Soil Moisture Patterns Research Articles

Multiday Soil Moisture Persistence and Atmospheric Predictability Resulting From Sahelian Mesoscale Convective Systems

AbstractSkill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales.

When are you measuring soil β‐glucosidase activities in cropping systems?

AbstractIn situ soil respiration is driven by annual patterns of temperature and soil moisture, but what about extracellular enzyme activities responsible for depolymerizing soil organic matter? We conducted biweekly measurements of potential soil β‐glucosidase activities during a 4‐month period from March soil thawing through July in annually cropped field plots in eastern South Dakota. Our objective was to determine the best sampling time to resolve the effects of crop rotational diversity on soil microbial activities. Potential β‐glucosidase activities were elevated immediately following soil thaw, peaked in May, and declined to their lowest value in mid‐summer. Temperature and precipitation had no value in predicting enzyme activities; however, enzyme activities were affected by crop rotational diversity and responded to current crop and previous crop. These findings are pertinent to the use of soil extracellular enzymes in soil health assessments and as indicators of microbial substrate preference with implications for soil carbon stabilization.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

Soil moisture and water redistribution patterns in white oak (Quercus alba) saplings and trees in fragmented urban woodlands

In the midwestern United States, models predict extended summer heatwaves and increasingly frequent and prolonged drought conditions. In the Chicago region, the potential for large-scale mortality of white oak trees (Quercus alba) coupled with the ongoing decline of white oak sapling recruitment are major concerns for researchers and practitioners. In this study, we determined the sources of water used by mature white oak trees and saplings in three qualitatively different sites within a remnant oak forest in Chicago during the 2021 drought. We investigated soil moisture dynamics (volumetric water content, VWC) and water isotope composition of leaf tissues (δD, δ18O), rainwater, and groundwater. These data were linked to sapling height (proxy for biomass) and ectomycorrhizal (ECM) functional types. We predicted that: (i) mature oak trees use deeper water sources and conducted hydraulic redistribution (HR), and (ii) mature trees shared water with saplings during dry periods via long-distance ECM functional types. Soil moisture decreased progressively from June to October (spring to fall), with August and September having the lowest moisture (<20 % VWC). Following rainfall recharge, temporal patterns of soil moisture showed gravity drainage and then ongoing stair-stepwise drawdown consistent with plant evapotranspiration. Leaf δD and δ18O values in mature trees and saplings were consistent with water uptake from rainfall and subsequent enrichment via evapotranspiration. In two sites, mature trees and saplings demonstrated distinct δD: δ18O slopes, with mature trees more enriched than saplings. In the third site, mature trees and saplings δD: δ18O slopes overlapped but here, the ECM community was dominated by contact-type ECM and sapling height increased with distance from the mature oak. Our findings indicate that HR was not a component of site ecohydrology, and future climate conditions may present increasing challenges for white oak recruitment as both mature trees and saplings compete for limited rainfall-derived soil moisture.

Changes in the Spatial Patterns of Near‐Surface Soil Moisture and Environmental Controlling Factors Before and After a Landslide

ABSTRACTRain‐induced landslides are common natural disturbances in forested headwaters that can strongly alter the spatial distributions of hydrological and environmental features. Although many studies have reported the impacts of landslides on the spatial patterns of soil moisture or their environmental controls, studies that have used detailed in situ data sets collected at the same location before and after a landslide are lacking. This study investigated the spatial distribution pattern of the near‐surface soil moisture and environmental controls, including topographic, soil and vegetation features, in a headwater catchment using ground‐based measurements with high spatial resolution after a landslide in 2016. The data set was compared to measurements taken at the same location before the landslide to explore whether the landslide altered the amount, spatial distribution, or potential controlling factors of near‐surface soil moisture. The mean soil moisture decreased across the site after the landslide, even under conditions of greater rainfall input than before the landslide. Spatial variation in soil moisture decreased in the high‐disturbance area but increased in the low‐disturbance area, although autocorrelation distances of soil moisture changed little. The relationships between the spatial mean and standard deviation of soil moisture markedly changed from a convex‐upward shape to a convex‐downward shape in the highly disturbed area. This indicates that the spatial mean of soil moisture exhibited its greatest spatial variation under moderate conditions before the landslide, with the lowest spatial variation occurring after the landslide. Most of the same controlling factors (i.e., slope gradient, topographic wetness index, vegetation density, soil porosity and saturated hydraulic conductivity) were explored after the landslide, but their influence levels greatly decreased or even disappeared. Thus, the landslide weakened the spatial connectedness between soil moisture and environmental features, which has not yet been restored 6 years after the landslide. We suggest that the connectedness between hydrological responses and environmental features is crucial for restoration. Their connectedness can serve as an indicator to identify the stage of ecological succession from the disturbances of a landslide.

Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces

Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.

Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models.

Modeling the impact of long-term land use changes on deep soil hydrological processes in the Loess Plateau, China

Land use change can significantly affect soil hydrology in arid and semi-arid regions, making it crucial to understand the relationship between vegetation roots and soil moisture. Current models often fail to predict root growth and its impacts on water dynamics accurately. Our work presents a novel model that seamlessly integrates the Community Land Model (CLM) with the Soil & Water Assessment Tool (SWAT). Furthermore, it enhances the root module within the CLM, enabling more accurate simulations of dynamic root depth and distribution across varying tree ages. This improvement particularly considers the crucial processes of dormancy and plant maturity. Soil moisture and root patterns under apple trees of varying ages and in wheat fields on the Loess Plateau was analyzed. Our findings indicate that our dynamic root depth model outperforms traditional static models, and can accurately reflect soil moisture levels with high precision (R2 = 0.80–0.81; Nash-Sutcliffe efficiency (NSE) = 0.65–0.75). In contrast to methods that utilize fixed root depths, dynamic root simulation can provide new insights. As apple orchards mature, the roots of 22-year-old apple trees have been found to reach a depth of 21 m in the soil. Conversely, the maximum root depth of wheat is limited to 1.9 m. This latter finding aligns more closely with the measured root depths, highlighting the accuracy of dynamic simulations. This model reveals that older apple orchards show decreased soil moisture at greater depths (>20 m), contrasting with wheat fields that affect moisture mostly within the top 2 m. Our results underscore the crucial role of dynamic modeling in comprehending root-soil water interactions. Furthermore, they imply that extended orchard cultivation practices can lead to a substantial depletion of deep soil moisture. Specifically, over a period of 1 to 22 years, a water deficit of up to 85 mm yr−1 has been observed. For a 22-year-old forest, the D-D (dynamic distributions of coarse and fine roots) method calculates a significant cumulative deep Soil Water Storage loss. Over the course of 22 years, this loss amounts to 1664 mm, which is almost three times compare to the annual rainfall recorded. Such a large loss has the potential to significant impact on groundwater recharge. This highlights the need for careful consideration in future afforestation efforts to prevent increased soil aridity.

Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for short. The PhyFill method employs a partial convolutional neural network technique to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous spatially daily soil moisture data on a global scale. Three validation strategies are employed: visual inspection through global pattern, simulated missing-region validation, and soil moisture validation with in situ measurements. The results indicated that the reconstructed soil moisture achieved a higher spatial coverage with satisfactory spatial continuity with neighbouring pixels. The simulated validation of the missing regions revealed that the averaged unbiased root mean square difference (ubRMSD) and correlation coefficient (R) were 0.01 m3/m3 and 0.99, respectively versus the gap filled SMAP product. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSD compared with the original SMAP soil moisture data (0.04 m3/m3vs. 0.04 m3/m3). The PhyFill method can generate globally continuous, high accurate soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.

Effects of vegetation types on soil wetting pattern and preferential flow in arid mountainous areas of northwest China

