Soil moisture and hydrology of fairy circles

In a changing climate, understanding how soil hydrology impacts greenhouse gas dynamics will be important for the future management of the soils in the forests on the Canadian Pacific west coast. In a laboratory study, the impact of soil hydrology on potential net methane (CH4) exchange rates and the abundance of methanotrophs (CH4 oxidation) and methanogens (CH4 production) in upland and water-saturated wet soils were investigated. CH4 oxidation and production rates were highest in the wet soils, which corresponded to higher numbers of methanotrophs and methanogens, indicating a link between the microbial abundance and CH4 exchange rates. Also, CH4 production was induced in the upland soils, indicating the presence of methanogens. The optimum soil moisture content for CH4 oxidation was highest in upland soils and the wet soils sustained higher CH4 oxidation rates over a broader range of soil moisture. These results underline the importance of the soil hydrological controls of CH4 oxidation in contrasting soils and forest types, which deserves further attention in field-based studies.

Significance of scale and lower boundary condition in the 3D simulation of hydrological processes and soil moisture variability in a forested headwater catchment

<p>The water-limited region frequently experiences extreme climate variability.  This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.  We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.  The satellite<span> soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.  The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture.  Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.  </span><span>Satellite soil moisture products </span><span>provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales.</span> <span>The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.  </span>This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.</p>

Soil moisture observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate soil moisture simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for soil hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of moisture, momentum, and energy at the earth's surface into soil hydrology models. Until now, the bucket and SiB have been verified by comparison with actual soil moisture data only on a limited basis. In this study, a Simplified SiB (SSiB) soil hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of soil moisture are compared to observations of soil moisture, literally 'ground truth,' snow cover, surface albedo, and net radiation, and with each other. For three of the stations, the SSiB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of soil moisture. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating soil moisture variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring -- SSiB incorrectly puts all the snowmelt into runoff. While producing similar soil moisture simulations, the models produce very different surface latent and sensible heat fluxes, which would have large effects on GCM simulations.

Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.

In a novel study, Getzin et al. (2022) have excavated 500 grasses at four regions of the Namib to systematically investigate the temporal process of how the young grasses die in fairy circles. Based on measurements of the root lengths, statistical testing, and comparative photo documentations the authors showed that sand termite herbivory did not cause the death of the freshly germinated grasses within fairy circles (FCs). Roots of those dead grasses were initially undamaged and even longer than those of the living grasses outside in the vegetation matrix, which is contrary to termite herbivory. The dying annual grasses within FCs had significantly higher root-to-shoot ratios than the vital grasses in the matrix, both of which can be attributed to the same grass-triggering rain event. This indicates that they died from water stress because the desiccating grasses invested biomass resources into roots, trying to reach the deeper soil layers with more moisture, but they failed.Jürgens and Gröngröft (2023) commented on our research findings. Here, we shed light on their statements by investigating the existing data evidence on the Namib fairy circles, which includes a thorough literature review about the proposed termite-feeding mechanism, as well as describing the properties of soil water within and around the FCs. Our review shows that there is no single study to date that has demonstrated with systematic field evidence in the form of root measurements and data from several regions of the Namib that the green germinating grasses within the FCs would be killed by root herbivory of sand termites.We emphasize that the top 10 cm of soil in the FCs is very susceptible to drying out. In this topsoil layer, the freshly germinated grasses with their 10 cm long roots die quickly after rainfall due to lack of water, because these small plants cannot reach and utilize the higher soil moisture content, which is only found in deeper soil layers below the dry topsoil. Based on 400 measurements of soil moisture during the rainy season 2024, we show that the topsoil in the FCs is significantly drier than in the matrix outside. Finally, we show that the soil physical conditions allow a very high hydraulic conductivity that supports the “uptake-diffusion feedback” during the first weeks after grass-triggering rainfall. During the first two weeks, the soil moisture at 20 cm depth ranged for several rainfall events between 9% and 18% within the FCs, hence way above the 6–8% threshold below which the hydraulic conductivity strongly declines. Even 20 days after rainfall, soil moisture was still above 8%. During this biologically active period, new grasses germinate after about five days, the large perennial grasses along the FC edge resprout and strongly draw water with their established root system at 20–30 cm depth, and the freshly germinated grasses in the FCs desiccate and die within 10–20 days. With our continuous soil moisture measurements, we argue that the quickly greening and competitively superior grasses on the FC edge, as well as the vital matrix grasses, draw soil water from the FCs. This rapid depletion of soil water and drying out of the topsoil leads to the death of the new grasses in the fairy circles.

