Soil Surface Conditions Research Articles

Soil Suitability Evaluation for Agriculture: New Model Development and Validation

ABSTRACT The evaluation of land suitability for agricultural production is a complex process that requires specialized information and expert analysis. This paper introduces the Soil Suitability Evaluation (SSE) model, a computer program designed to assess land suitability for agriculture. The model considers soil surface conditions (erosion, micro-relief, and slope), physical characteristics (texture and depth), and chemical properties (salinity, sodicity, fertility, and calcium carbonate). It involves calculating these soil characteristics along with their respective weights. Compared to other land suitability evaluation methods, the SSE model provides realistic and practical results including assessments of soil and crop suitability. It serves as a simple and useful tool for decision-makers to determine land quality for agricultural use and proposed some recommendations for land reclamation.

Rainfall Patterns and Groundwater Dynamics: Implications for Soil and Water Conservation in Kodihalli Sub-Watershed, Haveri District, Karnataka, India

The Kodihalli sub-watershed (4D4D2f) is lies between 14028’23.68”-14034’20.103” North latitudes and 75026’49.224”-75031’44.208” East longitudes and covers an area of about 4398.83 ha. with an average annual rainfall of 783.0 mm (2014-2021). The maximum rainfall of 439.0 mm is received from June to September, 226.0 mm from October to early December and the remaining 118.0 mm is received during the rest of the year. The number of rainy days (>2.5 mm) varied from 13-24 days per year. On an average, the number of rainy-day events likely to produce runoff (20 to 30 mm) are about 2 to 8 rainy days per year with moderate variation across the years. The Actual Evapotranspiration (AET) over the years 2014 - 2021 in the Kodihalli sub-watershed varied from 383.0 to 770.0 mm. During 2014 - 2021, the average annual AET (621.0 mm) was less than the average rainfall (783.0 mm). The average AET/P ratio between 2014-2021 was about 0.79 which is less than the sustainable limit of about 0.80. The soils of watershed viz., sandy clay loam (IR 10 mm/hr) and sandy clay (IR 14 mm/hr) resulted in more infiltration rate than clay (2 mm/hr) and clay loam soils (7 mm/hr). Soil infiltration during a rainstorm is closely related to a number of factors such as the intensity and kinetic energy of the rainfall, soil surface conditions and soil properties such as those related to aggregate stability. For effective soil and water conservation, maximum area of about 3769 ha (85.7%) requires graded bunding and 267 ha (6.06%) area requires contour bunding.

Impact of Rainfall and Soil Infiltration on Groundwater Dynamics of Yerekoppi-1 Micro-watershed, Haveri District, Karnataka, India

The amount of water infiltrating the soil surface directly affects the quantity of surface runoff and erosion, and the recharge of both soil and groundwater. Groundwater recharge is an informative indicator of the water located beneath the ground surface. It has a direct impact on renewable supplies of groundwater. In the present study on Impact of rainfall and soil infiltration rate was used for effect on the groundwater level of the Yerekoppi-1 micro watershed (Manakur sub-watershed) in Haveri district of Karnataka state. Average annual rainfall is 700.7 mm and mean potential evapotranspiration is 1541.8 mm. An area of about 888 ha (94.32 %) has clay texture at the surface and 53 ha (5.68%) area has contributing settlement. In Yerekoppi-1 micro- watershed, 38 borewells are there in the vicinity of major stream of the micro- watershed. The mean depth of groundwater levels in the micro- watershed were monitored at a monthly frequency during January-2023 to March-2024. It was found that the maximum average depth of bore wells are about 17.6 mbgl and lowest average depth of borewell about 8.8 mbgl. Soil infiltration rate was measured at upper, middle and lower reaches of the watershed area in the year 2023 and 2024. The majority of the area contributed clay soil. In all the areas of micro-watershed, infiltration rate for clay soil is less than 5 mm/hr. Infiltration rates for all the areas was 1.72- 4.78 mm/hr. Soil infiltration during a rainstorm is closely related to a number of factors such as the intensity and kinetic energy of the rainfall, soil surface conditions and soil properties such as those related to aggregate stability. The groundwater recharge may be improved by growing plantation crop for increasing infiltration rate, construction of percolation tanks and farm ponds in the lower most of the agricultural land.