Understanding the mechanisms governing the infiltration of precipitation into soil is crucial in eco-hydrological processes. However, the effect of vegetation types on the wetting front depth and velocity is poorly understood. Here, we analyzed 1234 infiltration events based on a large-scale long-term in-situ soil moisture monitoring network in arid mountainous area of northwest China. Our results show that the proportion of preferential flow was the largest in shrub (52.38 %), followed by alpine meadow (36.55 %), grassland (11.51 %), and barren (0.70 %). The wetting front velocity was consistent with the order of the proportion of preferential flow, with values of 11.42, 4.96, 2.32, and 1.16 cm/h, respectively. The mean velocity of preferential flow events was 2.05 times (0.06–71 times) higher in the shallow soil layer and 3.86 times (0.3–68 times) higher in the deep soil layer compared to matrix flow events. The wetting front depth was shallowest in alpine meadow (14.31 cm), followed by barren (15.70 cm), grassland (18.95 cm), and shrub (39.81 cm). Moreover, the wetting front depth and velocity reach their peak values in summer, primarily influenced by precipitation. Random Forests analysis results demonstrate that preferential flow is the primary factors influencing the profile wetting front depth, with control factors varying across different soil depths, soil water characteristic curve in shallow soil layers, and vegetation in deep soil layers, respectively. Meanwhile, soil organic carbon emerged as the most important factor impacting wetting front velocity. These findings contribute to a deeper understanding of infiltration processes in arid mountainous areas and offer a theoretical foundation for refining and enhancing mountain hydrological models.

Responses of Soil Moisture to Gully Land Consolidation in Asian Areas with Monsoon Climate

Groundwater resources are essential for sustaining ecosystems and human activities, especially under the pressures of climate change. This study employed Electrical Resistivity Tomography (ERT) to assess the impact of Gully Land Consolidation (GLC) engineering on the groundwater hydrological field of small watersheds in the China Loess Plateau (CLP). Results revealed ample subsurface water storage in backfilled areas, primarily migrating along the original river path owing to topographical limitations. Although the distribution patterns of soil moisture in each backfilling block varied slightly, the boundaries of soil moisture content and variation mainly appeared at depths of 8 m and 20 m underground. Significant moisture variation occurred across the 0–20 m underground layers, suggesting the 8–20 m layer could function as a groundwater collection zone in the study area. Human activities could disturb groundwater, altering migration pathways from the original river path. An optimized “Drainage–Conveyance–Barrier” system is proposed to enhance GLC sustainability, involving upstream groundwater level control, midstream soil moisture management, and downstream hydrological connectivity improvement. These findings carry substantial implications for guiding the planning and execution of GLC engineering initiatives. The novelty of this study lies in its application of ERT to provide a detailed spatial and temporal understanding of soil moisture dynamics in the GLC areas. Future research should focus on factors such as soil types and topographical changes for a comprehensive assessment of GLC’s impact on small watershed groundwater hydrology.

Effects of landscape position on perennial biomass and food crop performance in buffer areas

AbstractDue to the environmental consequences of annual‐dominated cropping systems, there is an increasing need to identify agronomic strategies that incorporate perennial crops. One strategy for increasing perennial cover is through the targeted use of annually harvested perennial food and bioproduct crops in buffer strips, which has the potential to create new revenue streams for farmers and substantially mitigate agricultural nutrient pollution from conventional cropping systems. As buffers are typically installed on marginal land, it is critical to understand how landscape position influences the success of perennial crops. The objectives of this study were to determine the relatively early influence of landscape position on the productivity of a variety of perennial crops and their subsequent soil nutrients and soil water storing capabilities. In this experiment, nine perennial (alfalfa, alsike clover, indiangrass, switchgrass, big bluestem, prairie cordgrass, intermediate wheatgrass, high‐diversity polyculture, low‐diversity polyculture) and two annual (corn, soybean) crops were planted across two landscape positions (hillslope and deposition). Plant biomass, plant tissue nitrogen, soil moisture, and soil NO3‐N and NH4‐N were measured and compared at two different locations in Minnesota. Overall, the polyculture mixes, and to some extent intermediate wheatgrass, performed the best with respect to biomass production while also providing ecosystem services across most soil by landscape position combinations tested in this study. However, there were some important findings specific to each soil and landscape position combination, mainly oriented toward biomass production. We also observed temporal patterns in soil moisture and depth‐related patterns in soil N reductions. This study presents an opportunity to optimize the use of perennial crops on marginal agricultural lands for improved environmental and economic benefit.

