Soil Moisture Heterogeneity Research Articles

Characterizing rapid infiltration processes on complex hillslopes: Insights from soil moisture response to rainfall events

Evaluating the soil moisture response characteristics to rainfall events is valuable for understanding eco-hydrological effects in the context of global climate change. However, the effects of soil thickness and rainfall characteristic indicators on soil moisture responses remain unclear, especially in the karst hillslopes characterized by highly complex topography. This study examined the soil moisture response to 35 rainfall events at 5-min intervals by deploying 20 sets of monitoring devices across two hillslope plots (5 × 20 m) with distinct mean soil thicknesses. The response metrics included the soil moisture response time (Tp2p) and wetting front velocity (Vwf), whereas indicator factors considered were soil thickness distribution, slope position, and rainfall characteristics. Overall, the results showed that Tp2p increased from downslope to upslope, as well as from the surface to deep layers. Surprisingly, the mean Vwf (1373 mm h−1) in this study site was significantly higher than that in non-karst regions (17–610 mm h−1). This suggests that rainwater can rapidly infiltrate the soil profile and contribute to subsurface runoff generation. Rainfall characteristics are the primary controlling factors influencing the soil moisture response to rainfall, with their contribution priority (44.3–63.9 %) higher than that of antecedent soil moisture conditions (26.1–35.2 %). The variation in soil moisture response metrics in shallow soil cover hillslopes was more affected by rainfall indicators (41–64 % vs. 31–47 %), thereby potentially weakening the potential influence of topography and soil properties. These findings highlight the effect of refined monitoring in characterizing soil moisture heterogeneity in response to rainfall and emphasize the potential impact of rapid responses on karst eco-hydrological processes.

Invasive plant species support each other's growth in low-nutrient conditions but compete when nutrients are abundant.

Globally, numerous ecosystems have been co-invaded by multiple exotic plant species that can have competitive or facilitative interactions with each other and with native plants. Invaded ecosystems often exhibit spatial heterogeneity in soil moisture and nutrient levels, with some habitats having more nutrient-rich and moist soils than others. The stress-gradient hypothesis predicts that plants are likely to engage in facilitative interactions when growing in stressful environments, such as nutrient-deficient or water-deficient soils. In contrast, when resources are abundant, competitive interactions between plants should prevail. The invasional meltdown hypothesis proposes that facilitative interactions between invasive species can enhance their establishment and amplify their ecological impact. Considering both hypotheses can offer insights into the complex interactions among invasive and native plants across environmental gradients. However, experimental tests of the effects of soil moisture and nutrient co-limitation on interactions between invasive and native plants at both interspecific and intraspecific levels in light of these hypotheses are lacking. We performed a greenhouse pot experiment in which we cultivated individual focal plants from five congeneric pairs of invasive and native species. Each focal plant was subjected to one of three levels of plant-plant interactions: (1) intraspecific, in which the focal plant was grown with another individual of the same species; (2) interspecific, involving a native and an invasive plant; and (3) interspecific, involving two native or invasive individuals. These plant-plant interaction treatments were fully crossed with two levels of water availability (drought vs. well-watered) and two levels of nutrient supply (low vs. high). Consistent with the stress-gradient and invasional meltdown hypotheses, our findings show that under low-nutrient conditions, the biomass production of invasive focal plants was facilitated by invasive interspecific neighbors. However, under high-nutrient conditions, the biomass production of invasive focal plants was suppressed by invasive interspecific neighbors. When competing with native interspecific neighbors, high-nutrient conditions similarly enhanced the biomass production of both invasive and native focal plants. Invasive and native focal plants were neither competitively suppressed nor facilitated by conspecific neighbors. Taken together, these results suggest that co-occurring invasive exotic plant species may facilitate each other in low-nutrient habitats but compete in high-nutrient habitats.

Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations

Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field.

