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Identifying agricultural practices that enhance water cycling is critical, particularly with increased rainfall variability and greater risks of droughts and floods. Soil infiltration rates offer useful insights to water cycling in farming systems because they affect both yields (through soil water availability) and other ecosystem outcomes (such as pollution and flooding from runoff). For example, conventional agricultural practices that leave soils bare and vulnerable to degradation are believed to limit the capacity of soils to quickly absorb and retain water needed for crop growth. Further, it is widely assumed that farming methods such as no-till and cover crops can improve infiltration rates. Despite interest in the impacts of agricultural practices on infiltration rates, this effect has not been systematically quantified across a range of practices. To evaluate how conventional practices affect infiltration rates relative to select alternative practices (no-till, cover crops, crop rotation, introducing perennials, crop and livestock systems), we performed a meta-analysis that included 89 studies with field trials comparing at least one such alternative practice to conventional management. We found that introducing perennials (grasses, agroforestry, managed forestry) or cover crops led to the largest increases in infiltration rates (mean responses of 59.2 ± 20.9% and 34.8 ± 7.7%, respectively). Also, although the overall effect of no-till was non-significant (5.7 ± 9.7%), the practice led to increases in wetter climates and when combined with residue retention. The effect of crop rotation on infiltration rate was non-significant (18.5 ± 13.2%), and studies evaluating impacts of grazing on croplands indicated that this practice reduced infiltration rates (-21.3 ± 14.9%). Findings suggest that practices promoting ground cover and continuous roots, both of which improve soil structure, were most effective at increasing infiltration rates.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

Agricultural land preparation and weed control techniques are essential farm management tools that affect the dynamics of soil water infiltration and the estimation accuracy of infiltration models. To analyse the interaction effect of tillage and weed control methods on the changes in soil physical properties and the efficacy of infiltration models, an experiment was conducted on a sandy clay loam forest ochrosol at Hodzo near Ho in Ghana. Four tillage systems (No Tillage [NT], Reduced Tillage [RT], Plough + Harrow + Ridging [PHR], and Deep Tillage + Plough + Harrow + Ridging [DPHR]) and three weed control methods (Hoeing [H], Machete [MAT] and No Weeding [NW]) were employed. The study also tested the reliability of the models (Kostiakov, Philip, and Horton) using the goodness of fit statistical criteria: Root mean squared error (RMSE), Mean absolute error (MAE), Coefficient of determination (R 2 ), and Nash-Sutcliffe efficiency (NSE). The results show that conservation tillage systems (CsT) and conventional tillage systems (CT) with MAT weeding treatments recorded the highest moisture content across the studied soil profile, especially for NT x MAT (11.189%) which was significant (p < 0.05) in the 15–30 cm layer; the lowest were observed in the CsT and CT with H weeding interactions, especially for the DPHR x H (8.086%). Comparing the interaction effect on the soil infiltration, the highest mean infiltration rate was significant (p < 0.05) under the NT X H treatment combination whilst the lowest infiltration rate was recorded in the DPHR X H and PHR X NW treatment combinations. The efficiency of the fitting models (Kostiakov > Horton > Philip) highly prioritised the soil tillage operations and weed management under the treatments DPHR x MAT > DPHR x NW > DPHR x H > RT x MAT > PHR x NW > PHR x MAT > NT x NW > RT x MAT > PHR x H > RT x H > NT x MAT > RT x NW > NT x H in that order. The trend shows that the increase in tillage intensity and the decrease in weed management intensity induce the quality of the estimation process and vice versa. The study, therefore, identified the use of machete (MAT) with DPHR under the Kostiakov model as the efficient land management for modelling the cumulative infiltration characteristics of the sandy clay loam ochrosols of the study area.

Hydrological responses corresponding to the agricultural land use alterations are critical for planning crop management strategies, water resources management, and environmental evaluations. However, accurate estimation and evaluation of these hydrological responses are restricted by the limited availability of detailed crop classification in land use and land cover. An innovative approach using state-of-the-art Variable Infiltration Capacity (VIC) model is utilized by setting up the crop-specific vegetation parameterization and analyse the effect of uniform and heterogeneous agricultural land use over the hydrological responses of the basin, in the Kangsabati River Basin (KRB). Thirteen year simulations (1998–2010) based on two different scenarios i.e., single-crop in agricultural land use (SC-ALU) and multi-crop in agricultural land use (MC-ALU) patterns are incorporated in the model and calibrated (1998–2006) and validated (2007–2010) for the streamflow at Reservoir and Mohanpur in the KRB. The results demonstrated that the VIC model improved the estimates of hydrological components, especially surface runoff and evapotranspiration (ET) at daily and monthly timescales corresponding to MC-ALU than SC-ALU (NSC > 0.7). Grid-scale ET estimates are improved after incorporating heterogeneous agricultural land use (NSC > 0.55 and R2 > 0.55) throughout the period of 1998–2010. This study improves our understanding on how the change in agricultural land use in the model settings alters the basin hydrological characteristics, and to provide model-based approaches for best management practices in irrigation scheduling, crop water requirement, and management strategies in the absence of flux towers, eddy covariance, and lysimeters in the basin.

Water infiltration into soil is important in geotechnical engineering. The classical Green-Ampt (GA) infiltration model is widely used in soil infiltration due to its physical significance, but it ignores the actual unsaturated layer in the infiltration process and has some deficiencies. Thus, the present study established a modified GA infiltration model (MLGA model) using Darcy’s infiltration law and continuity equation to fully consider the variation characteristics of the soil water profile in the infiltration process. The Philip model and the GA model have the same physical basis. By combining the internal relationship between the infiltration rate and cumulative infiltration of the two models, the expression of matrix suction of the MLGA model was obtained. The existing measured data was used to verify the correctness and applicability of the MLGA model. Finally, the sensitivity analysis of the critical parameters of the model was performed. The results showed that the MLGA model is reliable and can be used for water infiltration analysis in loess sites. When the MLGA model is used to calculate the infiltration depth, the maximum relative error between the calculated value and the measured data is less than 10%, and the average relative error is 5.54%, which indicates that the model has a high calculation accuracy. The average error of the MLGA model is 24.47% of the Kostiakov model and 80.42% of the existing modified GA model (LGAM model). Sensitivity analysis of the saturated permeability coefficient, initial water content, saturated water content, and matrix suction was performed using the single-factor disturbance method. The influence of each parameter on infiltration depth was summarized, and the sensitivity of each parameter was quantitatively evaluated, which revealed that saturated water content is a highly sensitive parameter. When using the MLGA model to calculate infiltration depth, the accuracy of saturated water content should be ensured first. Finally, the difference between the MLGA model and the LGAM model in describing the whole process of water infiltration is discussed, and the influence mechanism of critical parameters on the water infiltration process is deeply analyzed. The established MLGA model provides theoretical support for further study of the loess water infiltration mechanism.

The investigation into the impact of gravel on water infiltration process and hydraulic parameters in stony soil could offer a theoretical basis to enhance water availability in rocky mountain area. A one-dimensional vertical infiltration experiment was used in this study. Six groups of gravel content of 0% (CK), 10% (W1), 20% (W2), 30% (W3), 40% (W4) and 50% (W5) were established to explore the changes in the wetting front, cumulative infiltration volume and infiltration rate. Then the accuracy of four infiltration models in simulating soil water infiltration processes was evaluated. Finally, Hydrus-1D was used to perform numerical inversion of the soil water content after infiltration. The findings revealed that: (1) When the infiltration time reached 300 min, the wetting front of the W1 , W2 , W3, W4 and W5 treatments was 11.00%, 17.00%, 32.25%, 38.75% and 54.50% lower than CK, the cumulative infiltration volume was 29.80%, 38.97%, 45.62%, 54.74% and 73.17% lower than CK, and the stable infiltration rate was 50.98%, 52.94%, 66.67%, 68.63% and 86.27% lower than CK. (2) The soil–water infiltration processes were accurately described by the Horton model, the coefficient of determination ( R 2 ) > 0.935. (3) The simulation results of Hydrus-1D showed that with the increase of gravel content, the values of the retention water content ( θ r ), saturated water content ( θ s ), shape coefficient ( n ) and saturated hydraulic conductivity ( K s ) were decreased, the values of the reciprocal of air-entry ( α ) were increased. The value of R 2 was more than 0.894, the root mean square error ( RMSE ) and mean absolute error ( MAE ) were less than 2%, which demonstrated that the Hydrus-1D model exhibited superior capability in simulating the changes of water content in stony soil in rocky mountain area. The findings of this study demonstrated that gravel could decrease the water infiltration process and affect the water availability. It could provide data support for the water movement process of stony soil and rational utilization of limited water resources in mountainous area.

The penetration of substances from the surface to deep inside plant tissues is called infiltration. Although various plant tissues may be effectively saturated with externally applied fluid, most described infiltration strategies have been developed for leaves. The infiltration process can be spontaneous (under normal atmospheric pressure) or forced by a pressure difference generated between the lamina surface and the inside of the leaf. Spontaneous infiltration of leaf laminae is possible with the use of liquids with sufficiently low surface tension. Forced infiltration is most commonly performed using needle-less syringes or vacuum pumps. Leaf infiltration is widely used in plant sciences for both research and application purposes, usually as a starting technique to obtain plant material for advanced experimental procedures. Leaf infiltration followed by gentle centrifugation allows to obtain the apoplastic fluid for further analyses including various omics. In studies of plant-microorganism interactions, infiltration is used for the controlled introduction of bacterial suspensions into leaf tissues or for the isolation of microorganisms inhabiting apoplastic spaces of leaves. The methods based on infiltration of target tissues allow the penetration of dyes, fixatives and other substances improving the quality of microscopic imaging. Infiltration has found a special application in plant biotechnology as a method of transient transformation with the use of Agrobacterium suspension (agroinfiltration) enabling genetic modifications of mature plant leaves, including the local induction of mutations using genome editing tools. In plant nanobiotechnology, the leaves of the target plants can be infiltrated with suitably prepared nanoparticles, which can act as light sensors or increase the plant resistance to environmental stress. In addition the infiltration has been also intensively studied due to the undesirable effects of this phenomenon in some food technology sectors, such as accidental contamination of leafy greens with pathogenic bacteria during the vacuum cooling process. This review, inspired by the growing interest of the scientists from various fields of plant science in the phenomenon of infiltration, provides the description of different infiltration methods and summarizes the recent applications of this technique in plant physiology, phytopathology and plant (nano-)biotechnology.

Plant rooting depth affects ecosystem resilience to environmental stress such as drought. Deep roots connect deep soil/groundwater to the atmosphere, thus influencing the hydrologic cycle and climate. Deep roots enhance bedrock weathering, thus regulating the long-term carbon cycle. However, we know little about how deep roots go and why. Here, we present a global synthesis of 2,200 root observations of >1,000 species along biotic (life form, genus) and abiotic (precipitation, soil, drainage) gradients. Results reveal strong sensitivities of rooting depth to local soil water profiles determined by precipitation infiltration depth from the top (reflecting climate and soil), and groundwater table depth from below (reflecting topography-driven land drainage). In well-drained uplands, rooting depth follows infiltration depth; in waterlogged lowlands, roots stay shallow, avoiding oxygen stress below the water table; in between, high productivity and drought can send roots many meters down to the groundwater capillary fringe. This framework explains the contrasting rooting depths observed under the same climate for the same species but at distinct topographic positions. We assess the global significance of these hydrologic mechanisms by estimating root water-uptake depths using an inverse model, based on observed productivity and atmosphere, at 30″ (∼1-km) global grids to capture the topography critical to soil hydrology. The resulting patterns of plant rooting depth bear a strong topographic and hydrologic signature at landscape to global scales. They underscore a fundamental plant–water feedback pathway that may be critical to understanding plant-mediated global change.

Immune cell infiltration into pancreatic islets (termed insulitis) has been linked with destruction of pancreatic β-cells and thus with onset of diabetes mellitus. Recently published guidelines for reporting insulitis may generate some deliberation on pancreatic islet inflammation and a re-examination of the role that immune cells play in the process of β-cell death and dysfunction. Herein, we offer the viewpoint that a mild insulitis would be sufficient to produce an adequate supply of inflammatory molecules capable of initiating and maintaining an inflammatory state within the pancreatic islets. Recent guidelines recommend that a >2-fold increase in CD45+ immune cell infiltrates into islets be the minimum threshold for reporting insulitis. While standardization of the criteria for reporting insulitis is an excellent idea, these new parameters may raise questions about whether a mild leukocyte infiltration (e.g., 2-fold increase) into the pancreas and targeted towards islets is responsible for reductions in β-cell mass and function.

Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.