<p>The EFRE-Project BigData@Geo, founded by the European Union, aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as the public. As recent regional climate models are not dealing with the necessary spatiotemporal resolution the model REMO will be developed in the project‘s frame in cooperation with the Climate Service Center Germany (GERICS).</p><p>For these very high resolutions, besides improvements like the non-hydrostatic atmosphere, higher resolved static land surface parameters, and land use land cover changes, etc., realistic modeling of the soil hydrology becomes absolutely necessary. Therefore, REMO is extended by a 5-layer soil scheme which is a first step to overcome restrictions of the recently used soil hydrology scheme due to the included vertical water flow. Furthermore, the current work also aims to implement lateral water flows between grid cells because this is the only way to model the soil hydrology appropriate to the project‘s question.</p><p>The current model version of REMO includes a bucket scheme that treats the soil hydrology as a single layer. The soil depth is equal to the rooting depth and, thus, depends on the overlying vegetation class. Consequently, the whole soil moisture of the soil is available for transpiration. Evaporation only occurs if the soil moisture reaches at least 90 % of the field capacity.</p><p>The 5-layer scheme has 5 layers with increasing thicknesses for deeper layers. The maximum depth of the soil is at approximately 10 m or the depth of the bedrock. Due to the existence of water below the rooting zone and the processes of capillary rise and percolation more water becomes available for transpiration compared to the bucket scheme. Furthermore, evaporation only occurs if the uppermost layer contains soil moisture which is a more realistic process representation as well.</p><p>First results of the comparison of the two schemes and with observation data in the EURO-CORDEX region and a german subregion are presented. We also show some sensitivity studies of the current improvements to the parameterizations of the 5-layer scheme which are necessary for the goal of incorporation of the lateral flow.</p>

Soil moisture evidence of self-restoration in coal mining subsidence area of Shendong Mining area: Cognition based on stable isotopes

HighlightsA novel high-frequency dataset of streamflow, nitrate-N concentration, and soil moisture at depths throughout and below the root zone highlighted tight connectivity between soil moisture dynamics and nitrate-N concentrations spanning event to seasonal timescales.A TELM model successfully predicted both flow rate and nitrate exports from these complex systems because the time lag associated with the soil conditions was represented in model training.The inclusion of soil moisture and temperature data below the effective root zone was important to accurately capture the storm event hysteresis dynamics observed in the exported nitrate signals. Abstract. Efforts to reduce nitrogen contributions from karst agroecosystems have had variable success, in part due to an incomplete understanding of nitrogen source, fate, and transport dynamics in karst watersheds. Recent advancements in environmental sensors and data-driven artificial intelligence models may be useful in improving our understanding of system behavior and the linkages between soil hydrologic processes and karst nitrate loading dynamics. We collected 35 months of high-resolution streamflow, nitrate-N concentration, soil moisture and temperature (from 10-100 cm depths), and meteorological data in a karst agricultural watershed in the Inner-Bluegrass region of Central Kentucky. Two-layer extreme learning machine (TELM) models were developed to predict nitrate-N concentrations and flow rates as a function of meteorological and soil parameter inputs. Results suggest tight linkages between soil moisture gradients at different depths and nitrate-N concentrations at the watershed outlet. TELM modeling results supported visual observations from the high-frequency data and suggest that inclusion of both soil moisture and temperature parameters at all soil depths improved predictions of both flow rate and nitrate-N concentration (with optimal NSE values of 0.93 and 0.94, respectively, when all inputs were considered). Hysteresis analysis suggested that inclusion of the deepest soil layer (100 cm) was necessary to predict hysteresis observed during storm events. The findings of the study highlight the importance of variable activation of matrix waters in preferential flows throughout events and seasons and its subsequent impacts on nitrate-N concentrations. Results suggest that management models should incorporate vertical variability in soil hydrology to accurately characterize nitrate source and transport dynamics. Further, the results of hysteresis analysis underscore the importance of inclusion of hysteresis indices, in addition to typical model evaluation statistics, to ensure accurate representation of nutrient flow pathways. Keywords: Extreme learning machine, Karst agroecosystem, Nitrate, Water resources.

Soil structure is a crucial constituent influencing soil organic richness, rooting systems, and soil moisture conservation. Adding biochar to the soil, which directly affects aggregation, can significantly alter the soil moisture status. The extent of this impact is influenced by the temperature at which pyrolysis biochar is formed. The impact of biochar derived from wheat straw made at 350, 450, 550, and 650 °C (B350, B450, B550, B650) on soil aggregation and moisture retention was evaluated in this study. Based on the results, B550 had the largest mean weight diameter, most water-stable aggregates, and highest available water content compared to the control, with increases of 235%, 39% and 166% compared to the control. On the other hand, B350 was identified as the weakest treatment, with no significant difference from the control. Using B550 and B650 significantly reduced the soil bulk density by 13% and 12% compared to the control. Therefore, the formation of micro-aggregates, the development of soil porosity, and the subsequent increase in soil available water are unavoidable during the addition of B550. The change in the hydrophilic character of biochar and the attainment of an optimal oxygen/carbon ratio with pyrolysis degradations is a critical factor in soil hydrology issues.