Quantitative analysis of aeolian dust incidence and diagnosis using a dynamic Bayesian network model: A case study of estuary area in central Taiwan

The presence of high concentrations of PM10 in aeolian dust within the estuary area of central Taiwan has been a focal point for air quality protection and remediation efforts over the past 15 years. This study explores the quantitative analysis of aeolian dust occurrences concerning various levels of PM10 concentration, aiming to offer early warning information to local communities and government agencies. Employing Dynamic Bayesian network (DBN) models, the research interrelates conditional probability dependencies among air quality and meteorological factors monitored in the downstream area of the Jhuoshuei River, predicting the probability distribution of aeolian dust events. The constructed DBNs demonstrate the inference of probability distributions of meteorological factors' states based on PM10 concentrations, enabling diagnostic analysis of meteorological conditions during high PM10 concentration dust events. The DBN models exhibit robust predictive ability, achieving an overall accuracy of 85.72% in validation and 85.8% in testing, with high discriminatory capacity that the area under the receiver operating characteristic curve (AUC) values of 0.924 and 0.915 during validation and testing, respectively. Sensitivity analysis highlights wind direction, wind speed, relative humidity as significant factors affecting PM10 concentrations. The probability of low PM10 concentrations was notably higher compared to higher concentrations, indicating effective aeolian dust remediation efforts. However, attention is warranted toward the 10% incidence of aeolian dust events deemed unhealthy or hazardous to human health. Diagnosis of meteorological conditions during high PM10 concentration events suggests low temperature, high wind speeds, and dry conditions, particularly with northeastern or southwestern winds, as conducive factors. This study underscores the physical relationship between meteorological factors and PM10 concentrations, employing DBNs to consider temporal relationships and uncertainties in data, aligning with human health impact standards proposed by the Ministry of Environment. Future research should incorporate soil and land surface conditions to provide a more comprehensive understanding of aeolian dust dynamics and mitigation strategies.

Flow and Transport of Methane from Leaking Underground Pipelines: Effects of Soil Surface Conditions and Implications for Natural Gas Leak Classification

Flow and Transport of Methane from Leaking Underground Pipelines: Effects of Soil Surface Conditions and Implications for Natural Gas Leak Classification

Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests

Abstract. The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface model against surface energy flux, snow depth and soil temperature observations from four eddy-covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling.

Soil permittivity estimation over vegetative fields using dual polarimetric SAR data

Soil permittivity estimation using Synthetic Aperture Radar (SAR) data under the vegetation cover has been an extensive research domain. In particular, the vegetation canopy over the soil layer complicates the interaction of microwave signals with the underneath soil layer and the vegetation canopy geometry. Nevertheless, the sensitivity of the microwave signal with the target dielectric and geometric properties makes it possible to retrieve the target permittivity from the SAR data. This work proposes a methodology for retrieving soil permittivity using dual-polarimetric (HH-HV or VV-VH) SAR data. We first derive the 2 × 2 covariance matrix (CXX-XYXB) for the extended Bragg (X-Bragg) surface scattering for both HH-HV and VV-VH configurations. The X-Bragg covariance information inherently captures the soil surface condition. Finally, we utilize the existing scattering power factorization framework to retrieve soil permittivity. We have validated the efficacy of the proposed technique using the simulated NISAR product from the L-band UAVSAR data over Manitoba, Canada, and the C- and L- band ESAR data over DEMMIN, Germany. The proposed technique achieves the soil permittivity within a Root Mean Square Error (RMSE) range of 2 to 6 for HH-HV and VV-VH configurations.