Effect of Land Use Type on Soil Moisture Dynamics in the Sloping Lands of the Black Soil (Mollisols) Region of Northeast China

This study investigates the spatial and temporal heterogeneity of soil moisture on slopes of China’s northeastern black soil region, focusing on the effects of terrain adjustment and vegetation. Soil moisture dynamics in the 0–60 cm soil layer were measured at 10 cm intervals using the TRIME-PICO64 TDR® device on slopes with similar gradients representing three land use types: transverse ridge tillage (TRT) farmland, terraced fields (TFs) farmland, and pure forest woodland (WL). The results indicate significant variations in soil moisture content and water storage across different land use types in the order of TF > TRT > WL. The study further identified that soil bulk density, porosity, and water-holding indicators were in the order of WL > TF > TRT, inconsistent with the soil moisture results, indicating that soil quality cannot be the sole reason for the differences in moisture. The moisture differences between farmland types (TRT and TF) and WL are substantial, especially during the rainy season. In the rainy season (0–60 cm) and the dry season (30–60 cm), significant differences in moisture content are observed (p < 0.05). Significant differences in moisture content between farmland types are found at 0–40 cm during the rainy season and at 0–10 cm during the dry season. In the rainy season, soil moisture for TRT and TFs first decreases from 26.76% and 30.85% to 22.44% and 25.38%, then slightly increases to 27.01% and 27.07% along the slope. Meanwhile, WL displays the opposite pattern on upper, relatively steep slopes, with soil moisture increasing from 16.66% to 17.81%, and exhibits a pattern of change similar to TRT and TFs on lower, gentler slopes. TFs consistently show higher soil moisture and water storage at all slope positions than TRT and WL. TFs improve soil quality, reduce erosion and sedimentation, and shift the lowest soil moisture content to a lower slope position. During the dry season, soil moisture differences between slope positions for TRT and WL were small. In general, terracing can effectively modulate moisture distribution along slopes, increasing moisture by an average of 0.26~12.43%, while afforestation, despite improving soil quality, leads to an 18.14~31.13% reduction in soil moisture content, with the impact being particularly significant during the rainy season. These findings provide important insights for optimizing land use and ecological construction, including keeping the balance between soil and water conservation, especially for sub-humid slope terrain areas.

Spatial heterogeneity of soil moisture caused by drainage and its effects on cadmium variation in rice grain within individual fields

Paddy drainage is the critical period for rice grain to accumulate cadmium (Cd), however, its roles on spatial heterogeneity of grain Cd within individual fields are still unknown. Herein, field plot experiments were conducted to study the spatial variations of rice Cd under continuous and intermittent (drainage at the tillering or grain-filling or both stages) flooding conditions. The spatial heterogeneity of soil moisture and key factors involved in Cd mobilization during drainages were further investigated to explain grain Cd variation. Rice grain Cd levels under continuous flooding ranged from 0.16 to 0.22 mg kg−1 among nine sampling sites within an individual field. Tillering drainage slightly increased grain Cd levels (0.19–0.31 mg kg−1) with little change in spatial variation. However, grain-filling drainage greatly increased grain Cd range to 0.33–0.95 mg kg−1, with a huge spatial variation observed among replicated sites. During two drainage periods, soil moisture decreased variously in different monitoring sites; greater variation (mean values ranged from 0.14 to 0.27 m3 m−3) was observed during grain-filling drainage. Accordingly, 2.9–3.3-fold variation in soil Eh and 0.55–0.67-unit variation in soil pH were observed among those sites. In the soil with low moisture, ferrous fractions such as ferrous sulfide (FeS) were prone to be oxidized to ferric fractions; meanwhile, the followed generation of hydroxyl radicals involved in Cd remobilization was enhanced. Consequently, soil dissolved Cd changed from 2.97 to 8.92 μg L−1 among different sampling sites during grain-filling drainage; thus, large variation was observed in grain Cd levels. The findings suggest that grain-filling drainage is the main process controlling spatial variation of grain Cd, which should be paid more attention in paddy Cd evaluation.

Effects of Micro-Topography and Vegetation on Soil Moisture on Fixed Sand Dunes in Tengger Desert, China.