Correction: Using machine learning to enhance agricultural productivity in Turkey: insights on the importance of soil moisture, temperature and precipitation patterns

Correction: Using machine learning to enhance agricultural productivity in Turkey: insights on the importance of soil moisture, temperature and precipitation patterns

Quantifying soil moisture and evapotranspiration heterogeneity within a solar farm: Implications for stormwater management

As solar energy becomes a cheap source of renewable energy, the number of major utility-scale ground solar panel installations, often called ‘solar farms,’ are growing. With these solar farms often covering hundreds of acres, there is potential for impacts on natural hydrologic processes, including runoff generation and erosion. There is still limited research in this area, and best management practices to address these impacts are variable and not well understood. To fill this gap, we conducted a field investigation of soil moisture patterns, solar radiation, and vegetation at two solar farms in central Pennsylvania, USA that are representative of the complex terrain in the region (e.g., high or variable slopes). Both solar farms also included engineered infiltration basins or trenches that were investigated. Analysis of soil moisture patterns reveals redistribution of water relative to panels, with dripline soil moisture 19 % higher than the reference, and underpanel moisture 25 % lower than the reference, on average at both solar farms over a year. There are also the greatest periods of saturation and runoff generation at the panel driplines. However, an open interspace between panel rows and existing infiltration basins and trenches are playing a critical role in managing runoff. Micrometeorological monitoring indicates reduced evapotranspiration (ET) under panels, with potential underpanel ET 37–67 % lower in summer, and minimal difference in winter. However, a survey of vegetation revealed almost complete ground coverage under panels, which is critical for supporting infiltration and reducing erosion. As solar farms expand to meet important renewable energy goals, it is essential that care be taken to design the landscape to minimize runoff generation and erosion. This work demonstrates that healthy vegetation and well-draining soils can help manage runoff on solar farms; where necessary on more challenging landscapes, engineered stormwater controls can manage any unmitigated runoff.

On the Variability in the Temporal Stability Pattern of Soil Moisture Under Mediterranean Conditions

In recent decades, there has been increasing interest in studying the variability in soil water properties and, specifically, the spatiotemporal variability in the soil water content. This is motivated by the notable theoretical and applied research interests in soil moisture dynamics and their implications for many natural processes. This study aimed to study whether there are variations in the spatial pattern of the temporal stability of soil moisture over time and to analyze the possible influences of certain hydroclimatic (soil water content, precipitation, and evapotranspiration) and soil factors (texture, bulk density, and organic matter content) on these variations. This study was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS, Spain) under Mediterranean conditions, with daily surface moisture data (0–5 cm depth) obtained from 20 stations for the 2006-2023 period. The results showed differences between the average pattern obtained with the 18-year data series and that obtained with the data series for each year. In more than half of the years studied, the representative station differed from that derived from the average pattern. The mean annual precipitation and summer precipitation characteristics seem to be the main factors influencing the variability in the spatial pattern of the temporal stability of soil moisture.

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Precision Turfgrass Irrigation: Capturing Spatial Soil Moisture Patterns with ECa and Drone Data

Empirical models for calculating soil wetting patterns under surface drip irrigation systems: A comprehensive analysis

AbstractAccurate estimation of the surface wetted radius (R) and vertical wetted depth (Z) of wetting patterns in drip irrigation systems is crucial for ensuring that the designs of such systems are effective. This study compared 14 empirical models for estimating drip irrigation wetting patterns by assessing their accuracy using published measurement data and HYDRUS‐2D/3D simulations. The technique for order of preference by similarity to the ideal solution (TOPSIS) was employed to comprehensively rank the models. The results indicate that the empirical model proposed by Fan et al. (2023) (FY) exhibited the highest accuracy when the estimations of R and measured and simulated values were compared, with mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe modelling efficiency (NSE), and percent bias (PB) values of 2.2 cm, 3.4 cm, 0.79, and −7.1% and 5.2 cm, 7.0 cm, 0.50, and −14.1%, respectively. The empirical model proposed by Amin and Ekhmaj (2006) (AE) demonstrated the highest accuracy when the estimations of Z were compared with measured and simulated values, with MAE, RMSE, NSE and PB values of 1.7 cm, 2.0 cm, 0.95 and 4.15% and 4.4 cm, 5.9 cm, 0.82 and 4.7%, respectively. The comprehensive rankings of available models in the present study indicate that the FY model is the most universally applicable, followed by the Li et al. (2022) (LY) model, with comprehensive indices of 0.960 and 0.936, respectively. This research can aid in the selection of universally applicable, reliable and straightforward empirical models for estimating wetting patterns in drip irrigation systems.