Restoration approach for alpine extremely degraded landscapes: testing direct seeding and mulching with grass clippings

Alpine meadows are key ecosystems that are facing increasing degradation due to climate change and anthropogenic disturbances. Despite tremendous efforts in ecological restoration and diverse communities rebuilding in alpine regions, restoration of extremely degraded alpine meadows (which have lost mattic epipedon) remains a major challenge for land managers. Here, we performed a field restoration experiment that combined seeding three grass species commonly (Elymus nutans, Poa annua, and Festuca sinensis) used in alpine meadow restoration with the application of grass clippings as mulch (bare land without mulching, 50% mulch cover, and 100% mulch cover) to ameliorate the soil surface conditions of degraded bare land. Soils covered with 50% and 100% mulching increased soil moisture, seedling density, plant cover, plant density, aboveground biomass, and belowground biomass, and reduced soil temperature during strong radiation days and insulated at night. In the three grass species seeding plots, mulching with grass clippings also improved seedling density, plant cover, plant density, and biomass. Moreover, 100% mulching had stronger restoration effect than 50% mulching. We further demonstrated that the improvement of surface microclimate in degraded alpine meadows by grass clipping mulching, that is, increasing soil moisture and regulating soil temperature, is an important reason for the success of grassland restoration. Mulching with grass clippings simulates the original mattic epipedon, improving the surface hydrothermal conditions and promoting ecological restoration facilitation. We encourage the application of locally available grass clippings after seeding as this can be an efficient, low‐cost, and sustainable approach for restoring extremely degraded high‐elevation alpine grassland ecosystems.

Estimation of wind drift and evaporation losses of sprinkler irrigation systems using dimensional analysis

Wind Drift and Evaporation Losses (WDEL) critically determine the application efficiency of sprinkler irrigation systems. In specific conditions, even half of the irrigation water can evaporate or drift out of the irrigated area. The purpose of this study was to develop a WDEL estimation equation based on dimensional analysis (Buckingham's π method), considering a wide set of independent variables. An equation was sought that could be used for various sprinkler technologies and meteorological conditions in semiarid areas. Our research used experimental data from the literature, obtained in northeastern Spain and northwestern Iran. The complete data set consisted of 153 WDEL observations obtained at different operating pressures, nozzle diameters, irrigation durations, day and night irrigations, elevations from the soil surface and meteorological conditions. The experiments involved solid-sets and moving laterals, impact sprinklers, gear driven sprinklers and rotating spray plate sprinklers. The equation developed in this research included four technical independent variables (main nozzle diameter, auxiliary nozzle diameter, operating pressure and nozzle elevation) and four meteorological independent variables (air temperature and relative humidity, wind speed and solar radiation). The equation resulted in improved estimation respect to the equations previously derived from part of the experimental data set. The proposed equation was calibrated using 70% of the experimental data and validated using 30% of the experimental data. When the proposed equation was applied to the complete experimental data set, the determination coefficient was 0.81 and the root mean square error was 3.49%. The proposed equation represents a clear improvement respect to the equations reported in the literature in terms of the number and range of the independent variables and the predictive capacity.

Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content (SWC) is one of the critical indicators in various fields such as geotechnical engineering and agriculture. To avoid the time-consuming, destructive, and laborious drawbacks of conventional SWC measurements, the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis. In this study, a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images, aiming to alleviate the interference of complex surface conditions with color information extraction. In addition, an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images. Meanwhile, a subset of 10 parameters is identified through three variable analytical methods. Then, four machine learning models for SWC prediction based on partial least squares regression (PLSR), random forest (RF), support vector machines regression (SVMR), and Gaussian process regression (GPR), are established using 32 and 10 characteristic parameters, and their performance is compared. The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions. The RGB, CIEXYZ, and CIELCH color spaces and lightness parameters, as the inputs, are more suitable for the SWC prediction models. Furthermore, it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy, and the GPR model has the best prediction performance (R2 ≥ 0.95, RMSE ≤ 2.01%, RPD ≥ 4.95, and RPIQ ≥ 6.37). The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction, providing valuable insights for mapping soil moisture fields.