Soil moisture is a key factor in arid ecosystems, with local variations influenced by topography and vegetation. Understanding this relationship is crucial for combating desertification. Employing ANOVA, Mean Decrease Accuracy (MDA) analysis from random forest modeling and Structural Equation Modeling (SEM), this study investigates the distribution of soil moisture and its associations with topographic and vegetative factors across four micro-geomorphic units in the Tengger Desert, China. Significant heterogeneity in soil moisture across various layers and locations, including windward and leeward slopes and the tops and bottoms of dunes, was observed. Soil moisture generally increases from the surface down to 300 cm, with diminishing fluctuations at greater depths. Soil moisture peaks in the surface and middle layers on windward slopes and in deep layers at the bottom of dunes, exhibiting an initial rise and then a decline on windward slopes. Topographic (including slope direction and elevation difference) and vegetation (including shrub and herb coverage) factors significantly influence soil moisture across three depth layers. Topographic factors negatively affect soil moisture directly, whereas vegetation positively influences it indirectly, with shrub and herb abundance enhancing moisture levels. These insights inform ecological management and the formulation of soil moisture-conservation strategies in arid deserts. The study underscores customizing sand-binding vegetation to various micro-geomorphic dune units.

Weaker land–atmosphere coupling in global storm-resolving simulation

The debate on the sign of the soil moisture-precipitation feedback remains open. On the one hand, studies using global coarse-resolution climate models have found strong positive feedback. However, such models cannot represent convection explicitly. On the other hand, studies using km-scale regional climate models and explicit convection have reported negative feedback. Yet, the large-scale circulation is prescribed in such models. This study revisits the soil moisture-precipitation feedback using global, coupled simulations conducted for 1 y with explicit convection and compares the results to coarse-resolution simulations with parameterized convection. We find significant differences in a majority of points with feedback that is weaker and dominantly negative with explicit convection. The model with explicit convection is more often in a wet regime and prefers the triggering of convection over dry soil in the presence of soil moisture heterogeneity, in contrast to the coarse-resolution model. Further analysis indicates that the feedback not only between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker, in better agreement with observations. Our findings suggest that coarse-resolution models may not be well suited to study aspects of climate change over land such as changes in droughts and heatwaves.

An improved change detection method for high-resolution soil moisture mapping in permafrost regions

ABSTRACT Soil moisture plays a crucial role in understanding the hydrological cycle and the ecological environment. This research presents an improved change detection method that leverages time series data from Sentinel-1 radar and Sentinel-2 optical sensors (2019–2021) to estimate surface soil moisture. The response of backscatter to soil moisture in bare soil was expressed in a logarithmic form, and the influence function of the normalized difference vegetation index (NDVI) on the backscatter difference was established for various vegetation-covered surfaces. Therefore, the impact of vegetation on backscatter is effectively mitigated, and the resulting change in backscatter relative to bare soil conditions can be obtained. An empirical function is subsequently formulated to ascertain the reference values of soil moisture in each pixel. The retrieval of soil moisture is demonstrated in the Wudaoliang permafrost region of the Qinghai-Tibet Plateau and validated against ground measurements. The retrieval results of the improved change detection method exhibit correlation coefficients ranging from 0.672 to 0.941, with root mean squared errors (RMSE) ranging from 0.031 m 3 / m 3 to 0.073 m 3 / m 3 . Compared to the Soil Moisture Active Passive (SMAP) 9-km product, our new method demonstrates higher correlation (0.898 vs. 0.867) and lower RMSE (0.037 m 3 / m 3 vs. 0.044 m 3 / m 3 ). The soil moisture retrieved from Sentinel shows a strong correlation with the SMAP 9-km soil moisture in the time series, thereby providing a better representation of the region’s soil moisture heterogeneity. Our method demonstrates the feasibility of combining Sentinel-1 and 2 for high-resolution (100 m) soil moisture mapping in permafrost regions.

Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations

Abstract. Coupled surface–atmosphere high-resolution mesoscale simulations were carried out to understand meteorological processes involved in the radiation fog life cycle in a city surrounded by complex terrain. The controls of mesoscale meteorology and microscale soil moisture heterogeneity on fog were investigated using case studies for the city of Ōtautahi / Christchurch, New Zealand. Numerical model simulations from the synoptic to microscale were carried out using the Weather Research and Forecasting (WRF) model and the Parallelised Large-Eddy Simulation Model (PALM). Heterogeneous soil moisture, land use, and topography were included. The spatial heterogeneity of soil moisture was derived using Landsat 8 satellite imagery and ground-based meteorological observations. Nine semi-idealised simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture of about 0.31 m3 m−3, with two other simulations of halved and doubled soil moisture to demonstrate the range of soil moisture impact. Another contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other five simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Analysis using pseudo-process diagrams and accumulated latent heat flux shows significant spatial heterogeneity of processes involved in the simulated fog. Our results showed that soil moisture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when the heterogeneity signal was amplified and/or when the soil moisture was halved and doubled. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture can lead to changes in fog duration. These changes can be more than 50 min, although they are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (10 to 200 km) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale on the order of 100 m to 1 km. Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting.

Enhanced Soil Moisture Retrieval through Integrating Satellite Data with Pedotransfer Functions in a Complex Landscape of Ethiopia

Remotely sensed soil moisture products potentially provide a valuable resource for monitoring agricultural drought and assessing food security. The agriculture dominated countries of Eastern Africa experience high inter-annual variability of rainfall, but the monitoring and assessment of the predominantly rainfed agriculture systems is hindered by an absence of ground-based observations. This study evaluates the accuracy of three soil moisture products: ASCAT SWI 12.5 km, SMAP soil moisture data 9 km (SPL3SMP_E), and enhanced surface soil moisture map derived through integrating ASCAT SWI and Pedotransfer Functions (PTFs) (ASCAT_PTF_SM), in Ethiopia, through comparison with in situ-observed soil moisture datasets. Additionally, a new water retention PTF, developed for Ethiopian soils, is integrated with a high-resolution soil property dataset to enhance the spatial resolution of the soil moisture product. The results show that the new integrated dataset performs better in terms of unbiased root mean square error (ubRMSE = 0.0398 m3/m3) and bias (0.0222 m3/m3) in comparison with ASCAT SWI 12.5 km (ubRMSE = 0.0.0771 m3/m3, bias = 0.1065 m3/m3). SMAP is found to have limitations during the wet season, overestimating soil moisture. The finer spatial resolution of the data allows for a better depiction of heterogeneity of soil moisture across the landscape and can be used to identify water-related issues and improve hydrological models for agricultural water management.

Global estimates of daily evapotranspiration using SMAP surface and root-zone soil moisture

The interplay between soil moisture and evapotranspiration modulates the water available to sustain soil evaporation and influences canopy stomatal conductance controls on vegetation transpiration. Modeling this behavior remains challenging. Indeed, satellite remote sensing based Penman-Monteith (PM) ET models tend not to directly consider soil moisture constraints on evaporation and transpiration due to a lack of consistent soil moisture data. To address this issue, we modified a PM model to include satellite enhanced surface and root zone soil moisture from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. The resulting model was used to produce global 9-km daily ET estimates, including contributing fluxes from soil evaporation, transpiration and evaporation of canopy-intercepted precipitation. The global PM ET estimates were assessed using in situ sap flow and AmeriFlux measurements, upscaled FLUXCOM latent heat flux, as well as against other independent global ET data products (GLEAM, GLDAS, SSEBop, and LandFlux-EVAL). The modelled transpiration showed similar seasonal variation and positive correlation to in situ sap flow measurements available from several forest sites (R2 = 0.85; p < 0.01). When compared against AmeriFlux data, the PM ET estimates showed favorable agreement with annual ET measurements extracted from 34 diverse sites (R2 = 0.58; p < 0.01; RMSE = 227 mm yr−1). The PM 8-day ET results also reflected a similar hemispheric seasonality as the global FLUXCOM record (R2 = 0.94–0.98; p < 0.01), while comparisons against other global ET products showed moderate mean differences of between 49 and 107 mm yr−1 (11–25%) over the global domain. Our PM ET estimates varied up to 52% in response to SMAP surface soil moisture dynamics, displaying stronger surface (0–5 cm depth) than root zone (0–100 cm) soil moisture sensitivity. While PM ET sensitivity to soil moisture was greater in arid climate regions, it was also significant in humid climate zones, with analysis indicating that the inclusion of soil moisture predominantly acts as a sustaining influence on ET, especially in moisture limited drylands. PM ET sensitivity to temperature was stronger in humid forest regions relative to other climate and land cover regimes. Overall, the model results clarify the influence of soil moisture heterogeneity on the global ET pattern as informed by satellite-based estimates of surface and root zone soil moisture. Potential enhancements to the spatial and vertical resolution of soil moisture inputs are expected to enable further ET improvements through more realistic model representation of soil and plant available water.