This paper investigates the use of soil moisture data from satellites and a hydrological model as inputs to a simplified CH4 emission model (MeSMOD) for estimating CH4 emissions from boreal and pan-Arctic regions between 2015 and 2021. MeSMOD is calibrated using FLUXNET—CH4 sites and the predictive performance is evaluated using several metrics, including the Nash-Sutcliffe efficiency (NSE). Using satellite soil moisture with 100 m resolution, MeSMOD has the highest performance (NSE = 0.63) compared with using satellite soil moisture of 10 km and hydrological model soil moisture of 10 km and 50 km (NSE = 0.59, 0.56, and 0.53, respectively) against site-level CH4 flux. This study has upscaled the estimates to the pan-Arctic region using MeSMOD, resulting in comparable mean annual estimates of CH4 emissions using satellite soil moisture of 10 km (33 Tg CH4 yr−1) and hydrological model soil moisture of 10 km (39 Tg CH4 yr−1) compared with previous studies using random forest technique for upscaling (29.5 Tg CH4 yr−1), LPJ-wsl process model (30 Tg CH4 yr−1), and CH4 CAMS inversion (34 Tg CH4 yr−1). MeSMOD has also accurately captured the high methane emissions observed by LPJ-wsl and CAMS in 2016 and 2020 and effectively caught the interannual variability of CH4 emissions from 2015 to 2021. The study emphasizes the importance of using high-resolution satellite soil moisture data for accurate estimation of CH4 emissions from wetlands, as these data directly reflect soil moisture conditions and lead to more reliable estimates. The approach adopted in this study helps to reduce errors and improve our understanding of wetlands’ role in CH4 emissions, ultimately reducing uncertainties in global CH4 budgets.

Knowledge of soil moisture conditions is important for modeling soil temperatures, as soil moisture influences the thermal dynamics in multiple ways. However, in permafrost regions, soil moisture is highly heterogeneous and difficult to model. Satellite soil moisture data may fill this gap, but the degree to which they can improve permafrost modeling is unknown. To explore their added value for modeling soil temperatures, we assimilate fine‐scale satellite surface soil moisture into the CryoGrid‐3 permafrost model, which accounts for the soil moisture's influence on the soil thermal properties and the surface energy balance. At our study site in the Canadian Arctic, the assimilation improves the estimates of deeper (>10 cm) soil temperatures during summer but not consistently those of the near‐surface temperatures. The improvements in the deeper temperatures are strongly contingent on soil type: They are largest for porous organic soils (30%), smaller for thin organic soil covers (20%), and they essentially vanish for mineral soils (only synthetic data available). That the improvements are greatest over organic soils reflects the strong coupling between soil moisture and deeper temperatures. The coupling arises largely from the diminishing soil thermal conductivity with increasing desiccation thanks to which the deeper soil is kept cool. It is this association of dry organic soils being cool at depth that lets the assimilation revise the simulated soil temperatures toward the actually measured ones. In the future, the increasing availability of satellite soil moisture data holds promise for the operational monitoring of soil temperatures, hydrology, and biogeochemistry.

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

The recovery of soil void space through infiltration and evapotranspiration processes within green stormwater infrastructure (GSI) is key to continued hydrologic function. As such, soil void space recovery must be well understood to improve the design and modeling and to provide realistic expectations of GSI performance. A novel conceptual framework of soil moisture behavior was developed to define the soil moisture availability at pre-, during, and post-storm conditions. It uses soil moisture measurements and provides seven critical soil moisture points (A, B, C, D, E, F, F″) that describe the soil–water void space recovery after a storm passes through a GSI. The framework outputs a quantification of a GSI subsurface hydrology, including average soil moisture, the duration of saturation, soil moisture recession, desaturation time, infiltration rates, and evapotranspiration (ET) rates. The outputs the framework provide were compared to the values that were obtained through more traditional measurements of infiltration (through spot field infiltration testing), ET (through a variety of methods to quantify GSI ET), soil moisture measurements (through the soil water characteristics curve), and the duration of saturation/desaturation time (through a simulated runoff test), all which provided a strong justification to the framework. This conceptual framework has several applications, including providing an understanding of a system’s ability to hold water, the post-storm recovery process, GSI unit processes (ET and infiltration), important water contents that define the soil–water relationship (such as field capacity and saturation), and a way to quantify long-term changes in performance all through minimal monitoring with one or more soil moisture sensors. The application of this framework to GSI design promotes a deeper understanding of the subsurface hydrology and site-specific soil conditions, which is a key advancement in the understanding of long-term performance and informing GSI design and maintenance.

The knowledge of spatio-temporal dynamics of soil moisture within the catchment is very important for rainfall–runoff modelling in flood forecasting. In this study the comparison between remotely sensed soil moisture and soil moisture estimated from the SHETRAN hydrological model was performed for small and flashy Jičinka River catchment (75.9 km2) in the Czech Republic. Due to a relatively coarse spatial resolution of satellite data, the satellite soil moisture data were downscaled, by applying the method developed by Qu et al. (2015). The sub-grid variability of soil moisture was estimated on the basis of the mean soil moisture for the grid cell and the known hydraulic soil properties. The SHETRAN model was calibrated and verified to the observed streamflow hydrographs at the catchment outlet. The good correlation between the two different soil moisture information was obtained according to the majority of applied criteria. The results of the evaluation criteria indicate that the downscaled remotely sensed soil moisture data can be used as additional criteria for the calibration and validation of hydrological models for small catchments and can contribute to a better estimation of parameters, to reduce uncertainties of hydrological models and improve runoff simulations.