Estimation of water-infiltration rate in Mediterranean sandy soils using airborne hyperspectral sensors

The efficiency of spectral-based assessments of soil attributes using soil spectral libraries (SSLs) covering the visible–near-infrared–shortwave-infrared (VNIR–SWIR: 400–2500 nm) region has been proven in many studies. Nevertheless, as traditional SSLs are commonly developed under laboratory conditions, their application is limited for the assessment of soil surface-dependent properties such as water-infiltration rate (WIR) into the soil profile due to the sampling procedure. Currently, few studies are based on field SSLs for the prediction of physical soil properties. This study used a field-based protocol to measure soil reflectance data and WIR simultaneously in the field, and generate spectral-based decision tree models to predict WIR solely from field spectral measurements using the SoilPRO® assembly. The obtained models were applied to both airborne hyperspectral (HySpex) and satellite multispectral (Sentinel 2) data on a pixel-by-pixel basis to generate raster maps of WIR. The study areas were located in Macedonia (Greece), and were optimal for mapping WIR because the soil crust was well developed, and sites were characterized by bare soils (no vegetation coverage) with a sandy structure. Whereas the WIR map generated with the satellite data was poor due to the low spatial and spectral resolution of Sentinel 2 (20 m, 9 bands), the results obtained with the airborne hyperspectral HySpex sensor (5 m, 408 bands) were satisfactorily validated in the ground-truth stage with good prediction accuracy due to high spatial and spectral resolution. Validation accuracy of the HySpex observations using all field samples gave R2 = 0.68, whereas the predictions of the ground-truth samples that were not part of the calibration stage (field validation group) of the model gave R2 = 0.59. We concluded that these results are favourable for rapid estimation of soil surface conditions and pave the way for a wider spatial view from orbital hyperspectral remote-sensing sensors.

Contrasting impacts of non-native isopods and springtails on ecosystem processes under simulated Antarctic climate conditions

Climate change, coupled with the introduction of non-native organisms, represent major threats to the functioning of ecosystems, especially in species-poor communities such as polar terrestrial ecosystems. In this laboratory study, we quantified the impacts of the non-native springtail Folsomia candida and isopod Porcellio scaber on seed germination and growth of the non-native grass Poa pratensis, and ecosystem respiration. The impacts of invertebrate communities of progressively increasing complexity were assessed, starting with the native springtail Cryptopygus antarcticus alone, followed by C. antarcticus in combination with F. candida or P. scaber and, finally, a community including all three species. The impact of these invertebrate communities were studied in a simulation of contemporary Antarctic soil surface conditions (2 °C) and a +5 °C warming scenario over one growing season.Warming resulted in earlier germination (21 d), 10-fold increased plant biomass, N-content (>5-fold), and higher levels (90%) of ecosystem respiration. Warming also resulted in a 350% increase in C. antarcticus abundance.The presence of the woodlouse P. scaber had the strongest impact on the measured soil and plant variables and this impact was largely irrespective of temperature. Impacts included: delay in seedling emergence (4 d), reduced plant emergence (20%), and higher ecosystem respiration (135%). The presence of both C. antarcticus and P. scaber resulted in 30% higher plant leaf N-content and a reduction in C:N ratio from 21 to 17. The experimental communities containing F. candida showed a 37% reduction in plant biomass under warming. The presence of P. scaber reduced C. antarcticus abundance (94%) but F. candida abundance was unaffected.Our data indicate that non-native invertebrates differ in their ecosystem impacts, with potentially significant consequences for ecosystem functioning and community composition of plants and animals in cold biomes.