Abrupt permafrost thaw drives spatially heterogeneous soil moisture and carbon dioxide fluxes in upland tundra.

Permafrost thaw causes the seasonally thawed active layer to deepen, causing the Arctic to shift toward carbon release as soil organic matter becomes susceptible to decomposition. Ground subsidence initiated by ice loss can cause these soils to collapse abruptly, rapidly shifting soil moisture as microtopography changes and also accelerating carbon and nutrient mobilization. The uncertainty of soil moisture trajectories during thaw makes it difficult to predict the role of abrupt thaw in suppressing or exacerbating carbon losses. In this study, we investigated the role of shifting soil moisture conditions on carbon dioxide fluxes during a 13-year permafrost warming experiment that exhibited abrupt thaw. Warming deepened the active layer differentially across treatments, leading to variable rates of subsidence and formation of thermokarst depressions. In turn, differential subsidence caused a gradient of moisture conditions, with some plots becoming consistently inundated with water within thermokarst depressions and others exhibiting generally dry, but more variable soil moisture conditions outside of thermokarst depressions. Experimentally induced permafrost thaw initially drove increasing rates of growing season gross primary productivity (GPP), ecosystem respiration (Reco ), and net ecosystem exchange (NEE) (higher carbon uptake), but the formation of thermokarst depressions began to reverse this trend with a high level of spatial heterogeneity. Plots that subsided at the slowest rate stayed relatively dry and supported higher CO2 fluxes throughout the 13-year experiment, while plots that subsided very rapidly into the center of a thermokarst feature became consistently wet and experienced a rapid decline in growing season GPP, Reco , and NEE (lower carbon uptake or carbon release). These findings indicate that Earth system models, which do not simulate subsidence and often predict drier active layer conditions, likely overestimate net growing season carbon uptake in abruptly thawing landscapes.

Quantitative detection and attribution of soil moisture heterogeneity and variability in the Mongolian Plateau

As an essential state variable of the earth’s terrestrial system, soil moisture (SM) is highly significant regarding hydrological processes, agricultural production, land management in response to issues like soil erosion etc. However, the net effects of different environmental factors on the heterogeneity and variations of SM remain unclear, due to the complex interactions between variables. Indeed, investigating such effects in the highly climate-vulnerable Mongolian Plateau is imperative for water resource management and climate change adaptation. In this study, we analyzed the contributions of different environmental factors on the spatial heterogeneity and variations of SM in the Mongolian Plateau between 1982 and 2020, using a geographic detector model (GDM) and a novel nonlinear Granger causality model. The results of the GDM analysis demonstrated that precipitation and vegetation were the main controls relevant to SM heterogeneity, with their explanatory powers (Q statistics of GDM) found to be higher than 0.67. In two breakpoints—one in the early 1990′s and another in 2007—SM demonstrated a pattern of increase, then a decrease, subsequently followed by an increase (insignificant decrease in SM at 100–289 cm depth only). Precipitation was identified as the Granger causal of SM variations over 33.39–97.65% of the plateau’s vegetated area, leading to the drying up of 40% of its SM. The greatest contribution to SM in 28–289 cm of the plateau was observed as coming from one-month-old precipitation, due to the lag in the manifestation of effects. Further, rising temperatures were found to have an immediate influence (2.73–5.23%) on SM in 14.61–54.90% of the whole plateau’s vegetated area, an impact which was relatively high (>14%) in its northeastern wetting zone. Vegetation change posed a relatively weak effect (<2%) on SM over a limited area of the plateau (27.75–35.42%). Although lesser than the impacts of general climate changes, the contributions of climatic extremes could not be neglected, ultimately accounting for up to 10% of SM changes. In summary, this study provides new insights into the individual contributions of various environmental factors on the spatial heterogeneity and variations of SM in the Mongolian Plateau, offering vital information for policymaking regarding climate change mitigation and adaptation, sustainable use of water resources, and ecological restoration for arid and semi-arid region.

