron sensing (CRNS) is probably the most promising noninvasive proximal soil moisture measurement technique at the field scale, its application for hydrological simulations remains underexplored in the literature so far. This study assessed the use of CRNS to inversely calibrate soil hydraulic parameters at the intermediate field scale to simulate the groundwater recharge rates at a daily timescale. The study was conducted for two contrasting hydrological years at the Guaraíra experimental basin, Brazil, a 5.84‐km², a tropical wet and rather flat landscape covered by secondary Atlantic forest. As a consequence of the low altitude and proximity to the equator low neutron count rates could be expected, reducing the precision of CRNS while constituting unexplored and challenging conditions for CRNS applications. Inverse calibration for groundwater recharge rates was used based on CRNS or point‐scale soil moisture data. The CRNS‐derived retention curve and saturated hydraulic conductivity were consistent with the literature and locally performed slug tests. Simulated groundwater recharge rates ranged from 60 to 470 mm yr–1, corresponding to 5 and 29% of rainfall, and correlated well with estimates based on water table fluctuations. In contrast, the estimated results based on inversive point‐scale datasets were not in alignment with measured water table fluctuations. The better performance of CRNS‐based estimations of field‐scale hydrological variables, especially groundwater recharge, demonstrated its clear advantages over traditional invasive point‐scale techniques. Finally, the study proved the ability of CRNS as practicable in low altitude, tropical wet areas, thus encouraging its adoption for water resources monitoring and management. Core Ideas: Cosmic‐ray neutron sensing (CRNS) can detect field‐scale soil moisture also at low latitudes.Soil hydraulic parameters were calibrated using CRNS and inverse simulations.Daily groundwater recharge rates were simulated using CRNS‐based soil hydraulic parameters.CRNS‐based recharge was matching independent area average recharge estimates. [ABSTRACT FROM AUTHOR]

Biofuel crops, including annuals such as maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and canola (Brassica napus L.), as well as high‐biomass perennial grasses such as miscanthus (Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large‐scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009–2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots being notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2–34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011. Core Ideas: Expansion of cellulosic biofuels may cause large impacts on the water balance.Root processes that control ET are poorly characterized in new agricultural systems.Electrical resistivity measurements are sensitive to root water uptake processes.A new coupled hydrogeophysical inversion can estimate crop parameters.Higher LAI and earlier emergence results in greater modeled ET in the biofuel grasses. [ABSTRACT FROM AUTHOR]

Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass‐yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county‐level wild hay yield reported by the National Agricultural Statistics Service (NASS). We next evaluated the performance of mechanistic, evapotranspiration (ET)‐driven grassland productivity models with and without assimilation of measured FAW into the models' water balance routines. Models were calibrated by comparing estimated ET with ET measured using eddy covariance, and calibration proved essential for accurate ET estimates. Models were validated by comparing NASS county‐level hay yields to the modeled yields, which were the product of normalized transpiration estimates (the ratio of transpiration to reference ET) and an empirically derived grassland water productivity (the ratio of accumulated biomass to normalized transpiration) estimate. The mechanistic model produced more accurate estimates of wild‐hay yields with soil moisture data assimilation (Nash–Sutcliffe efficiency [NSE] = 0.55) than without (NSE = 0.10). These results suggest that improved estimates of grassland productivity could be achieved using in situ soil moisture, which could benefit grazing management decisions, wildfire preparedness, and disaster assistance programs. Core Ideas: In situ soil moisture data were used to estimate grassland biomass productivity.Statistical and mechanistic models were assessed as alternative applications of the data.Soil moisture correlated strongly with productivity in a simple statistical model.The mechanistic model was improved after calibration and with soil moisture.Soil moisture data have good potential for grassland productivity forecasting. [ABSTRACT FROM AUTHOR]

The effect of clear-fell harvesting on soil greenhouse gas (GHG) fluxes of carbon dioxide (CO 2), methane (CH 4), and nitrous oxide (N 2 O) was assessed in a Sitka spruce forest growing on a peaty gley organo-mineral soil in northern England. Fluxes from the soil and litter layer were measured monthly by the closed chamber method and gas chromatography over 4 years in two mature stands, with one area harvested after the first year. Concurrent measurements of soil temperature and moisture helped to elucidate reasons for the changes in fluxes. In the 3 years after felling, there was a significant increase in the soil temperature, particularly between June and November (3 to 5 ∘ C higher), and in soil moisture, which was 62 % higher in the felled area, and these had pronounced effects on the GHG balance in addition to the removal of the trees and their carbon input to the soil. Annual soil CO 2 effluxes reduced to almost a third in the first year after felling (a drop from 24.0 to 8.9 t CO 2 ha -1 yr -1) and half in the second and third year (mean 11.8 t CO 2 ha -1 yr -1) compared to before felling, while those from the unfelled area were little changed. Annual effluxes of N 2 O more than doubled in the first two years (from 1.0 to 2.3 and 2.5 t CO 2e ha -1 yr -1 , respectively), although by the third year they were only 20 % higher (1.2 t CO 2e ha -1 yr -1). CH 4 fluxes changed from a small net uptake of - 0.03 t CO 2e ha -1 yr -1 before felling to a small efflux increasing over the 3 years to 0.34 t CO 2e ha -1 yr -1 , presumably because of the wetter soil after felling. Soil CO 2 effluxes dominated the annual net GHG emission when the three gases were compared using their global warming potential (GWP), but N 2 O contributed up to 20 % of this. This study showed fluxes of CO 2 , CH 4 , and N 2 O responded differently to clear-felling due to the significant changes in soil biotic and abiotic factors and showed large variations between years. This demonstrates the need for multi-year measurements of all GHGs to enable a robust estimate of the effect of the clear-fell phase on the GHG balance of managed forests. This is one of very few multi-year monitoring studies to assess the effect of clear-fell harvesting on soil GHG fluxes. [ABSTRACT FROM AUTHOR]

Aims: Hydraulic redistribution (HR) enhances water resources for neighboring crops in silvopastoral agroforestry (AF). Here, we tested whether and to what extent water stressed shallow-rooted neighboring plants benefit from water redistributed by deep-rooted poplar plants. Methods: We conducted trace experiments with deuterated water (2H2O) in greenhouse soil column experiments. We measured hydraulic lift (HL) by poplars grown at two levels of soil drying and estimated the amount of hydraulically lifted water. In a parallel experiment we grew poplars and barley (Hordeum vulgare) in two columns connected via a small cross-rooting segment. Results: Soil moisture measurements and stable isotope signatures of soil and xylem water proved the occurrence of HL in poplar. Additionally, stable isotopes proved the transport of water from deep roots of poplars to shallow roots of barley. Conclusions: In conclusion, the experiments showed that poplars are capable to redistribute water during drought spells and that this water can facilitate plant growth of shallow-rooted crops. This result implies evidence for an enhanced soil water supply of plants in agroforest systems under drought conditions. [ABSTRACT FROM AUTHOR]