Research of soil surface image occlusion removal and inpainting based on GAN used for estimation of farmland soil moisture content

In smart agriculture, soil images are frequently used to estimate soil moisture content (SMC). The soil image can’t fully reflect the real soil surface condition due to the interference of weeds and other obstructions. To solve the occlusion problem, a new occlusion removal idea is proposed: identifying the occluded object, generating corresponding masks to cover it, and restoring the area. Therefore, it involves three parts: image inpainting network research, occlusion recognition and removal system, and practical application in farmland. Soil Surface Occlusion Image Inpainting Network (SOI NET) was proposed for image inpainting based on Generative Adversarial Networks (GAN). A two-stage generation network was designed: Rough Network and Refinement Network which uses the coherent semantic attention (CSA) module to improve the network's context information utilization capability. A soil surface image occlusion removal system was designed, including vehicle-mounted detection system, occlusion target recognition, mask generation and occlusion removal. Indoor and field models for estimating SMC using image color parameters were established. To verify the performance of SOI NET, experiments were carried out to compare with the traditional method and other deep learning methods. The actual farmland occlusion removal experiment was carried out with weeds as the removal object which are the most common occlusion. The results revealed that the system can effectively remove the occlusion from image surface images. In order to verify the improvement effect of occlusion removal image on the detection accuracy of vehicle-mounted terminals, further farmland experiments were conducted to estimate SMC. R2 increased from 0.637 to 0.667 and Root Mean Square Error (RMSE) decreased from 1.916 % to 1.822 % after occlusion removal. SOI NET and soil occlusion removal system can make the processed images reflect the real soil conditions and provide help for soil image analysis.

Effects of rainfall pattern and soil surface roughness on surface–subsurface hydrological response and particle size distribution of red soil slope

Water erosion is strongly affected by rainfall pattern and soil surface roughness (SSR), the available information is inadequate to understand the influences of both on erosion processes and mechanisms. Four simulated rainfall patterns as constant (CR), increasing (IR), decreasing (DR), and rising-falling (RFR) with the same total rainfall amount (180 mm) and duration (180 min) were designed and applied to two soil surface conditions (smooth and rough) with the slope gradient of 15°. The response and generation of surface flow were positively correlated with the initial rainfall intensity of different rainfall patterns. The surface flow generation ranked as: DR > CR > RFR > IR, and the subsurface flow was the opposite. The CR pattern produced the lowest sediment yield, which was around 46.7% of the average soil loss for the varying-intensity rainfall patterns. SSR significantly promoted surface flow generation and sediment production under varying-intensity rainfall patterns, the surface flow coefficient and soil loss rate of the rough surface were 0.6%–13.1% and 3.20–11.27 g m−2 min−1 higher than that of the smooth surface. SSR inhibited the transport of sand particles and promoted the enrichment of clay particles. Under constant and variable rainfall patterns, the average content of clay particles of the rough surface was 2.8% and 0.9% more than that of the smooth surface, respectively. SSR could decrease fine particles and increase coarse particles in the eroded sediment, but only functioned under the rainfall patterns with lower initial rainfall intensity. Through comprehensively considering the effects of rainfall pattern and SSR, the stream power was the best hydraulic parameter for predicting the soil loss rate. The results are beneficial to the investigation of soil erosion in agricultural sloping farmland caused by natural rainfall events.

Soil Attributes Mapping with Online Near-Infrared Spectroscopy Requires Spatio-Temporal Local Calibrations

Building machine learning (ML) calibrations using near-infrared (NIR) soil spectroscopy direct in agricultural areas (online NIR), soil attributes can be fine-scale mapped in a faster and more cost-effective manner, guiding management decisions to ensure the maintenance of soil functions. However, a financially and environmentally unattractive density of 3–5 laboratory soil samples per ha is required to build these calibrations. Since no reports have evaluated if they are reusable or if a new calibration is required for each acquisition, this study’s objective was to acquire online NIR spectra in an agricultural field where ML models were previously built and validated, assessing their performance over time. Two spectral acquisitions were held over a fallow tropical field, separated by 21 days. Soil properties (clay, organic matter, cation exchange capacity, pH, phosphorus, potassium, calcium, and magnesium) were predicted using principal components regression models calibrated with day 1 spectra. Day 1 and day 21 predicted values and maps interpolated by ordinary kriging were compared. Spectra characteristics (morphology, features, and intensity) were evaluated. Predicted values from the two days were not correlated, as no causal relationship was found for the only Pearson’s correlation coefficient (r) significative at 99% (p < 0.01) (calcium, with r = 0.22 in the comparison pairing the nearest neighbors from the two days). For clay, organic matter, and cation exchange capacity, despite their robust prediction on day 1, no significative r values were found, ranging from −0.14 to 0.32, when comparing day 1 with day 21. The maps of the two days presented no similar spatial distribution, hindering their use for management decisions. Soil moisture is a suggested source of variation, but the analysis indicated that it was not the only one, requiring further investigation of the effect of soil surface conditions and environmental variables. Although further investigations should be performed, the results presented suggest that online NIR spectra ML models require spatio-temporal local calibrations to perform properly.

Nitrogen mineralization following fall‐applied compost to subsurface drip‐irrigated processing tomatoes in California

AbstractOrganic wastes diverted from landfills can reduce greenhouse gas emissions and are used as supplemental nutrient sources for agricultural crops after they are composted. In California, compost should be applied in the fall to processing tomatoes (Solanum lycopersicum L.) to avoid delaying field preparation and transplanting in the spring. The objective of this study was to determine soil nitrogen (N) mineralization following fall application of greenwaste compost (GWC) based on the change of mineral N during a 4 month in‐field buried bag soil incubation in 2 prospective subsurface drip‐irrigated processing tomato fields (PTF1 and PTF2). GWC rates of 0, 11, 22, and 34 Mg ha−1 were evaluated in PTF1 and PTF2 in October 2019. Very dry surface soil conditions in fall 2019 and the disruptive procedure used to prepare soil for in‐field incubation using the buried bag method led us to calculate net N mineralization after adjusting for the initial mineral N concentration of the unamended (0 Mg ha−1) control. The adjusted net N mineralization was significantly less in PTF1 (8.6 mg N kg−1) than PTF2 (16.4 mg N kg−1) and the estimated net N mineralization rate of the surface 0–15 cm soil depth following GWC application to PTF1 was less than PTF2 (1.6–1.9 vs. 2.3–3.1 kg N ha−1 week−1). In‐field incubation requires soil disturbance that likely affected N mineralization and additional research should consider the feasibility of using pretreatments or pre‐incubations to account for disruption to antecedent soil conditions.

Improving regional climate simulations based on a hybrid data assimilation and machine learning method

Abstract. The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

Modelling infiltration and infiltration excess: The importance of fast and local processes

AbstractPhysically based distributed hydrological models aim at an adequate representation of hydrological processes, including runoff generation. A significant proportion of runoff is generated through the subsurface, that is, by groundwater flow or unsaturated subsurface stormflow. However, in the case of high rainfall intensity and/or low soil‐surface infiltrability, surface runoff may strongly contribute to total runoff, too, either through saturation excess (“Dunne‐type surface runoff”) or infiltration excess (“Hortonian surface runoff”). Both types of surface runoff can be rather important if antecedent wetness is high and parts of the catchment area are saturated (leading to saturation excess), or if the maximum infiltration rate into the soil surface is less than the actual rainfall intensity (resulting in infiltration excess). Even though the latter process can be very important during high‐intensity rainstorms, both for flood generation and for matter transport linked with surface runoff, an appropriate consideration of this process in catchment models is still challenging. Actually, budgeting between the actual rainfall intensity and the soil surface infiltration capacity is required and there are a number of challenges in the details: First, the “real” rainfall intensity may vary tremendously in time increments much smaller than the time step of the model. The soil surface infiltrability can also be significantly reduced, for example, by crusting, compaction or sealing of the soil surface or through hydrophobic effects. Otherwise, soil infiltrability can be strongly enhanced as a consequence of preferential flow paths/macropores caused by, for example, bioturbations or other voids. Finally, there is a high variability of such soil surface features at a small spatial scale, below the typical spatial modelling unit. We present observational data and approaches to deal with these challenges. We show results from combined infiltration/infiltration‐excess experiments and observations at three spatial scales. Then, we present a model approach based on a double‐porosity soil enabling the combined modelling of high infiltration rates and dampened soil moisture distribution after termination of infiltration, as observable in the field. Furthermore, we present an approach to model the effects of soil surface conditions on actual infiltration capacity. These approaches improved the plausibility and explanatory power of the model concerning surface runoff generation and soil moisture dynamics. For instance, model results at the plot and hillslope scales show that it is possible to simulate high infiltration rates jointly with a relatively slow movement of moisture within the soil matrix, field phenomena often observed in the case of heavy rainfall. We also simulate non‐linear and space–time variability effects of soil surface conditions, which can be important for flood generation.

Mean‐weight diameter of aggregation as affected by initial screen size of two fine‐textured soils

AbstractSoil aggregation is considered a key indicator of soil health to protect soil against erosion, enhance organic C storage, and offer habitat for soil organisms. Various methods to assess aggregation may change interpretations of management, and therefore should be cross‐calibrated. We assessed the impact of initial sieve opening size (8 or 4.75 mm) prior to determination of dry‐stable and water‐stable mean‐weight diameter (MWD) from two fine‐textured soils—a Rhodic Hapludox from São Paulo, Brazil and a Rhodic Kanhapludult from North Carolina, United States. Both soils were subjected to management expected to alter surface soil conditions. As expected, initial sieving through 8 mm led to greater dry‐stable MWD (3.37 ± 0.60 mm) than initial sieving through 4.75 mm (1.94 ± 0.28 mm). However, soil stability index (water‐stable MWD/dry‐stable MWD) was not affected by initial sieve size opening (0.56 ± 0.13 mm mm−1 under both initial sieve openings). Management interpretations were consistent with both approaches as well, and in particular to detect the strong depth effect on water‐stable MWD (i.e., declining with depth). Water‐stable macroaggregates had 32% ± 25% greater C concentration than microaggregates; similarly under both initial sieving conditions. Soil stability index when initially sieved through 4.75 mm was highly associated with aggregate stability of 1–2‐mm sized dry aggregates, which is a more common procedure. We conclude that passing soil through a screen with 4.75‐mm openings to conduct a diversity of soil analyses can be appropriate for obtaining reasonable estimates of and interpretations about surface soil aggregation.

Leaching of phthalate acid esters from plastic mulch films and their degradation in response to UV irradiation and contrasting soil conditions.

Phthalate acid esters (PAEs) are commonly used plastic additives, not chemically bound to the plastic that migrate into surrounding environments, posing a threat to environmental and human health. Dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP) are two common PAEs found in agricultural soils, where degradation is attributed to microbial decomposition. Yet the impact of the plastic matrix on PAE degradation rates is poorly understood. Using 14C-labelled DBP and DEHP we show that migration from the plastic matrix into soil represents a key rate limiting step in their bioavailability and subsequent degradation. Incorporating PAEs into plastic film decreased their degradation in soil, DBP (DEHP) from 79% to 21% (9% to <1%), over four months when compared to direct application of PAEs. Mimicking surface soil conditions, we demonstrated that exposure to ultraviolet radiation accelerated PAE mineralisation twofold. Turnover of PAE was promoted by the addition of biosolids, while the presence of plants and other organic residues failed to promote degradation. We conclude that PAEs persist in soil for longer than previously thought due to physical trapping within the plastic matrix, suggesting PAEs released from plastics over very long time periods lead to increasing levels of contamination.