Spatio-Temporal Heterogeneity of Soil Moisture on Shrub–Grass Hillslope in Karst Region

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture.

Multiscale Analysis of Surface Heterogeneity–Induced Convection on Isentropic Coordinates

Abstract This study uses semi-idealized simulations to investigate multiscale processes induced by the heterogeneity of soil moisture observed during the 2016 Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign. The semi-idealized simulations have realistic land heterogeneity, but large-scale winds are removed. Analysis on isentropic coordinates enables the tracking of circulation that transports energy vertically and facilitates the identification of the primary convective processes induced by realistic land heterogeneity. The isentropes associated with upward motion are found to connect the ground characterized by high latent heat flux to cloud bases directly over the ground with high sensible heat flux, while isentropes associated with downward motion connect precipitation to the ground characterized by high sensible heat fluxes. The mixing of dry, warm parcels ascending from the ground with high sensible heat fluxes and moist parcels from high latent heat regions leads to cloud formation. This new mechanism explains how soil moisture heterogeneity provides the key ingredients such as buoyancy and moisture for shallow cloud formation. We also found that the submesoscale dominates upward energy transport in the boundary layer, while mesoscale circulations contribute to vertical energy transport above the boundary layer. Our novel method better illustrates and elucidates the nature of land atmospheric interactions under irregular and realistic soil moisture patterns. Significance Statement Models that resolve boundary layer turbulence and clouds have been used extensively to understand processes controlling land–atmosphere interactions, but many of their configurations and computational expense limit the use of variable land properties. This study aims to understand how heterogeneous land properties over multiple spatial scales affect energy redistribution by moist convection. Using a more realistic land representation and isentropic analyses, we found that high sensible heat flux regions are associated with relatively higher vertical velocity near the surface, and the high latent heat flux regions are associated with relatively higher moist energy. The mixing of parcels rising from these two regions results in the formation of shallow clouds.

Local and landscape environmental heterogeneity drive ant community structure in temperate seminatural upland grasslands.

Environmental heterogeneity is an important driver of ecological communities. Here, we assessed the effects of local and landscape spatial environmental heterogeneity on ant community structure in temperate seminatural upland grasslands of Central Germany. We surveyed 33 grassland sites representing a gradient in elevation and landscape composition. Local environmental heterogeneity was measured in terms of variability of temperature and moisture within and between grasslands sites. Grassland management type (pasture vs. meadows) was additionally included as a local environmental heterogeneity measure. The complexity of habitat types in the surroundings of grassland sites was used as a measure of landscape environmental heterogeneity. As descriptors of ant community structure, we considered species composition in terms of nest density, community evenness, and functional response traits. We found that extensively grazed pastures and within-site heterogeneity in soil moisture at local scale, and a high diversity of land cover types at the landscape scale affected ant species composition by promoting higher nest densities of some species. Ant community evenness was high in wetter grasslands with low within-site variability in soil moisture and surrounded by a less diverse landscape. Fourth-corner models revealed that ant community structure response to environmental heterogeneity was mediated mainly by worker size, colony size, and life history traits related with colony reproduction and foundation. We discuss how within-site local variability in soil moisture and low-intensity grazing promote ant species densities and highlight the role of habitat temperature and humidity affecting community evenness. We hypothesize that a higher diversity of land cover types in a forest-dominated landscape buffers less favorable environmental conditions for ant species establishment and dispersal between grasslands. We conclude that spatial environmental heterogeneity at local and landscape scale plays an important role as deterministic force in filtering ant species and, along with neutral processes (e.g., stochastic colonization), in shaping ant community structure in temperate seminatural upland grasslands.