Measurements of soil moisture have great significance for agricultural production and environmental protection. A variety of technologies have been introduced into the current monitoring of surface soil moisture, particularly widely-used optical remote sensing and synthetic aperture radar (SAR) microwave remote sensing. In this study, retrieval for surface soil moisture in farmland was proposed using multi-source remote sensing data and feature dimension reduction with GA-BP neural networks. Sentinel-1 microwave and Sentinel-2 optical remote sensing data were used with a high spatial and temporal resolution. Three field surveys and sampling were carried out simultaneously during the transit time of the Sentinel-1 satellite. A total of 20 sampling points were set on the ground in the study area to collect soil moisture in the longitude and latitude coordinates. Firstly, Sentinel-1 and Sentinel-2 data were preprocessed to extract 21 feature parameters, including 9 backscatter coefficient, 5 polarization characteristic parameters, surface roughness, and 6 vegetation indices. Then, the differential evolution feature selection (DEFS) was utilized to obtain an optimal feature subset with 10 parameters, including the incident angle, ..., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Water Band Index (WBI), and Fusion Vegetation Index (FVI). Principal component analysis (PCA) was used to further reduce the dimension of the optimal feature subset. The feature subset was reduced to eight dimensions, where an eight-dimensional feature matrix was obtained. After that, back propagation (BP) neural network was established to describe the nonlinear relationship between characteristic parameters and surface soil moisture, whereas, genetic algorithm (GA) was used to optimize the node weights and accelerated the learning speed of the BP neural network. The feature matrix after dimension reduction and some measured data of soil moisture were input into the GA-BP network for training, where the distribution map of soil moisture was obtained in the study area. Finally, taking a winter wheat field in Henan Province as the study area, three comparative experimental schemes were set to verify the accuracy of inversion using the measured data. The experimental schemes with DEFS and PCA presented the highest accuracy, where the coefficient of determination was 0.789 3, and the root mean square error was 0.028 7 cm³/cm³, compared with the genetic BP neural network, indicating the coefficient of determination increased by 0.215 7, and the root mean square error was reduced by 0.029 5 cm³/cm³. Meanwhile, the frequency distribution of soil moisture inversion was basically consistent with the measured soil moisture of sampling points. The experimental results demonstrated that the GA-BP network combining with DEFS and PCA can eliminate the redundant characteristic parameters for high inversion accuracy, and a high-resolution distribution map of surface soil moisture with a large area. The finding can offer some advantages and application potentials to the surface soil moisture retrieval in farmland. [ABSTRACT FROM AUTHOR]

Cosmic‐Ray Neutron Probes (CRNP) have found application in soil moisture (SM) estimation due to their conveniently large (>100 m) footprints. Here, we explore the possibility of using high‐density polyethylene (HDPE) moderator to limit the field of view, and hence, the footprint of a SM sensor formed of 12 CRNP mounted on to a mobile robotic platform (Thorvald) for better in‐field localization of moisture variation. Ultra Rapid Adaptable Neutron‐Only Simulation neutron scattering simulations are used to show that 5 cm of additional HDPE moderator (used to shield the upper surface and sides of the detector) is sufficient to (a) reduce the footprint of the detector considerably, (b) approximately double the percentage of neutrons detected from within 5 m of the detector, and (c) does not affect the shape of the curve used to convert neutron counts into SM. Simulation and rover measurements for a transect crossing between grass and concrete additionally suggest that (d) SM changes can be sensed over a length scales of tens of meters or less (roughly an order of magnitude smaller than commonly used footprint distances), and (e) the additional moderator does not reduce the detected neutron count rate (and hence increase noise) as much as might be expected given the extent of the additional moderator. The detector with additional HDPE moderator was also used to conduct measurements on a stubble field over three weeks to test the rover system in measuring spatial and temporal SM variation. Key Points: The possibility of using high density polyethylene moderator on a set of Cosmic‐Ray Neutron Probes (Rovers) to limit the field of view is exploredNeutron scattering simulations show that the moderator provides considerable control over the sensor's footprintThe Rover was used to conduct measurements on a stubble field to test the system in measuring spatial and temporal soil moisture variation [ABSTRACT FROM AUTHOR]

Availability of long-term and high-resolution measurements of soil moisture is crucial when it comes to understanding all sorts of changes to past soil moisture variations and the prediction of future dynamics. This is particularly true in a world struggling against climate change and its impacts on ecology and the economy. Feedback mechanisms between soil moisture dynamics and meteorological influences are key factors when it comes to understanding the occurrence of drought events. We used long-term high-resolution measurements of soil moisture on a large inclined lysimeter at a test site near Karlsruhe, Germany. The measurements indicate (i) a seasonal evaporation depth of over 2 m. Statistical analysis and linear regressions indicate (ii) a significant decrease in soil moisture levels over the past 2 decades. This decrease is most pronounced at the start and the end of the vegetation period. Furthermore, Bayesian change-point detection revealed (iii) that this decrease is not uniformly distributed over the complete observation period. The largest changes occur at tipping points during years of extreme drought, with significant changes to the subsequent soil moisture levels. This change affects not only the overall trend in soil moisture, but also the seasonal dynamics. A comparison to modeled data showed (iv) that the occurrence of deep desiccation is not merely dependent on the properties of the soil but is spatially heterogeneous. The study highlights the importance of soil moisture measurements for the understanding of moisture fluxes in the vadose zone. [ABSTRACT FROM AUTHOR]

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[Display omitted] • Swales can be surprisingly dry despite conventional expectations of their wetness. • Soil and vegetation characteristics strongly influence soil moisture variations. • Over 50% of soil moisture variations are explained by non-topographic factors. • A new index that integrates topographic and non-topographic information is proposed. • This index advances predictability of catchment-scale soil moisture variation. Concave hillslopes, also known as swales, play a pivotal role in facilitating the transfer of water and nutrients from hillslopes to riparian zones in headwater catchments by serving as pathways for surface and subsurface flow. Swales have long been recognized as hydrologic wet spots that contribute significantly to catchment runoff. However, long-term in situ observations have rarely confirmed this conventional understanding. In this study, we present a comprehensive analysis of three years of daily soil moisture measurements taken at multiple depths across 33 sites within a forested catchment to delineate key determinants of soil moisture variation at the catchment scale. Contrary to conventional understanding, our findings indicate that swales may represent areas of lower soil moisture, with those located on sunny hillslopes experiencing a higher frequency of dry conditions, amounting to 13.31% of the time during the study period, when compared to other topographical features such as slopes, valleys, and ridges. Topography alone accounted for less than half of the variation of soil moisture across the catchment. Whereas non-topographic factors (such as vegetation, soil porosity, soil temperature, and soil depth) strongly influenced soil moisture distribution in time and space. To better capture the nuances of soil moisture dynamics, we propose an enhancement to the Topographic Wetness Index (TWI) through the incorporation of non-topographic variables, which improves the model's representation of soil moisture variations. Our findings elucidate the complex interactions between topographic and non-topographic factors in shaping soil moisture dynamics at the catchment scale, offering valuable insights for refining process-based hydrological models and contributing to a deeper understanding of the spatial and temporal variability of soil moisture in headwater catchments. [ABSTRACT FROM AUTHOR]

• The THREW-T model produced good simulation on streamflow, snow, soil moisture, and isotope simultaneously in two basins. • Calibration toward isotope improved the simulations of internal streamflow and soil moisture's spatiotemporal variation. • Calibration toward isotope yielded the most reasonable runoff source apportionment, and reduced the uncertainty. Multiple-objective calibration helps constrain the parameter uncertainties and improve the performances of hydrological models. Previous studies have indicated that calibration toward soil moisture data could improve the streamflow simulation, but its influence on the runoff source apportionment quantification still needs to be analyzed. Meanwhile, although isotope calibration has proved to improve the representation of internal hydrological processes, the value of isotope on the simulation of internal state variables such as soil moisture has yet to be examined. This study utilized the tracer-aided hydrological model THREW-T (Tsinghua Representative Elementary Watershed – Tracer-aided version) in two mountainous basins on the Tibetan Plateau (The Upper Brahmaputra and Upper Yangtze basins) to evaluate the value of soil moisture and isotope data on model calibration. The result shows that: (1) The THREW-T model produced good simulation on streamflow, snow cover area, soil moisture, and stream water isotope simultaneously in the two study areas. Calibration toward soil moisture and isotope caused slight (∼0.03) but statistically significant (p < 0.01) decrease on the Nash-Sutcliffe coefficient of streamflow simulation compared to the baseline calibration variant only toward streamflow. (2) Calibration toward soil moisture brought no improvement to streamflow simulation for the validation period and stations in both basins, only improving soil moisture simulation. However, calibration toward the isotope improved the simulations of internal streamflow and soil moisture's spatiotemporal variation. (3) Different calibration variants resulted in different estimations of the runoff source apportionment, and independent evidence indicated that the results obtained by isotope calibration were most reasonable. Calibrations toward streamflow and soil moisture underestimated and overestimated the contributions from subsurface runoff, respectively. Isotope was the most sensitive objective to the runoff source apportionment and significantly reduced the uncertainty. Our study found a lower value of soil moisture data than the isotope on model calibration. However, we believe that the full potential of soil moisture data was not utilized due to the current limitations in soil moisture simulation and measurement methods, and the development of relevant technologies will make the soil moisture data more valuable for model calibration. [ABSTRACT FROM AUTHOR]

• Freeze-thaw cycles varied as a function of the extent of vegetation cover. • Migration of soil water was greatest with low vegetation cover. • Reallocation of soil water caused temperature in the soil complex to vary. • Freeze-thaw cycles promoted evaporation of soil water in the plant-rooting zone. Freeze-thaw events can lead to the upward migration of shallow groundwater in recharging the upper soil profile. However, their effects on soil water movement in areas of deep groundwater is not entirely known, especially in arid and semiarid regions of the world. This study analyzed the soil hydrothermal coupling at two desert sites in the Mu Us Sandy Land (northwest China), noted for their differences in surface scrub-vegetation cover, one with 80 % cover and another with 40 % (sites A and B, respectively). The work consisted of collecting soil temperature and moisture measurements at the two sites, both renowned for their deep watertable. The results showed that the freezing period at both sites was about 3–6 times longer than the thawing period, and that the duration of uniform freezing was inversely related to the degree in surface vegetation cover. During the two freezing periods of 2017–2019, as much as 3.87 (or 21.9 %) and 7.37 mm (42.2 %) of soil water was observed to have migrated upwards from the lower soil layers. Altogether, the total distance traveled by the soil water exceeded 180 cm. Also, about 33.2 (site A) and 22.9 mm of soil water (site B) were depleted because of evaporation in the first 185 cm of the soil complex during the two freeze–thaw events. Extent of vegetation cover, initial soil moisture profile, and soil texture influenced the quantity of water lost from individual soil layers due to evaporation and between soil-layer water exchanges. Soil water migration also affected changes in soil temperature in the range of −12.1 to 4.8 °C within the first 185 cm of the soil complex. Changes in soil temperature were responsible for decelerating freezing and thawing processes, prolonging soil water reallocation processes in the soil. Overall, freeze–thaw events increased the amount of shallow soil water losses due to evaporation, triggering a corresponding decline in soil water in the plant-rooting zone. This drop could conceivably lead to a reduction in sprouting and growth of spring vegetation, further degrading affected sand-dominated desertlands. This information may provide insight into the hydrological response of freeze–thaw events in arid regions, as well as provide useful information in guiding afforestation efforts in associated desertlands. [ABSTRACT FROM AUTHOR]

• Accurate soil moisture measurement is essential, but gravimetric method is impractical for real-time or large-scale studies. • Dielectric-based sensors require precise calibration. • The review evaluates calibration methods, including regression equations and neural networks, highlighting effectiveness and limitations. • Selecting the proper calibration method based on sensor type, soil, and environment is crucial for accurate data. Soil moisture, also referred to as soil water content, is a crucial geophysical state variable in various fields, including agriculture, hydrology, meteorology, and remote sensing calibration/validation (cal/val). Therefore, accurate measurements of in situ soil moisture are essential to ensure the improved modeling of geophysical processes, perform cal/val, and apply them in decision-making for societal benefits. There are many ways to measure soil moisture, though the gravimetric method of soil moisture measurement is the most accurate, however, it is not suitable for real-time or large-scale field studies due to labor- and time-intensive procedures. In other words, the gravimetric method-based soil moisture measurements are not very feasible for hydrological or agricultural applications where a large number of in-situ observations are needed frequently as well as for validation of the remote sensing-based products that require sampling in a narrow time window during satellite passes. Hence, alternative modern methods like electronic sensors that measure soil moisture instantly are necessary for such applications. Since soil moisture sensors usually operate on the principle of dielectric characterization of the soil and water and use conversion relationships to provide soil moisture, these sensors require appropriate calibration for precise measurements. Although various procedures for calibrating dielectric soil moisture sensors are available in the scientific literature, factors affecting calibration, the technical complexity of calibration, and lack of confidence in their effectiveness limits their use in field studies. In this context, a review of soil moisture sensor calibration protocols has been conducted with the aim of examining the effectiveness and limitations of various calibration methods and approaches for dielectric permittivity-based soil moisture sensors. The importance of selecting an appropriate calibration method based on the sensor type, soil type, and environmental conditions is highlighted in this review. We also examine various calibration methods, including traditional equations based on linear or polynomial regression and advanced methods such as the nonlinear approach (i.e., artificial neural network). The need for regular calibration and maintenance of soil moisture sensors to ensure accurate and reliable data is also emphasized. The review concludes by providing recommendations for selecting the appropriate calibration method/approach for dielectric soil moisture sensors to achieve the required accuracy and future directions. Overall, this review provides valuable insights for researchers and practitioners in the field of dielectric sensor-based soil moisture monitoring and management. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the study and method for measuring the electrical properties of the agricultural soil using a wireless sensor network to counter the dynamic changes in electrical properties of soils under variable rainfall and flood conditions. Soil moisture and nutrients can be correlated with the resistance of soil, which is the essential electrical property of the soil. Resistivity can help us to classify different agricultural soil types and can be used for real‐time monitoring of crop fields. The diverse resistivity soil patterns may help to classify the physical, chemical and biological properties of soil. In our research work, we have taken soil from different districts of Madhya Pradesh state and classified it based on resistivity. We have also designed a low‐cost resistivity sensor module with a mathematical justification of the results. The new sensor module has been tested on different soil samples of Madhya Pradesh, and wireless sensor network (WSN) has been proposed to collect and process the data for application in the agriculture fields. The paper also describes the challenges and the various methodologies for the measurement of soil resistivity and moisture using the Internet of Things (IoT) architecture. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate in the papaya Tainung genotype, the effects of partial root-zone drying (PRD) technique on soil water regimes by using different frequencies of shifting irrigation-side of plant row and the effects of PRD technique on (1) crop agronomic performance, (2) titratable fruit acidity (TA), (3) total soluble solids (TSS), and TSS/TA ratio. Also, we analyze the spatial dynamic of papaya condition using normalized difference vegetation index (NDVI) from different satellite images. The study was conducted in the semi-arid region of Bahia (BA) and Minas Gerais (MG), Brazil. The combination of 100% (Full irrigation—FU), 50%, and 35% in the irrigation depth (WID) and frequencies of shifting plant-row side irrigation of 0 (Fixed Irrigation—FX), 7, 14, and 21 days were applied. Nine treatments were studied in BA and five in MG. The water available in the soil was reduced to 44% for frequencies of shifting plant-row side irrigation of 7 days, 50% for 14 days, and 85% for 21 days, compared to the soil water availability at field capacity. Partial water deficit in the soil through the PRD technique did not significantly reduce the total root length, effective root depth, and root effective horizontal distance of the papaya Tainung genotype. However, PRD treatments showed leaf abscission, which resulted in reduced leaf area and NDVI values, especially in the MG experiment. Papaya yield and fruit quality were not affected. However, except for PRD 21 35%, irrigation water depth reduced to 50 and 35% under PRD increased crop water productivity (CWP) in papaya plants. Thus, the PRD technique may save 35% of WID using the alternation of lateral shift irrigation of crop row every 7 days under water scarcity in semi-arid regions. The NDVI index was important to compare the papaya canopy vigor between the experimental areas studied. We also confirmed the potential of NDVI to monitor the vigor of papaya canopy, since we could notice the sensibility of NDVI to identify water stress in papaya in higher vapor pressure deficit (VPD) conditions occurred in October 2016 and January 2017 in Bom Jesus da Lapa-BA. Therefore, the PRD strategy can be a useful tool to save water in papaya cultivation under semi-arid conditions. [ABSTRACT FROM AUTHOR]

Sensitivity to water availability is a key physiological trait for grassland species located in arid and semiarid environments, where successful germination is closely related to rainfall dynamics. Festuca pallescens inhabits diverse environments along a steep precipitation gradient in North Patagonia, thus offering a suitable model for the study of germination behavior in response to water availability. By analyzing germination in nine populations using a hydrotime model approach, we aimed to find within-species variation. Seed population behavior was analyzed under different hydric conditions using hydrotime model parameters (hydrotime, mean base water potential and its standard deviation). We estimated the mean base water potential for F. pallescens (ψb(50) = − 2.79 ± 0.45 MPa), which did not differ significantly between populations. However, the hydrotime parameter (θH) varied markedly, suggesting physiological adaptation to local environments. Higher values of θH were found in populations located at the extremes of the distribution gradient, indicating that germination may be prevented or delayed in conditions that are suboptimal for the species. Since the variation in hydrotime model parameters did not follow a cline, micro-environmental cues may have the greatest influence on the physiological behavior of the species, rather than the macroscale rainfall gradient. [ABSTRACT FROM AUTHOR]

ERA5 reanalysis data shows significant correlation between Siberian snow depth in March and following Indian summer monsoon rainfall (ISMR). Whilst the Siberian snow depth is negatively correlated with seasonal monsoon rainfall over most of India, it is positively correlated with the monsoon rain over north‐east Indian regions. This relationship has significantly strengthened during the past 2 decades. We show that the strengthening relationship is more likely related to recent atmospheric circulation changes owing to persistent global warming, particularly the changes in the atmospheric circulation over the North Atlantic. There are indications that during 1999–2018, the extratropical climatic variability (e.g., Arctic Oscillation [AO] and North Atlantic Oscillation [NAO]) significantly influenced Siberian snow in the month of March. In contrast, during the period 1979–1998, the influence of NAO and AO on Siberian snow was negligible. Analysis indicates that Siberian High, a dominant atmospheric circulation system that exerts strong influence on Eurasian weather and climate, has also significantly weakened during the past 2 decades. We hypothesize that the March Siberian snow influences the ISMR through a delayed hydrological response, in which the increased (reduced) accumulated snow during springtime provides wetter (drier) soil during the following summer. This leads to the alternation of the meridional tropospheric temperature gradient. The heavy (less) spring snowfall in north (south) Siberia led to changes in summertime meridional tropospheric temperature gradient, which resulted in the weakening of subtropical westerly jet and Tibetan anticyclone. The reduced intensity of tropical easterly jet connected to Tibetan anticyclone resulted in the weakening of summer monsoon Hadley cell, leading to subsequent suppression of rainfall. This association between Siberian snow and ISMR provides seasonal prediction potential. Key Points: In the backdrop of global warming, relationship between Siberian snow and Indian summer monsoon rainfall (ISMR) has strengthened during past 2 decadesThe association between the Siberian snow and the atmospheric circulation, particularly North Atlantic Oscillation has become more pronounced in recent decadesSiberian snow influences the ISMR via delayed hydrological response [ABSTRACT FROM AUTHOR]

The water balance is an essential tool for hydrologic studies and quantifying water‐balance components is the focus of many research catchments. A fundamental question remains regarding the appropriateness of water‐balance closure assumptions when not all components are available. In this study, we leverage in‐situ measurements of water fluxes and storage from the Southern Sierra Critical Zone Observatory (SSCZO) and the Kings River Experimental Watersheds (KREW) to investigate annual water‐balance closure errors across large (1016–5389 km2) river basins and small (0.5–5 km2) headwater‐catchment scales in the southern Sierra Nevada. The results showed that while long‐term water balance in river basins can be closed within 10% of precipitation, in the smaller headwater catchments as much as a quarter of precipitation remained unaccounted for. A detailed diagnosis of this water‐balance closure error using distributed soil moisture measurements in the top 1 m suggests an unaccounted deeper storage and a net groundwater export from the headwater catchments. This imbalance was also found to be very sensitive to the timescales over which water‐balance closures were attempted. While some of the closure errors in the simple water balance can be attributed to measurement uncertainties, we argue for a broader consideration of groundwater exchange when evaluating hydrological processes at headwater scales, as the assumption of negligible net groundwater exchange may lead to an overestimation of fluxes derived from the water balance method. [ABSTRACT FROM AUTHOR]

Understanding the potential impacts of climate variability/change on soil moisture is essential for the development of informed adaptation strategies. However, long-term in-situ soil moisture measurements are sparse in most countries. The objectives of this study were to develop and analyse the temporal variability of a long-term soil moisture data set in South Africa. In this study, a water balance model was used to reconstruct long-term soil moisture data sets from 1980 through 2018, in three sites that represent the diverse agroclimatic conditions of South Africa. Additionally, long-term changes and variability of soil moisture were examined to investigate the potential impacts of climate variability on soil moisture. The results of the Mann-Kendall test showed a non-significant decreasing trend of soil moisture for inland stations at a rate between -0.001 and -0.02 mm per annum. In contrast, a statistically significant (at 5% level of significance) increasing trend of soil moisture for a coastal station at a rate of 0.1131 mm per annum was observed. The findings suggest that the Bainsvlei and Bronkhorstspruit stations located in the inland region are gradually becoming drier as a result of decreasing rainfall and increasing air temperature. In contrast, the Mandeni station located in the coastal region is becoming wetter as a result of increasing rainfall, despite the increase in air temperature. The findings indicate that climate variability is likely to change the soil moisture content, although the influence will vary with region and climatic conditions. Therefore, understanding the factors that affect soil moisture variability at the local scale is critical for the development of informed and effective adaptation strategies. Significance: • Long-term modelled estimates were used to investigate the potential impacts of climate variability on soil moisture in three different agroclimatic conditions of South Africa. • Results show that inland regions are gradually becoming drier as a result of decreasing trends of rainfall and increasing air temperatures while coastal regions are becoming wetter as a result of increasing trends of rainfall. • This study indicates that climate variability is likely to change soil moisture, although various regions will be affected differently. • The development of informed adaptation strategies at the local scale is critical to cope effectively with climate variability. [ABSTRACT FROM AUTHOR]

A persistent challenge in the measurement of soil moisture from satellites stems from the inherently low spatial resolution using active or passive system. The lobe differencing correlation radiometer (LDCR) on a small unmanned aerial system (sUAS) is shown to provide a capability to measure soil moisture at high spatial resolution for a range of scientific and operational purposes. Flight tests of LDCR on a fixed wing sUAS were performed at the Canton, Oklahoma Soilscape site in September 2015, and Irrigation Research Foundation (IRF) in Yuma, Colorado, in June 2016. The LDCR design and performance are discussed, and the calibration using both preflight lab test data and in-flight data over a calm pond was performed to calibrate the radiometer. Radio frequency interference (RFI) from the sUAS platform was observed and mitigated. The LDCR sampling processes are detailed and an implementation of the τ-ω vegetation correction model along with a semiempirical surface roughness correction model incorporating a full-domain soil moisture mapping algorithm is presented. The algorithm uses a weakly nonlinear observation operator suitable for irregular sUAS flight trajectories that maps volumetric soil moisture (VSM) on a user-defined product grid from the sUAS sampling grid. Using LDCR radiometric and thermal measurements, along with Landsat-based vegetation water content (VWC) and soil texture information, soil moisture was mapped at decameter spatial resolution. The retrieved VSM data are favorably compared with in situ VSM measurements and irrigation records. A method for determining LDCR VSM estimation errors is developed to quantify the mapping algorithm accuracy and assess the impact of error sources. [ABSTRACT FROM AUTHOR]

Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections. [ABSTRACT FROM AUTHOR]

The instrumental monitoring of torrential catchments is a fundamental research task that provides necessary information to improve our understanding of the mechanisms of debris flows. While most monitoring sites include meteorological sensors and analyze the critical rainfall conditions, very few contain soil moisture measurements. In our monitoring site, the Rebaixader catchment, 11 debris flows and 24 debris floods were detected during the last 9 years. Herein, the initiation mechanisms of these torrential flows were analyzed, focusing on the critical rainfall conditions and the soil water dynamics. Comparing the temporal distribution of both rainfall episodes and torrential flows, the Kernel density plots showed maximum values for rainfalls at the beginning of June, while the peak for torrential flows is on July 20. Thus, the antecedent rainfall, and especially the soil moisture conditions, may influence the triggering of torrential flows. In a second step, a new updated rainfall threshold was proposed that included total rainfall duration and mean intensity. The analysis of soil moisture data was more complicated, and no clear trends were observed in the data set. Therefore, additional data have to be recorded in order to quantitatively analyze the role of soil moisture on the triggering of torrential flows and for the definition of thresholds. Some preliminary results show that the soil moisture at the beginning of a rainfall event affects the maximum increase of soil moisture, while a slight trend was visible comparing the initial soil moisture with the necessary rainfall amount to trigger a torrential flow. [ABSTRACT FROM AUTHOR]

Soil water content (SWC, % vol) is a key factor affecting plant growth and development. SWC measurement is vital to rational use of water resources for irrigation, and the accuracy of sensors in SWC measurement is of significant importance to smart data-driven irrigation. Here, a laboratory experiment and a field lysimetric experiment were conducted to evaluate the accuracy of Insentek sensors under various soil conditions (1.1 to 1.5 bulk densities and sand to clay soil textures) and irrigation levels (30, 45, and 60 mm), in 2018 and 2019. A microweighing lysimeter and oven-drying method were used as standard methods to compare the Insentek method. The root mean square error (RMSE, % vol) and relative prediction deviation (RPD) between the Insentek and microlysimetric SWC values were 0.89–1.04% vol and 5.6–6.8, respectively, under laboratory condition. The RPD value is larger than the threshold value of 4.0, indicating the accuracy of the Insentek sensors is reliable under laboratory condition. Except for 60 mm irrigation treatment, the RMSE between Insentek and the oven-drying method under field condition was 1.44–1.93% vol, and the RPD value was 1.56–1.93, lower than the threshold value of 4.0. The tiny gap between the Insentek sensor and soil may accelerate water infiltration along the probe 0-3 d after irrigation while increase air filling 5–7 d after irrigation, causing greater RMSE and lower RPD values. The dissatisfied performance in field condition may also be associated with the obvious drawbacks of oven-drying method, such as disturbance in soil sampling. When using oven-drying method to analyze the accuracy of the Insentek sensors in field condition, the concerns should be well addressed. [ABSTRACT FROM AUTHOR]

Soil moisture is a crucial factor limiting the growth and survival of plants on the Loess Plateau. Its level has a severe impact on plants' growth and development and the type and distribution characteristics of communities. This study area is the Jihe Basin in the Loess Plateau, China. Multiple linear regression models with different environmental variables (land use, topographic and meteorological factors, etc.) were developed to simulate soil moisture's spatial and temporal changes by integrating field experiments, indoor analysis, and GIS spatial analysis. The model performances were evaluated in the Jihe Basin, with soil moisture content measurements. The result shows that soil moisture content is positively correlated with soil bulk density, monthly rainfall, topographic wetness index, land use coefficient, and slope aspect coefficient but negatively correlated with the monthly-averaged temperature and the relative elevation coefficient. The selected variables are all related to the soil moisture content and can account for 75% of the variations of soil moisture content, and the remaining 25% of the variations are related to other factors. Comparing the simulated and measured values at all sampling points shows that the average error of all the simulated values is 0.09, indicating that the simulation has high accuracy. The spatial distribution of soil moisture content is significantly affected by land use and topographic factors, and seasonal variation is remarkable in the year. Seasonal variation of soil moisture content is determined by the seasonal variation of rainfall and the air temperature (determining evaporation) and vegetation growth cycle. Therefore, the proposed model can simulate the spatial and temporal variation of soil moisture content and support developing the soil and water loss model on a basin scale. [ABSTRACT FROM AUTHOR]

Water fluxes at the soil–atmosphere interface are a key piece of information for studying the terrestrial water cycle. However, measuring and modeling water fluxes in the vadose zone poses great challenges. While direct measurements require costly lysimeters, common soil hydrologic models rely on a correct parametrization, a correct representation of the involved processes, and the selection of correct initial and boundary conditions. In contrast to lysimeter measurements, soil moisture measurements are relatively cheap and easy to perform. Using such measurements, data-driven approaches offer the possibility to derive water fluxes directly. Here we present FluSM (fluxes from soil moisture measurements), which is a simple, parsimonious and robust data-driven water balancing framework. FluSM requires only a single input parameter (the infiltration rate) and is especially valuable for cases where the application of Richards-based models is critical. Since permeable pavements (PPs) present such a case, we apply FluSM on a recently published soil moisture data set to obtain the water balance of 15 different PPs over a period of 2 years. Consistent with findings from previous studies, our results show that vertical drainage dominates the water balance of PPs, while surface runoff plays only a minor role. An additional uncertainty analysis demonstrates the ability of the FluSM-approach for water balance studies, since input and parameter uncertainties only have a small effect on the characteristics of the derived water balances. Due to the lack of data on the hydrologic behavior of PPs under field conditions, our results are of special interest for urban hydrology. [ABSTRACT FROM AUTHOR]

Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future. [ABSTRACT FROM AUTHOR]

This research describes the use of an optical system combined with artificial neural network (ANN) for wireless and nondestructive prediction of soil moisture level. The former system comprising of near infrared (NIR) emitters of wavelengths 1200 nm and 1450 nm, and a photodetector for near real time soil moisture measurement in loams and peats holding different amount of water. There were 63 and 90 sets of data from loams and peats, respectively, used in the development of the dual stage-multiclass ANN model, wherein measurement of light attenuation (from nondestructive system) was correlated with percent soil moisture (from destructive gold standard approach) in pre-measurement stage. The result revealed a relatively good performance in the training of the NN with regression, R, of 0.8817 and 0.8881, and satisfactory error performance of 0.7898 and 1.172, for loams and peats, respectively. The testing of the system on 50 new samples of loam and peat showed a considerably high mean accuracy of 92 % for loams while 82 % was observed for peats. This study attributes the poorer performance of the system used on peats to the detection resolution of percent soil moisture, and structure and properties of the corresponding soil. This work concluded that the developed technology may be feasible for use in the future design and improvement of agricultural soil management. [ABSTRACT FROM AUTHOR]

Boreal forest soils are globally an important sink for methane (CH 4), while these soils are also capable of emitting CH 4 under favourable conditions. Soil wetness is a well-known driver of CH 4 flux, and the wetness can be estimated with several terrain indices developed for the purpose. The aim of this study was to quantify the spatial variability of the forest floor CH 4 flux with a topography-based upscaling method connecting the flux with its driving factors. We conducted spatially extensive forest floor CH 4 flux and soil moisture measurements, complemented by ground vegetation classification, in a boreal pine forest. We then modelled the soil moisture with a random forest model using digital-elevation-model-derived topographic indices, based on which we upscaled the forest floor CH 4 flux. The modelling was performed for two seasons: May–July and August–October. Additionally, we evaluated the number of flux measurement points needed to get an accurate estimate of the flux at the whole study site merely by averaging. Our results demonstrate high spatial heterogeneity in the forest floor CH 4 flux resulting from the soil moisture variability as well as from the related ground vegetation. The mean measured CH 4 flux at the sample points was - 5.07 µ mol m -2 h -1 in May–July and - 8.67 µ mol m -2 h -1 in August–October, while the modelled flux for the whole area was - 7.42 and - 9.91 µ mol m -2 h -1 for the two seasons, respectively. The spatial variability in the soil moisture and consequently in the CH 4 flux was higher in the early summer (modelled range from - 12.3 to 6.19 µ mol m -2 h -1) compared to the autumn period (range from - 14.6 to - 2.12 µ mol m -2 h -1), and overall the CH 4 uptake rate was higher in autumn compared to early summer. In the early summer there were patches emitting high amounts of CH 4 ; however, these wet patches got drier and smaller in size towards the autumn, changing their dynamics to CH 4 uptake. The mean values of the measured and modelled CH 4 fluxes for the sample point locations were similar, indicating that the model was able to reproduce the results. For the whole site, upscaling predicted stronger CH 4 uptake compared to simply averaging over the sample points. The results highlight the small-scale spatial variability of the boreal forest floor CH 4 flux and the importance of soil chamber placement in order to obtain spatially representative CH 4 flux results. To predict the CH 4 fluxes over large areas more reliably, the locations of the sample points should be selected based on the spatial variability of the driving parameters, in addition to linking the measured fluxes with the parameters. [ABSTRACT FROM AUTHOR]

This paper concerns the simulation of the water table elevation in shallow unconfined aquifers where infiltration is assumed as the main mechanism of recharge. The main aim is to provide a reliable tool for groundwater management that satisfies water supply managers. Such a tool is a candidate as a physically based alternative to the use of empirical methods or general circulation models. It is based on the use of two widely available sets of data: the water table elevation measurements and soil moisture time series. In fact, the former are usually provided by government agencies on public websites whereas the latter are included in the atmospheric global datasets (reanalysis). It is notable that data from reanalysis are accessible to any citizen and organization around the world on an open-access basis (e.g., Copernicus). In the proposed method, the measured water table elevations are correlated quantitatively with the water fluxes toward the aquifer evaluated using the soil moisture data from ERA5 reanalysis (provided by ECMWF) within a Richards equation-based approach. The analysis is executed using data from the Umbria region (Italy) on both a daily and monthly scale. In fact, these are the time intervals of interest for a proper management of groundwater resources. The proposed relationships include both a logarithmic and linear term and point out the possible different regimes of the shallow aquifers with regard to the recharge due to infiltration. These different mechanisms reflect in the different role played by the water fluxes toward the aquifer in terms of water table elevation changes according to the considered time scale. [ABSTRACT FROM AUTHOR]

Regional-scale precise soil moisture measurements are required for remote sensing-based soil moisture product validation besides, complimenting in several hydrological and agricultural applications. Though the gravimetric method provides the most accurate soil moisture measurements, it cannot be extended to the regional-scale due to the large number of sampling requirements. An impedance probe is a suitable substitute for the time-intensive gravimetric method; however, it needs soil/field-specific calibrations for precise measurements. The present study aims to develop a generalized calibration of an impedance probe (i.e., ThetaProbe) for precise measurements of soil moisture at the regional-scale within the root-mean-square-error (RMSE) of 0.04 m3 m−3 to fulfil the accuracy requirement of current satellite missions. A few methods for calibrating impedance probe were investigated using 496 gravimetric samples and coincident impedance probe measurements collected over 83 locations through field campaigns in a paddy dominated tropical Indian watershed that covers an area of 500 km2. The manufacturer generalized calibration was found to have high RMSE (0.0523 m3 m−3) and considerable bias (0.0241 m3 m−3) in soil moisture measurements. Developed generalized and soil-specific calibration based on a linear regression technique that resulted in RMSE values of 0.0468 and 0.0422 m3 m−3 , respectively. Further, a Bayesian neural network (BNN) based method, a nonlinear technique, was used for developing a generalized calibration of the impedance probe. The results illustrated that BNN-based generalized calibration (RMSE<0.04 m3 m−3) performs better than the linear regression–based calibrations (RMSE>0.04 m3 m−3). Moreover, the performance of BNN-based generalized calibration was further improved by the inclusion of soil physical properties as input and yielded an RMSE value up to 0.0352 and 0.0366 m3 m−3 during training and cross-validation process, respectively. [ABSTRACT FROM AUTHOR]

The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements. [ABSTRACT FROM AUTHOR]

The effect of clearfell harvesting on soil greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) was assessed in a Sitka spruce forest growing on a peaty gley organo-mineral soil in northern England. Fluxes from the soil and litter layer were measured monthly by the closed chamber method and gas chromatography over four years in two mature stands, with one area harvested after the first year. Concurrent measurements of soil temperature and moisture helped to elucidate reasons for the changes in fluxes. In the three years after felling, there was a significant increase in the soil temperature, particularly between June and November (3 to 5 °C higher), and in soil moisture which was 62 % higher in the felled area, and these had pronounced effects on the GHG balance, in addition to the removal of the trees and their carbon input to the soil. Annual soil CO2 effluxes reduced to almost a third in the first year after felling (a drop from 24.0 to 8.9 t CO2 ha-1 yr-1) and half in the second and third year (mean 11.8 t CO2 ha-1 yr-1) compared to before felling, while those from the unfelled area were little changed. Annual effluxes of N2O more than doubled in the first two years (from 1.0 to 2.3 and 2.5 t CO2e ha-1 yr-1, respectively), although by the third year they were only 20 % higher (1.2 t CO2e ha-1 yr-1). CH4 fluxes changed from a small net uptake of -0.03 t CO2e ha-1 yr-1 before felling to a small efflux increasing over the 3 years to 0.34 t CO2e ha-1 yr-1, presumably because of the wetter soil after felling. Soil CO2 effluxes dominated the total net GHG emission calculated using the global warming potential (GWP) of the three gases, but N2O contributed up to 20 % of the total annual emissions. This study showed fluxes of CO2, CH4 and N2O responded differently to clearfelling due to the significant changes in soil biotic and abiotic factors and showed large variations between years. This demonstrates the need for multi-year measurements of all GHGs to enable a robust estimate of the effect of the clearfell phase on the GHG balance of managed forests. This is one of a very few multi-year monitoring studies to assess the effect of clearfell harvesting on soil GHG fluxes. [ABSTRACT FROM AUTHOR]

Crop water stress monitoring represents a fundamental step in agricultural production. In order to increase water savings and enhance agricultural sustainability, implementation of suitable irrigation scheduling methods is essential, and requires early detection of water stress in crops, before it causes irreversible damage and yield loss. There are different methods to measure water stress, some of them are based on soil moisture measurements while others are based on calculations of vegetation indices, evapotranspiration or soil water balance. Currently, the use of remote sensing technologies for the analysis of plant water status comprises a wide range of available methods such as infrared thermometry for canopy temperature measures, microwave radiation for soil water content assessment, and spectral vegetation indices for the study of the reflectance responses of canopies to different environmental conditions. The aim of the presented work is to investigate the applicability of the optical trapezoid model (OPtical TRApezoid Model) in mapping the moisture content within agricultural field. The model ability to provide vegetation characteristics, and crop water status at the canopy scale can improve the site-specific decision-making process in a precision agriculture. [ABSTRACT FROM AUTHOR]

In this work, we authors report the investigation of polyvinyl alcohol (PVA) modified poly(3,4-ethylene-dioxythiophene)-poly(styrene sulfonate) (PEDOT-PSS): titanium dioxide (TiO2) nanocomposite sensors towards relative humidity and soil moisture measurements at room temperature for agricultural applications. The morphological and structural features of the nanocomposite films were studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infra-red spectroscopy (FTIR) and theromogravimetric analysis (TGA). These characterizations reveal the successful formation of nanocomposite structures with improved thermal stability. The conductivity of nanocomposite film shows significant improvement compared to pure PEDOT-PSS by three orders of magnitude. Sensitive layer of PEDOT-PSS:PVA doped TiO2 thin films were fabricated on ITO substrate using the spin coating technique to study relative humidity and soil moisture response. The nanocomposite film was subjected to varying levels of relative humidity (%RH) ranging from 5 to 95% and varied moisture content in soils of Montmorillonite and Kaolinite at room temperature. The resistance was found to decrease with increasing RH and gravimetric moisture content in Montmorillonite and Kaolinite soils. The humidity sensing response of PEDOT-PSS: PVA doped TiO2 (8 wt%) composite thin film shows higher sensitivity of 96% with fast response and recovery times of 10 and 50 s respectively. Further, the sensitivities of 10.5 and 8.32 kΩ with 0.2% variation in gravimetric water content were achieved upon exposure of the prepared sensor to red and black soils respectively. Due to being relatively inexpensive, simple fabrication technique, excellent sensitivity, good repeatability and reproducibility these PVA modified PEDOT-PSS: TiO2 nanocomposites sensors could be potentially useful in quantification of soil moisture content and relative humidity for agricultural applications. [ABSTRACT FROM AUTHOR]

Flash drought is one kind of extreme hydro-meteorological events that frequently occur worldwide in recent years. Different from traditional slowly-evolving droughts, flash droughts unusually have rapid onset, threatening food and agricultural security, and the health of eco-environment. To compare the spatio-temporal characteristics of flash drought and slowly evolving drought, the soil moisture data were extracted from the ERA-Interim reanalysis product (at a depth of 0-100 cm) with a spatial resolution of 0.25°and a temporal coverage from 1979 to 2018. Records of the soil moisture data were converted into weekly values. Seven candidate probability distribution functions (including Gamma, Beta, logarithmic, logical logarithmic, Weibull, extreme value, and generalized extreme value distributions) were employed to fit the weekly data series. The optimal probability distribution function for calculating soil moisture percentile was selected according to the Kolmogorov-Smirnov test (KS test) and two evaluation coefficients, i.e., the root mean square error and bias. For drought event identification, the 40% and 20% of soil moisture percentile were used as the upper limit and the lower limit, respectively, to ensure the identified drought events really falling into drought associated with vegetation stress. The onset duration was determined as the period when soil moisture percentile declines persistently and maintains a rather low value. The differences between flash droughts and slowly-evolving droughts in terms of drought frequency and drought area were analyzed. The relationships between flash droughts and slowly-evolving droughts were investigated based on the spatio-temporal patterns of two typical drought events. The results showed that droughts with long duration (more than 26 weeks) were more frequent in the north of the Yangtze River. In contrast, the south of the Yangtze River suffered few droughts and short-term drought. Different from drought events with short duration, the time series of affected area percentage for drought events with long duration (26 weeks or longer) presented significant increments from 2005 to 2015, especially in 2010 when more than 40% of the area suffered drought, suggesting a general trend of droughts with long duration and enlarged area extent. During the drought onset stage, the duration of drought of the south region was shorter than that of the north region. Typical flash drought events occurred in the middle and eastern part of Inner Mongolia, southwest China, the upper reaches of the Pearl River, and lower reaches of the Yangtze River, while slowly-evolving droughts happened mainly in the northwest China, and some areas in Xinjiang. The area percentage of flash drought was comparable to that of the slowly-evolving drought before 2000, after that, the affected area of slowly-evolving drought expanded significantly up to twice that of flash drought. The spatio-temporal migration paths of the 2010 autumn drought and the 2017 summer drought showed that the flash drought and slowly-evolving drought may develop independently, and may also present a co-existing relationship. Finally, the in-situ soil moisture measurements from the International Soil Moisture Network (ISMN) were employed for validation of the results. The spatial pattern of moisture was similar with that of the ERA-Interim reanalysis product. For example, soil moisture in humid and semi-humid zones decreased much quickly than in arid and semi-arid zones during a drought episode. This may be related to the climate condition, soils, and vegetation characteristics of the underlying surface. The soil moisture data of the ERA-Interim reanalysis product is capable of capturing the moisture dynamics during the onset stage of drought, and could be an alternative for flash drought monitoring and assessments. The results can provide some important information for understanding the mechanisms of drought and the development of drought monitoring techniques. [ABSTRACT FROM AUTHOR]