Combined effects of rainfall‐runoff events and antecedent soil moisture on runoff generation processes in an upland forested headwater area

AbstractIn this study, we investigate the combined effect of different rainfall‐runoff event types and antecedent soil moisture (ASM) on runoff processes in the headwater elementary discharge area of a small forested upland catchment. The study focuses on (i) the relationship between soil moisture thresholds and runoff generation; (ii) the combined effect of ASM and tree vicinity and (iii) the relationship between different rainfall‐runoff event types and different types of runoff (baseflow and stormflow). The results suggest that ASM has a strong impact on local runoff generation processes. Soil water content (35%–36%) threshold exceedance was related to stormflow runoff generation caused by the activation of quick preferential flow paths in the soil during storm events, especially in the upper and the deepest soil layers. At the same time, unexpected non‐linear increases in baseflow runoff ratios were documented during dry, precipitation‐free, periods and when the 31%–34% soil moisture threshold was exceeded, presumably due to the hydrological connection of farther slope areas during these conditions. Multiple stormflow periods, which exhibited the lowest runoff coefficient, were the most significant events in terms of water retention and soil water recharge due to increased vertical hydrological connectivity enabling more rapid transport to deeper soil layers. However, this rainfall type occurred least often over the study period. The important role of forest stands (individual trees) in creating spatial patterns of soil moisture and preferential infiltration paths to deeper soil layers was also confirmed. These results contribute towards a better conceptualisation of hydrological behaviour in elementary headwater discharge areas and highlight the potential dangers associated with expected increases in extreme weather events.

Effect of Nostoc commune cover on shallow soil moisture, runoff and erosion in the subtropics

Nostoc commune (Nostoc commune Vaucher ex Born & Flahault) is widely distributed in grasslands within the subtropical region. Because of its morphological changes are more sensitive to moisture, it may affect the soil’s response to infiltration-runoff processes, thus significantly influencing soil moisture dynamics, runoff, and soil loss. However, the exact effects of this phenomenon are not yet fully understood. This study established a one-year monitoring experiment of soil moisture, runoff and soil erosion in the karst area of Southwest China from September 2021 to November 2022. Using bare soil (CK) as the control, two treatments of Nostoc commune cover at 100 % coverage (C1) and 50 % coverage (C2) were set up in 3 repetitions to investigate the shallow soil’s response to Nostoc commune cover and rainfall and to quantify the effects of land cover and rainfall type on runoff and soil loss. The results showed that Nostoc commune cover significantly increased the shallow-soil water content, and the effect of increasing soil water content was more significant with increasing coverage. The cumulative soil water increment (CSWI) varied greatly under different types of rainfall due to the high water-absorbing capacity of Nostoc commune. The CSWI showed C1 < C2 < CK under light rain and moderate rain events, while the CSWI showed C1 > C2 > CK under heavy rain and rainstorm events. The temporal pattern of soil moisture changed greatly under different cover treatments and rainfall events. In particular, the response time under different rainfall types varied from 10 min to 250 min. The runoff depth and soil loss in CK were 1.19–2.15 times and 1.92–14.43 times that of C1 and C2, respectively. This indicates that Nostoc commune cover has a significant effect on controlling runoff and soil erosion. In addition, the sensitivity of runoff and soil loss to the maximum 10-min rainfall intensity was higher than that of the maximum 30-min rainfall intensity, which was attributed to the rapid swelling of Nostoc commune in the hydration process, which greatly reduced runoff and soil erosion. This study can contribute to further understanding the hydrological process and soil erosion characteristics under different surface covers in humid karst areas.

Subsurface flow model for estimating 2D wetting pattern in drip irrigation

AbstractNumerical model using 2D Richard’s equation in cylindrical polar coordinate system was developed to assess the soil wetting patterns in a drip irrigation system. A fully implicit finite‐difference scheme was used to solve equations with suitable initial and boundary conditions. The developed model was validated with an experimental result available in the literature, which shows a high level of concordance with low values of RMSE and R2 greater than 0.93 for all discharge rates. Furthermore, the versatility and applicability of the model were demonstrated for complex subsurface conditions considering homogeneous and heterogeneous soil profiles. Water ponding on the surface was observed in clay loam soil, contrasting with deep percolation in sandy loam soil. This underscores the significance of adjusting both discharge rate and irrigation frequency in accordance with prevailing soil conditions.