Wildfire disturbance reveals evidence of ecosystem resilience and precariousness in a forest–grassland mosaic

AbstractForest and grassland ecosystems are sometimes located adjacently within the same climate. In Interior British Columbia, Canada, there are complex forest–grassland mosaics within the Interior Douglas‐fir biogeoclimatic zone. Historically, both grassland and forest ecosystems experienced high‐frequency, low‐severity fire regimes. Since European settlement and introduction of livestock grazing and fire exclusion, trees have encroached on grasslands, and tree densities in forests have increased. In this study, we characterize plant communities and near‐surface soil moisture in forest and grassland sites, and in historical grassland sites affected by tree encroachment. We hypothesized that spatial and temporal patterns of near‐surface soil moisture are reflected in aboveground plant community composition and structure. After initial sampling of soil moisture and plant communities, the study area was burned in a wildfire. Applying a multifactorial approach to comparing adjacent grassland and forest sites, we treated the wildfire event as a natural experiment, sampling post‐wildfire plant species composition and soil moisture, and measuring the severity and spatial heterogeneity of surface burn conditions. Evidence supports the concept of mutually exclusive fire‐reinforced bistable grassland and forest states, with greater spatial heterogeneity of soil moisture and burn severity in forests, and highly uniform patterns of soil moisture, vegetation, and burn severity in grasslands. Areas of forest encroachment on grasslands had understory plant communities dominated by exotic species, while restored grasslands had native bunchgrass cover like typical grasslands of the region. Additionally, there was post‐wildfire divergence of forest‐ and grassland‐associated plant communities. Viewed through a resilience theory conceptual framework, we suggest that ecosystem legacies are reinforcing post‐wildfire ecosystem identity and associated native plant communities. External factors—particularly past heavy livestock grazing and fire suppression—have caused ecosystem precariousness that can be addressed with management actions.

Methane emission from stems of European beech (Fagus sylvatica) offsets as much as half of methane oxidation in soil.

Trees are known to be atmospheric methane (CH4 ) emitters. Little is known about seasonal dynamics of tree CH4 fluxes and relationships to environmental conditions. That prevents the correct estimation of net annual tree and forest CH4 exchange. We aimed to explore the contribution of stem emissions to forest CH4 exchange. We determined seasonal CH4 fluxes of mature European beech (Fagus sylvatica) stems and adjacent soil in a typical temperate beech forest of the White Carpathians with high spatial heterogeneity in soil moisture. The beech stems were net annual CH4 sources, whereas the soil was a net CH4 sink. High CH4 emitters showed clear seasonality in their stem CH4 emissions that followed stem CO2 efflux. Elevated CH4 fluxes were detected during the vegetation season. Observed high spatial variability in stem CH4 emissions was neither explicably by soil CH4 exchange nor by CH4 concentrations, water content, or temperature studied in soil profiles near each measured tree. The stem CH4 emissions offset the soil CH4 uptake by up to 46.5% and on average by 13% on stand level. In Central Europe, widely grown beech contributes markedly to seasonal dynamics of ecosystem CH4 exchange. Its contribution should be included into forest greenhouse gas flux inventories.

Signal contribution of distant areas to cosmic-ray neutron sensors – implications for footprint and sensitivity

Abstract. This paper presents a new theoretical approach to estimate the contribution of distant areas to the measurement signal of cosmic-ray neutron detectors for snow and soil moisture monitoring. The algorithm is based on the local neutron production and the transport mechanism, given by the neutron–moisture relationship and the radial intensity function, respectively. The purely analytical approach has been validated with physics-based neutron transport simulations for heterogeneous soil moisture patterns, exemplary landscape features, and remote fields at a distance. We found that the method provides good approximations of simulated signal contributions in patchy soils with typical deviations of less than 1 %. Moreover, implications of this concept have been investigated for the neutron–moisture relationship, where the signal contribution of an area has the potential to explain deviating shapes of this curve that are often reported in the literature. Finally, the method has been used to develop a new practical footprint definition to express whether or not a distant area's soil moisture change is actually detectable in terms of measurement precision. The presented concepts answer long-lasting questions about the influence of distant landscape structures in the integral footprint of the sensor without the need for computationally expensive simulations. The new insights are highly relevant to support signal interpretation, data harmonization, and sensor calibration and will be particularly useful for sensors positioned in complex terrain or on agriculturally managed sites.

Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast

Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km2 watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast.