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The filter paper (FP) method, a cost-effective, versatile, and convenient approach, has found extensive application in analyzing fine-grained soils, enabling the successful measurement of a broad spectrum of total and matric suctions. However, using the FP method in sand with a limited range of suction profiles has been notably underrepresented in the existing literature. Therefore, this study investigated the applicability of the contact FP method in establishing soil water retention curves (SWRCs) using five distinct FP calibration curves for 10 poorly graded sands. Among these calibration curves, the one presented by Chandler yielded the highest suction values, which were followed by those of ASTM. Furthermore, the FP results were subjected to fitting with a mathmatical model, which led to the observation that the overall trends in the obtained SWRCs displayed smooth sigmoidal shapes, resulting in a complete and coherent SWRCs. The SWRC results of five sand samples were assessed through validation against the hanging column technique (HCT) results, indicating a high degree of concordance and affirming the suitability of the FP method for sands characterized by a limited suction profile. Notably, the entire range of suction values fell within the 0.01–20 kPa range based on the calibration curves presented by Chandler and ASTM for all the examined sand samples, except sands 9 and 10. Furthermore, the suction values were unobtainable using the two aforementioned calibration curves within the ranges of 0.01–1.47 kPa and 0.01–0.172 kPa. [ABSTRACT FROM AUTHOR]

Mulch has been considered effective for saving water and promoting plant growth. However, little has been investigated about the effect of mulch from recycled shredded paper under different soil moisture conditions on spinach growth. A pot-grown Komatsuna spinach experiment with a factorial design was conducted under two main conditions, with and without shredded paper mulch. Each of these conditions was treated with three different soil moisture levels (SWC) 20%, 25%, and 30%. The smallest evapotranspiration amount was in the mulch-only, no-plant treatment with SWC 20% (92.88 mm), and the largest was in the plant-only, no-mulch treatment (226.19 mm). All biomass yield attributions were negatively influenced by increasing the frequency of irrigation and SWC levels. SWC 20% with shredded mulch resulted in the largest dry matter, although it was statistically non-significant. But it resulted in taller plant heights and a larger leaf area index (LAI) compared to soil with no shredded mulch (p < 0.05). For SWC 25% and 30% treatments, Komatsuna spinach with no shredded mulch resulted in slightly superior plant dry matter and plant height, compared to mulched plants. These results suggest that SWC 20% with shredded paper mulch has the highest potential for saving water among all treatments for growing spinach under limited water availability. [ABSTRACT FROM AUTHOR]

To investigate the soil hydraulic characteristics across a wide range of suctions (0-10 MPa) of Benggang soil, the soil water holding capacities of different soil layers in Benggang was determined using the pressure film meter and the filter paper methods. Traditional and modified Van Genuchten-Mualem (VG) models were used to fit the soil moisture characteristics curves. The results showed that the water-holding curves determined by the pressure film meter and filter paper methods intersected and tended to overlap. This observation suggested combining the two measurement methods to obtain soil moisture characteristic curves over a wide suction range. Further, within the wide suction range, the water-holding capacity of the sandy soil layer was higher than that of the red and detritus soil layers in the low suction stage (h<0.1 MPa). However, in the high suction stage (h>0.1 MPa), the red soil layer had the highest water-holding capacity. Both the traditional and modified VG models were able to fit the soil moisture characteristics of each soil layer of the Benggang wall with a root mean square error of less than 0.021 5. The Nash efficiency and correlation coefficients were greater than 0.926 6 and 0.899 1, respectively. Noticeably, the modified VG model was more suitable for describing the soil moisture characteristic curves of the Benggang wall across a wide suction range. The residual values of the soil moisture characteristic curves for the red, sandy, and detritus layers were 378 kPa (corresponding to a water content of 18.82%), 173 kPa (corresponding to a water content of 18.95%), and 92 kPa (corresponding to a water content of 18.41%), respectively. Finally, the form of the segmental function (with the residual value as the inflection point) was more suitable for describing the unsaturated hydraulic conductivity over a wide suction range of Benggang soils than the conventional VG model. [ABSTRACT FROM AUTHOR]

Numerous recent studies have argued that moisture limitation is leading to growth declines and mortality of black spruce (Picea mariana) and white spruce (Picea glauca) in the North American boreal forest. A parallel line of research suggests that increasingly common severe wildfires are altering successional pathways and leading to long‐term replacement of spruce forests with those dominated by paper birch (Betula papyrifera, Betula neoalaskana) and trembling aspen (Populus tremuloides). When both conifers and deciduous trees establish after fire, this biome shift hypothesis implicitly assumes that deciduous species will outcompete the conifers, owing to their more rapid vertical growth and because they might be less sensitive to warm and dry conditions.We established a research site in a white spruce‐paper birch forest on an east‐facing slope in interior Alaska and tested the hypothesis that Alaska paper birch are better adapted to warm and dry conditions than white spruce. Over 6 years (2013–2018), we made hourly measurements of microclimate and xylem sap flux of both species. We also collected increment cores and conducted climate‐growth analyses for both species.During our 6‐year study, growing seasons with low volumetric soil water content (SWC) were those that followed shallow winter snowpacks and had limited summer rainfall, not necessarily those with warm air temperature. Both species were sensitive to moisture limitation. The tree‐ring data revealed significant positive effects of cumulative water year precipitation on radial growth, with a steeper slope for paper birch than for white spruce. Radial growth of both species was also positively related to mean water year air temperature. Sap flux density declined progressively for white spruce over the range of observed SWC and abruptly for paper birch when SWC fell below ~15%.Synthesis. Our results show that, while paper birch might be less sensitive to mild drought than white spruce, it may be more sensitive to severe drought, raising questions about the ability of paper birch to outcompete co‐occurring white spruce in a drier climate. [ABSTRACT FROM AUTHOR]

A variety of routine methods are available for the detection of silver (I) (Ag+) ions, but most of them rely on expensive, sophisticated and desktop instruments. Herein, a low-cost, instrument-free and portable Ag+ biosensor was described by initially designing a new class of 3D origami microfluidic paper-based analytical devices (μPADs) into each of which one piece of reagent-loaded nanoporous membrane was integrated. It combines analyte-triggered self-growing of silver nanoparticles to block the membrane's pores in situ for rapid yet efficient signal amplification with a handheld personal glucose meter for a portable and sensitive quantitative readout based on the biocatalytic reactions between the glucose oxidase and glucose. Its utility is well demonstrated with the specific detection of the analyte with a limit of detection as low as ∼58.1 pM (3σ), which makes this new biosensing method one of the most sensitive Ag+ assays in comparison with many other typical methods recently reported. Moreover, the satisfactory recovery of analyzing several types of real water examples, i.e., tap water, drinking water, pond water and soil water, additionally validates its feasibility for practical applications. • A new type of 3D paper-based device is designed for biosensing of picomolar Ag+. • It integrates nanoporous membranes with self-growing for signal amplification. • It only requires a personal glucose meter for a portable, quantitative readout. • This instrument-free biosensor offers new opportunities for point-of-care uses. [ABSTRACT FROM AUTHOR]

This paper presents a new analytical solution to predict the ultimate depth and holding capacity of plate anchors based on an integrated analysis of the anchor and mooring system. The configuration equations of the mooring line, both in soil and water, have been solved to obtain the complete line profile. A key advancement is that the mooring line angle at mudline varies compatibly with the whole anchor–mooring system. This is different from the solutions available in literature that neglect the mooring line segment in water and only assume a constant mooring line angle at the mudline. The system of equations is solved by using an iteration scheme, and parametric studies are demonstrated. The effects of line length, water depth, and seabed strength profile are explored by demonstrating the performance of a commercial plate anchor. It is found that neglecting the mooring line in water can significantly overestimate the ultimate embedment and capacity, especially when the mooring line length is relatively short. This paper can be used as an engineering tool to predict the ultimate capacity of an anchor–mooring system considering the integrated behavior of the whole system. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to reduce the dimensionality of unknown coefficient vectors of finite-element (FE) solutions in two-grid (CN) FE (TGCNFE) format for the nonlinear unsaturated soil water flow problem by using a proper orthogonal decomposition (POD) and to design a new reduced-dimension iteration TGCNFE (RDITGCNFE). For this objective, a new time semi-discrete CN (TSDCN) scheme for the nonlinear unsaturated soil water flow problem is first designed and the existence, stability, and error estimates of TSDCN solutions are demonstrated. Subsequently, a new TGCNFE format for the nonlinear unsaturated soil water flow problem is designed and the existence, unconditional stability, and error estimates of TGCNFE solutions are demonstrated. Next, a new RDITGCNFE format with the same FE basis functions as the TGCNFE format is built by the POD method and the existence, unconditional stability, and error estimates of RDITGCNFE solutions are discussed. Ultimately, the rightness of theory results and the superiority of the RDITGCNFE format are verified by two sets of numerical tests. It is worth noting that the RDITGCNFE format differs completely from all previous reduced-dimension methods, including the authors' previous works. Therefore, the study of this paper can not only provide a new theoretical method for the dimensionality reduction of numerical models for nonlinear problems but also provide an algorithm implementation technology for the numerical simulation of practical engineering problems. [ABSTRACT FROM AUTHOR]

Soil moisture monitoring is a research hotspot in the application of navigation satellite reflected signals. In order to retrieve soil moisture in any period with high precision and solve the problem that soil moisture cannot be retrieved when satellite observation data is partially missing, the principle of generalized extension in engineering science is applied to GNSS-IR technology in the paper. Firstly, the generalized extension approximation method is used to interpolate the precise ephemeris to obtain the altitude angle information of each satellite in each epoch. Then, the generalized extended interpolation and extrapolation models were used to supplement the initial phase of the reflected signal corresponding to the missing data as the input of the inversion model to predict soil moisture. The soil moisture inversion experiment was completed according to the observation data provided by PBO. The results show that the generalized extension method can cope with the missing data of different lengths and distributions in GNSS-IR soil moisture retrieval, When the data were confirmed for 15 days, the correlation coefficient of inversion results was 0.916, RSME was 0.03358 cm3/cm3, and MAE was 0.02912 cm3/cm3, and the correlation coefficient of more than 0.85 can be maintained when the amount of missing data reaches 60 % of the test set. [ABSTRACT FROM AUTHOR]

Soil moisture data with both a fine spatial scale and a short global repeat period would benefit many hydrologic and climatic applications. Since the radar transmitter malfunctioned on NASA's Soil Moisture Active Passive (SMAP) in 2015, SMAP soil moisture has been downscaled using numerous alternative fine-resolution data. In this paper, we describe the creation and validation of a new downscaled 3 km soil moisture dataset, which is the culmination of previous work. We downscaled SMAP enhanced 9 km brightness temperatures by merging them with L-band Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data, using a modified version of the SMAP active–passive brightness temperature algorithm. We then calculated 3 km SMAP/CYGNSS soil moisture using the resulting 3 km SMAP/CYGNSS brightness temperatures and the SMAP single-channel vertically polarized soil moisture algorithm (SCA-V). To remedy the sparse daily coverage of CYGNSS data at a 3 km spatial resolution, we used spatially interpolated CYGNSS data to downscale SMAP soil moisture. 3 km interpolated SMAP/CYGNSS soil moisture matches the SMAP repeat period of ~2–3 days, providing a soil moisture dataset with both a fine spatial scale and a short repeat period. 3 km interpolated SMAP/CYGNSS soil moisture, upscaled to 9 km, has an average correlation of 0.82 and an average unbiased root mean square difference (ubRMSD) of 0.035 cm3/cm3 using all SMAP 9 km core validation sites (CVSs) within ±38° latitude. The observed (not interpolated) SMAP/CYGNSS soil moisture did not perform as well at the SMAP 9 km CVSs, with an average correlation of 0.68 and an average ubRMSD of 0.048 cm3/cm3. A sensitivity analysis shows that CYGNSS reflectivity is likely responsible for most of the uncertainty in downscaled SMAP/CYGNSS soil moisture. The success of 3 km SMAP/CYGNSS soil moisture demonstrates that Global Navigation Satellite System–Reflectometry (GNSS-R) observations are effective for downscaling soil moisture. [ABSTRACT FROM AUTHOR]

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region. [ABSTRACT FROM AUTHOR]

The geotechnical tube technology has been widely used in the dewatering treatment of high-water-content slurries, such as engineering waste muds, mine water sludges, and dredging slurries. However, the existing engineering practice adopting the traditional geotextile tubes is characterized by low efficiency and difficult quality control. To address these issues, a new approach was developed by combining the geotextile tubes and vacuum preloading technology. In this method, several plastic drain boards were embedded in a geotextile tube as prefabricated horizontal drains (PHDs), and a vacuum was applied to promote drainage. The effectiveness of the proposed method was verified by a comparison test. A two-dimensional plane-strain analytical consolidation problem with a distributed drainage boundary was then formulated and solved to predict the consolidation process of soil in a vacuum preloaded geotextile tube. The analytical solution was validated against the results of a pilot field test, and then the influence of PHD spacing on the consolidation process was also discussed. Compared to the traditional geotextile tubes, the approach proposed in this paper promises good applicability and can effectively shorten the treatment time by 73.3% and reduce the water content of treated soils by 51.9%. [ABSTRACT FROM AUTHOR]

Infrastructures are mostly built on unsaturated soil in the tropical region such as Nigeria, yet soil investigations and designs are based on saturated soil mechanics owing to difficulties associated with soil suction measurements using direct methods such as pressure plate extractor, triaxial testing equipment for testing unsaturated soil, etc. Soil water characteristic curve is an important parameter for estimating unsaturated soil property function. This research considered an indirect method (filter paper) of laboratory soil suction measurement (which is relatively simple, fast and inexpensive) and predictive correlation equations for estimating soil water characteristic curve from index properties of soil for four unsaturated tropical soils of Nigeria, three predictive correlation equations were used in this research namely; Navid et al model (2012), Zapata et al model (2000) and Witczak model (2006), the soil water characteristic curve constant parameters computed from these models were fitted through either Van Genuchten model (1980) or Fredlund & Xing model (1994). Results of air entry values of soils from experimental work and predictive correlation equations were in close range which indicated that in spite of the difficulties experienced in performing laboratory suction test, the predictive correlation equations such as the ones used in this research were found to be proper for estimating soil water characteristic curve. [ABSTRACT FROM AUTHOR]

Drought is a disaster that seriously constrains economic development and endangers human life. This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for drought monitoring, using Cyclone Global Navigation Satellite System (CYGNSS) data to monitor drought in Jiangxi and Hunan Provinces, China, in 2022. This study applies the Random Undersampling Boosting (RUSBoost) algorithm to detect waterbodies and linear regression to retrieve soil moisture (SM). Result shows that drought in September was heaviest, with the area of Poyang Lake in Jiangxi and Dongting Lake in Hunan decreasing by 70.2% and 76.9%, respectively, compared to that in June. The variation in retrieved SM shows that the Poyang Lake Plain and Jitai Basin in Jiangxi and the Dongting Lake, Yuanjiang River, and Xiangjiang River basins in Hunan suffered from the most serious drought. The variation in retrievals shows high consistency with various reference datasets, including Soil Moisture Active Passive (SMAP) SM data and vegetation condition index (VCI). The correlation coefficient between retrieved SM and VCI is 0.93 in Jiangxi and 0.94 in Hunan. [ABSTRACT FROM AUTHOR]

Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl). [ABSTRACT FROM AUTHOR]

This paper deals with the problem of predetermining the spray angle and uniformity of the flat fan sprayer with a semicircular impact surface for the intra-soil application of liquid mineral fertilizers. The jet impact on a round splash plate and radial atomization properties are investigated theoretically, the formation features of the spray with an obtuse angle are studied in a geometrical way, and the design search of the nozzle shape and optimization calculations are performed using computational fluid dynamics (CFD) simulations and then verified experimentally. It was revealed that the spray rate and spray angle can be adjusted by changing the parameter s, and when the spray angle is within s = 0–0.2 mm, it forms spray angles with range of 140°–175°. The spraying angle, in turn, shows the potential length of the tillage knife in accordance with the undersoil cavity dimensions. A spray uniformity of up to 74% was achieved, which is sufficient for applied studies and for intra-soil application operations. According to the investigations and field experiments, it can be concluded that the designed nozzle is applicable for the intra-soil application of liquid mineral fertilizers. The use of flat fan nozzles that form a spraying band under the soil cavity and along the entire length of the tillage knife ensures a highly efficient mixing process, the liquid mineral fertilizers with treated soil (particles) positively contributing to plant maturation. [ABSTRACT FROM AUTHOR]

Using polyaspartic acid (PAsp) and bentonite (BT) as the main raw materials, a new type of degradable soil water retaining agent (PAsp-AA/BT) was synthesized by microwave radiation. The optimum synthesis conditions and comprehensive properties of PAsp-AA/BT were discussed and the structure and surface characteristics of PAspsp-AA/BT were characterized by FTIR, SEM, XRD and TGA in the paper. The results showed that the optimum synthesis conditions of PAsp-AA/BT were as follows: the dosages of polyaspartic acid (PAsp), bentonite (BT), initiator potassium persulfate, crosslinking agent N,N′-methylene bisacrylamide was 5, 3, 0.3, 0.03%, respectively, the neutralization degree of acrylic acid was 75%, and the microwave power was 490W. Under this condition, the absorption ratio of the synthesized PAspsp-AA/BT in deionized water and 0.9% NaCl solution was 953 and 164 g/g, respectively. The synthesized PAsp-AA/BT had a high water absorption rate, good water retention and repeated water absorption, and the degradation rate in soil within 30 days reached 32.75%, with good degradation effect. The analysis of SEM, FT-IR, XRD and TGA showed that: the surface of PAsp-AA/BT was rough and had obvious pore structure, which was conducive to the diffusion of water molecules; polyaspartic acid, bentonite and acrylic acid were polymerized; the cross-linking structure was formed between polyaspartic acid, bentonite and acrylic acid; the product of PASP-AA/BT had good thermal stability. This study provides a new soil water retaining agent, which is helpful for the better development of soil water retaining agent research. [ABSTRACT FROM AUTHOR]

The use of bistatic polarimetric SAR for rough surface observation has attracted increasing interest in recent years, with its acquisition of additional polarimetric information. In this paper, we investigate the sensitivity of polarimetric variables to soil moisture and surface roughness, with the intention of locating favorable bistatic geometries for soil moisture retrieval. However, in the bistatic setting, the expanded imaging geometry is convolved with the polarimetric scattering response along with the in-scene variations in the soil moisture and surface roughness. The probing polarization states continuously evolve with the bistatic geometry, incurring varying polarization orientation angles. In this investigation, we propose to first compensate the bistatic polarimetric observations for the geometry-induced polarization rotation. Simulations based on a two-scale rough surface scattering model are then used to evaluate the optimal imaging geometry for the best sensitivity to the soil moisture content. We show the different sensing geometries associated with a full list of common polarimetric variables, as we seek favorable bistatic geometries in non-specular directions. The influences of both surface roughness scales are evaluated, with the small-scale roughness parameter imposing the greatest limitation on our results. [ABSTRACT FROM AUTHOR]

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat. [ABSTRACT FROM AUTHOR]

Efficient methods for predicting weather-related hazards are crucial for stakeholders managing environmental risks. Many environmental hazards depend on the evolution of meteorological conditions over protracted periods, requiring assessments that account for evolving conditions. The TAMSAT-ALERT approach addresses this challenge by combining observational monitoring with a weighted climatological ensemble. As such, it enhances the utility of existing systems by enabling users to combine multiple streams of monitoring and forecasting data into holistic hazard assessments. TAMSAT-ALERT forecasts are now used in a number of regions in the Global South for soil moisture forecasting, drought early warning and agricultural decision support. The model presented here, General TAMSAT-ALERT, represents a significant scientific and functional advance on previous implementations. Notably, General TAMSAT-ALERT is applicable to any variable for which time series data are available. In addition, functionality has been introduced to account for climatological non-stationarity (for example due to climate change); large-scale modes of variability (for example El Nino), and persistence (for example of land-surface condition). In this paper, we present a full description of the model, along with case studies of its application to prediction of Central England Temperature, Pakistan vegetation condition and African precipitation. [ABSTRACT FROM AUTHOR]

The paper compares physical and chemical characteristics of technogenic soil profiles at coal mine spoil from Maritsa-Iztok Mines. Sites with coniferous vegetation and without vegetation were selected. Brief comparison was made between physicochemical characteristics of studied soil profiles and those from other mine spoils. Relations with previous studies are also provided. The technogenic soil profiles have high cation exchange capacity and organic carbon content. Soil water repellency was measured and its correlation with studied soil characteristics of the Technosols was established. The water drop penetration time (WDPT) decreases sharply after heating of extremely hydrophobic soil samples. Before heating it correlates significantly with exchangeable cations of aluminium (R*WDPT-Al = 0.863) in the soil profile under pine and less with hydrogen in the profile without vegetation (RWDPT-H8.2 = 0.409). In the pine site, significant correlation was also found between the WDPT and the total organic carbon (R*WDPT-TOC = 0.711) as well as with fulvic organic carbon (R*WDPT-FOC = 0.824). In the non-vegetated soil profile, the correlation is significant with the fraction of unextracted organic carbon (R*WDPT-Cunextr. = 0.644). [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ–h–K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor's specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R2 = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems-Interferometric Reflectometry (GNSS-IR) is an emerging remote sensing technique studied and demonstrated for soil moisture retrieval over bare soil using multipath GNSS data. However, retrieval of soil moisture in the presence of vegetation/crops is still a very challenging task even becoming a more complex problem without using any ancillary datasets to compensate the vegetation effect for soil moisture retrievals in higher crop growth stages. This paper presents a novel multipath phase-based vegetation correction scheme for improved field-scale soil moisture retrieval using L-band data from Navigation with Indian Constellations (NavIC) based on GNSS-IR technique. The proposed three steps vegetation correction scheme categorized the crop as per growth stages for sensitivity analysis of NavIC derived multipath phase as GNSS-IR observables and significantly compensated vegetation effects on multipath phase for improved soil moisture retrievals. The proposed method utilized only multipath phase data as single SNR metrics without any ancillary data sets and validated during complete growth cycle of winter wheat crop (sowing to harvesting stages). It has been shown that the soil moisture retrieved using proposed vegetation correction scheme provides a Pearson correlation coefficient of 0.87 and ubRMSD of 0.0341 m3/m3 with in situ measured soil moisture, which is better than bare scheme with Pearson correlation coefficient of 0.71 and ubRMSD of 0.0480 m3/m3. Overall, proposed scheme has found highly effective for vegetation correction and has shown a good potential candidate for VSM retrievals over crop-covered soil using other GNSS constellations (GPS, GLONASS, Galileo and BeiDou etc.). [ABSTRACT FROM AUTHOR]

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material's ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements. [ABSTRACT FROM AUTHOR]

In order to improve the molding rate of biomass particles extruded by ring mold of the mobile straw granulator, a real-time straw moisture content detection system based on frequency was designed in this paper. The detection system comprised the frequency based acquisition devices and the supporting circuits, and support vector regression based calculation method. The acquisition device contained a soil separation cylinder and a signal detection chamfer. The soil separation cylinder was used to remove the soil from the straw. The moisture of the straw was transformed into the relatively stable frequency for detection, but the temperature can affect the Brownian movement of free water. Hence, the designed signal detection chamfer mainly contained a frequency sensor and a temperature sensor. The proposed calculation method blended the frequency and temperature to acquire the accurate moisture of the straw. A water replenishment module was also designed to verify the effectiveness of the detection system, and it was used to supply water to the straw when it becomes too dry. The system was verified in the experimental plots and field. The actual moisture content was obtained by 105°C drying method. The results obtained in the experiment plots showed that the detectable moisture content range was between 9.09% to 46.68%, the maximum detection error was less than 0.44%, and the average absolute error was less than 0.33%, and the molding rate could reach approximately 94%. The results obtained in the fieldd showed that the average molding rate achieved was 93.57% and 89.76% for straws with moisture content of about 20% and 15%, respectively. The detection system comprehensively takes into account the influence of temperature and soil on moisture content and can effectively improve the working efficiency of the mobile straw granulator. [ABSTRACT FROM AUTHOR]

Hydropedology is an interdisciplinary field that studies the interactions between soil and water, recognizing that soils influence hydrological processes through their hydraulic properties, and serve as indicators of hydrological behaviour through their morphological properties that are shaped by water regimes. Given the practical implications of hydropedology and its integration into South Africa's latest soil classification system, an updated categorization of soil forms and 1 657 (1 629 + 28) families was necessary, organizing them into three overarching response groups based on their predominant hydrological responses: recharge, interflow, and responsive. Within these groups, recharge soils are further classified into deep, shallow, and slow subgroups, interflow soils encompass soil/bedrock, shallow, and slow categories, while responsive soils are subdivided into responsive shallow and responsive wet. This paper aims to enhance the reader's comprehension of hydrological responses and simplify the intricacies integrated into South Africa's official soil classification system. [ABSTRACT FROM AUTHOR]

This paper focuses on the possible impact of the thermal soil regime on the occurrence of forest fires in the Zaječar administrative district in Eastern Serbia. The study uses data on soil temperature from the Republic Hydrometeorological Service of the Republic of Serbia and the ERA5-Land gridded reanalysis dataset for monthly temperature and volumetric soil water of the soil level 1 (0-7 cm), and soil level 2 (7-28 cm) with horizontal resolution of 0.1°C (approximately 12 km × 12 km) during the period of 2009-2021. Differences in soil temperature at depths of 2 cm, 5 cm, 10 cm, and 20 cm at the meteorological station in Zaječar for the periods 1961-1990 and 2009-2021 are observed. By analyzing the data on the registered number of forest fires for the period 2009-2021, pronounced oscillations in the dynamics of forest fires can be observed. The minimum number of forest fires (0) is registered in 2014, when the maximum of soil moisture in the soil levels 1 and 2 is observed. A high number of forest fires corresponds to high soil temperatures in 2012 and 2017. Soils drier and hotter than the average are registered in 47.0% of all months with the fire occurrences. About 50% of fires occurred in dry and hot soils during 2012 and 2017. This percentage was zero in 2014, when there were no fires. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research. [ABSTRACT FROM AUTHOR]

Meteorological data are essential in precision agriculture in the Canadian Prairies and are often associated with spatiotemporal discontinuity and scarcity. Reanalysis data products aim to address this challenge and have recently gained popularity. The European Centre for Medium-Range Weather Forecasts' ERA5, and its high-resolution land component, ERA5-Land, are two reanalysis datasets that provide hourly estimates of many climate variables globally. This paper focuses on evaluating the performance of ERA5 and ERA5-Land over the Canadian prairies, utilizing data from 109 weather stations situated in southern Manitoba, Canada. Various variables are investigated at daily, monthly, and annual aggregation periods, including air temperature, ground temperature, soil water content, wind speed, precipitation, and evaporation. The datasets are evaluated regarding seasonal bias and spatial distribution of errors over the study area. Regression parameters are also presented to address the biases. Among the investigated variables, air temperature and wind speed exhibit the lowest errors. The evaluation further reveals an overall tendency to overpredict ground temperatures and precipitation while underpredicting evaporation. Based on these findings, the ERA5 and ERA5-Land datasets hold significant potential in applications such as climate-smart agriculture, energy demand analysis, assessing renewable energy resources, and facilitating sustainable urban development. [ABSTRACT FROM AUTHOR]

Clogging is a major geohazards risk in mechanized tunnelling through cohesive soils. Clay clogging results from the high adhesion between the clay and metal. Based on the water film theory and Reynolds fluid equation, the interfacial adhesion between metal and soil is simplified in this study as viscous hydrodynamic behavior between planes. Considering the influence of capillary force and the viscous force of water film at the interface between metal and soil, a theoretical calculation model of interfacial adhesion between metal and soil is established. The influence of water film thickness and separation rate on the interfacial adhesion between metal and soil is qualitatively analyzed. Then, the adhesion stress between the clay and the metal surface was tested with a pullout test and the influence of moisture content, pullout rates and types of clay minerals on the adhesion stress was analyzed. Finally, the calculation model of adhesion force was compared with the experimental results. The calculation model of soil adhesion stress established in this paper can quantitatively describe the relationship between soil adhesion force and moisture content and can also qualitatively reveal the influence mechanism of soil moisture content on adhesion stress. [ABSTRACT FROM AUTHOR]

Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth's atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m−3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m−3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m−3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM. [ABSTRACT FROM AUTHOR]

Throughfall (TF) is an important water input of rainfall redistribution into floor, and its spatial-temporal variability under some species' canopies has been documented to evaluate effect on splash erosion. However, the understanding of TF variability under large broad-leaved canopy remains insufficient. In this study, the spatial heterogeneity, temporal stability and drop size of TF were quantified using variogram fitting, normalised ranking and filter paper staining, respectively, under banana (Musa nana Lour.) canopy comprising long and wide leaves. Results indicated TF pattern showed strong spatial correlation at a range of 3-5 m. High spatial variability of TF was found, which was affected by rainfall event size and was accompanied by great canopy disturbance. TF plots revealed high time variability, which was mainly controlled by unstable banana canopy structure. TF drop size from leaf dripping points varied in 3-10 mm and showed significant differences (p,0.05) among five kinds of leaf shapes, implying that concave and broken banana leaves were involved in the variability of TF drop size. Overall, results demonstrate the spatial-temporal variability of TF is dramatically induced by banana canopy with broad leaves, which may result in non-uniform soil water content and splash erosion under the canopy. [ABSTRACT FROM AUTHOR]

This work presents a simple and innovative protocol employing a microfluidic paper-based analytical device (µPAD) for equipment-free determination of mercury. In this method, mercury (II) forms an ionic-association complex of tetraiodomercurate (II) ion (HgI42−(aq)) using a known excess amount of iodide. The residual iodide flows by capillary action into a second region of the paper where it is converted to iodine by pre-deposited iodate to liberate I2(g) under acidic condition. Iodine vapor diffuses across the spacer region of the µPAD to form a purple colored of tri-iodide starch complex in a detection zone located in a separate layer of the µPAD. The digital image of the complex is analyzed using ImageJ software. The method has a linear calibration range of 50–350 mg L−1 Hg with the detection limit of 20 mg L−1. The method was successfully applied to the determination of mercury in contaminated soil and water samples which the results agreed well with the ICP-MS method. Three soil samples were highly contaminated with mercury above the acceptable WHO limits (0.05 mg kg−1). To the best of our knowledge, this is the first colorimetric µPAD method that is applicable for soil samples including mercury contaminated soils from gold mining areas. [ABSTRACT FROM AUTHOR]

Atriplex canescens is widely cultivated as drought and salt-tolerant fodder in arid regions of Northwest China, which is used for photoremediation of degraded land and soil and water conservation. To explore the growth performance of A. canescens when exposed to drought and salt stress, seedlings were treated with a range of drought stress (WC1: 75 ± 3.6%, WC2: 49 ± 2.9% and WC3: 27 ± 2.5% of soil water content) and the corresponding drought stress with additional sodium salt supplementation (NaCl:Na2SO4 = 1:1 with the total concentration of Na+ set to 150 mM). The findings of this paper indicated that moderate sodium salt could stimulate the growth of A. canescens and effectively alleviate the deleterious impact of drought stress by increasing the turgor potential (ψt) and relative water content (RWC) and decreasing the leaf water osmotic potential (ψs). Furthermore, the photosynthetic capacity was improved and the negative effects of drought stress on photosystem II (PSII) were mitigated. The extra 150 mM sodium salt also markedly increased the contribution of Na+ to ψs and the contribution of betaine to ψs. In summary, these results indicate that A. canescens can adapt to drought stress by accumulating enough Na+ for osmotic adjustment (OA). Additionally, this paper is aimed to provide a fundamental basis for the utilization and cultivation of A. canescens as a favored pasture crop in the Qaidam basin, thus increasing the ecological and environmental benefits for arid regions worldwide. [ABSTRACT FROM AUTHOR]

An appropriate contact mechanics model and parameters are key to achieving accurate results in discrete element analyzis. This is necessary to predict the process of contact collision between the soil and maize seed during deposition. In this paper, the contact process between maize seed and soil is analyzed using the maize seed variety (Liangyu 99) and maize-sowing field soil (with three different moisture contents) as research objects. Based on this, the contact process between maize seeds and soil has been analyzed, on the basis of which a mechanical model suitable for simulating the contact process between maize seeds and soil has been explored, and the selection of parameters between heterogeneous particles (maize seed and soil particles) has been investigated. The results showed that adhesion forces have a significant effect on the collision process between seed and soil particles. While the presence of tangential adhesion force can be replaced by increasing the static and rolling friction coefficients, the normal adhesion force cannot be compensated in this way. The Edinburgh Elasto-Plastic Adhesive (EEPA) model is selected in this paper to describe the contact between seed and soil particles. The significance of the input parameters in the EEPA model is investigated using the Plackett–Burman test. The parameters between soil and seed particles are optimized using the central composite design, and the optimal parameter combinations are obtained. The relative error between the simulation and test result of the slope test for the three soil moisture contents is within 5.4%, validating the accuracy of the calibrated parameters. [ABSTRACT FROM AUTHOR]

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented. [ABSTRACT FROM AUTHOR]

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies' technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making—including aspects of salience, credibility, and legitimacy—within each national context. [ABSTRACT FROM AUTHOR]

This paper aims to improve the Soil and Water Assessment Tool (SWAT) model performance across the Major River Basins in Madagascar (MRBM), specifically for SWAT simulation in the Manambolo, Onilahy, Mananara, and Mandrare basins. A multi-gauge calibration was carried out to compare the performance of SWAT + Toolbox, and R-SWAT, SWAT + Editor Hard calibration on a monthly time step for the periods 1982-1999. We found that the SWAT + model generated greater surface runoff, while the SWAT model resulted in higher groundwater flow in both CSFR and CHIRPS datasets. It has been demonstrated that the SWAT + Toolbox had more potential in calibrating runoff across the MRBM compared to R-SWAT. Calibration in both methods led to a reduction in surface runoff, percolation, water yield, and curve number but increased the lateral flow, evapotranspiration (ET), and groundwater flow. The results showed that the multi-gauge calibrations did not significantly enhance simulation performance in the MRBM compared to single-site calibration. The performance of the SWAT + model for runoff simulation within the SWAT + Toolbox and R-SWAT was unsatisfactory for most basins (NSE,0) except for Betsiboka, Mahavavy, Tsiribihina, Mangoro, and Mangoky basins (NSE = 0.40-0.70; R² = 0.45-0.80, PBIAS≤ +25), whether considering the CHIRPS or CSFR datasets. Further study is still required to address this issue. [ABSTRACT FROM AUTHOR]

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models. [ABSTRACT FROM AUTHOR]

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations. [ABSTRACT FROM AUTHOR]

In environmentally sensitive areas, especially the arid and semi-arid regions, the greening stability process and its influencing factors can directly affect the sustainable development of the ecological environment. In this study, multi-source remote sensing data such as land use/cover data, MODIS NDVI, and soil moisture, methods such as stability index, vegetation quantitative remote sensing, and Geodetector were employed to analyze the sustainability of the greening process in the Mu Us Sandy in 2000–2020, which were viewed from three aspects: changes in stability of land use types and function, soil moisture change and influencing factors on greening stability. The results showed that, (1) From the stability of land use types, continuous stable ecological land accounted for more than 50%, showing that decreased from northwest toward southeast. (2) From the functional stability, NDVI showed a fluctuated growth (0.035/a), with an increasing distribution pattern from northwest to southeast. Additionally, Vegetation changes were unstable and concentrated in the western part of the study area (OtogBanner and Otog Front Banner), while the eastern part was stable, in which vegetation improvement took the main position. Moreover, mobile dunes almost disappeared, and semi-fixed dunes decreased and gradually shrank to the west of the sandy area, while fixed dunes soared and were concentrated in the middle of the sandy land. (3) From the soil moisture change, soil moisture at different underground depths showed an overall increasing trend, but the deep soil moisture was higher than the shallow, and spatial distribution varied greatly. (4) From the influencing factors, natural factors significantly influence greening stability, among which precipitation had a particularly profound impact, and interactions with other natural and social factors were higher explanatory. The paper aims to explore whether the ecological environment is developing in a good and orderly direction in the Mu Us Sandy Land, and the potential factors that cause its changes, to provide a theoretical basis for scientific governance in the Mu Us Sandy Land and other arid and semi-arid areas in the future. [ABSTRACT FROM AUTHOR]

Geotextile offers numerous benefits in improving pavement performance, including drainage, barrier functionality, filtration, and reinforcement. Wicking geotextile, a novel variant in this category, possesses the intrinsic ability to drain water autonomously from soils. This paper details the development and application of a comprehensive multiphysics model that simulates the performance of wicking geotextile within a pavement system under freezing climates. The model considers the inputs of various environmental dynamics, including the impact of meteorological factors, groundwater levels, ground heat, and drainage on the pavement system. The model was firstly validated using field data from a long-term pavement performance (LTPP) road section in the cold region. It was subsequently applied to assess the impacts of wicking geotextile if it was installed on the road section. The model simulated the coupled temporal and spatial variations in soil moisture content and temperature. The simulation results demonstrated that wicking geotextile would create a suction zone around its installation location to draw water from surrounding soils, therefore reducing the overall unfrozen water content in the pavement. The results also showed that the installation of wicking geotextile would delay the initiation of frost heave and reduce its magnitude in cold region pavement. [ABSTRACT FROM AUTHOR]

Climate change is expected to have long-term impacts on drought and wildfire risks in Oregon as summers continue to become warmer and drier. This paper investigates the projected changes in drought characteristics and drought propagation in the Umatilla River Basin in northeastern Oregon for mid-century (2030–2059) and late-century (2070–2099) climate scenarios. Drought characteristics for projected climates were determined using downscaled CMIP5 climate datasets from ten climate models and Soil and Water Assessment Tool to simulate effects on hydrologic processes. Short-term (three months) drought characteristics (frequency, duration, and severity) were analyzed using four drought indices, including the Standardized Precipitation Index (SPI-3), Standardized Precipitation-Evapotranspiration Index (SPEI-3), Standardized Streamflow Index (SSI-3), and the Standardized Soil Moisture Index (SSMI-3). Results indicate that short-term meteorological droughts are projected to become more prevalent, with up to a 20% increase in the frequency of SPI-3 drought events. Short-term hydrological droughts are projected to become more frequent (average increase of 11% in frequency of SSI-3 drought events), more severe, and longer in duration (average increase of 8% for short-term droughts). Similarly, short-term agricultural droughts are projected to become more frequent (average increase of 28% in frequency of SSMI-3 drought events) but slightly shorter in duration (average decrease of 4%) in the future. Historically, drought propagation time from meteorological to hydrological drought is shorter than from meteorological to agricultural drought in most sub-basins. For the projected climate scenarios, the decrease in drought propagation time will likely stress the timing and capacity of water supply in the basin for irrigation and other uses. [ABSTRACT FROM AUTHOR]

Freezing conditions under different humidity will influence the mechanical properties of geotextiles, leading to the gradual fracture of geotextiles. It brings hidden danger to the whole isolation, reinforcement and protection of rock and soil. It is particularly important to study the tensile and puncture properties of geotextiles considering low temperature and moisture content. In this paper, a series of tensile and puncture tests of geotextiles are performed under different low temperatures (0, −3, −6, −9, and −12 °C) and at different moisture content levels (0, 5, 10, 30, 50, and 80%). From the microscopic perspective, the failure mechanism considering the low temperature and moisture content was explained comprehensively. Experimental results indicate that with a decrease in freezing temperature, the tensile strength of geotextiles increases as a parabolic function while the elongation at failure decreases as an exponential function. Additionally, the puncture strength of geotextiles presented a parabolic increase with the decreasing temperature. Under the freezing temperature environment, the higher moisture content of geotextiles can generate a higher puncture strength increment. This research contributes to a more comprehensive understanding of the tensile and puncture properties of geotextile materials considering low temperature and moisture content. It can provide important guidance for the design of slopes, the reinforcement of earthen dams, and roadbed reinforcement with geotextiles in cold regions. [ABSTRACT FROM AUTHOR]

Event-based estimates of the design flood in ungauged catchments are normally based on a single catchment response time parameter expressed as either the time of concentration (TC), lag time (TL) and/or time to peak (TP). In small, gauged catchments, a simplified convolution process between a single observed hyetograph and hydrograph is generally used to estimate these time parameters. In medium to large heterogeneous, gauged catchments, such a simplification is neither practical nor applicable, given that the variable antecedent soil moisture status resulting from previous rainfall events and spatially non-uniform rainfall hyetographs can result in multi-peaked hydrographs. In ungauged catchments, time parameters are estimated using either empirical or hydraulic methods. In South Africa (SA), unfortunately, the majority of the empirical methods recommended for general use were developed and verified in catchments ≤ 0.45 km² without using any local data. This paper presents the further development and verification of the streamflow-based approach developed by Gericke (2016) to estimate observed TP values and to derive a regional empirical TP equation in Primary Drainage Region X, SA. A semi-automated hydrograph analysis tool was developed to extract and analyse complete hydrographs for time parameter estimation using primary streamflow data from 51 flowgauging sites. The observed TP values were estimated using three methods: (i) duration of total net rise of a multi-peaked hydrograph, (ii) triangular-shaped direct runoff hydrograph approximations, and (iii) linear catchment response functions. The combined use of these methods incorporated the high variability of eventbased time parameters, and Method (iii), in conjunction with an ensemble-event approach sampled from the time parameter distributions, should replace the event-based approaches to enable the improved calibration of empirical time parameter equations. The conceptual approach used to derive the regional empirical TP equation should also be adopted when regional equations need to be derived at a national scale in SA. [ABSTRACT FROM AUTHOR]

In this paper, we have first discussed that groundwater is very important, especially as it is getting scarce. We see rain waters on the puddles but it is not going underground. That has to do with the increased saturated soil, which gives less space for water to move deeper. We studied the nonlinear problem that arises in infiltration phenomenon with External effect (Input and output). The infiltration phenomenon with External effect (Input and output) is defined by a non-linear partial differential equation, which gives the soil moisture content under suitable conditions. The moisture content was determined and was analyzed using the Differential transform method. We investigated groundwater infiltration with an external impact (Input and output) using the Differential Transform method. The analysis is also explained, as well as its numerical and graphical depiction. For this, we utilized MATLAB for that. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the weather conditions at the point where an estimate is made. Therefore, IWeCASF's aim is to determine these weather conditions. The procedure begins measuring them at only one checkpoint, called the primary checkpoint. The model determines the weather conditions anywhere within a region through image processing algorithms and fuzzy inference systems. The results are compared with the measurement records and with a spatial interpolation method. The performance is similar to or better than interpolation, especially in the rain, where the model developed is more accurate due to the certainty of replication. Additionally, IWeCASF does not require more than one measurement point. Therefore, it is a more appropriate approach to complement the point estimation model for enabling a regional soil moisture estimation. [ABSTRACT FROM AUTHOR]