Local Soil Moisture Research Articles

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Strong plastic responses in aerenchyma formation in F1 hybrids of Imperata cylindrica under different soil moisture conditions.

Hybrids can express traits plastically, enabling them to occupy environments that differ from parental environments. However, there is insufficient evidence demonstrating how phenotypic plasticity in specific traits mediates hybrid performance. Two parental ecotypes of Imperata cylindrica produce F1 hybrids. The E-type in wet habitats has larger internal aerenchyma than the C-type in dry habitats. This study evaluated relationships between habitat utilisation, aerenchyma plasticity, and growth of I. cylindrica accessions. We hypothesize that plasticity in expressing parental traits explains hybrid establishment in habitats with various soil moisture conditions. Aerenchyma formation was examined in the leaf midribs, rhizomes and roots of two parental ecotypes and their F1 hybrids in their natural habitats. In common garden experiments, we examined plastic aerenchyma formation in leaf midribs, rhizomes and roots of natural and artificial F1 hybrids and parental ecotypes and quantified vegetative growth performance. In the natural habitats where soil moisture content varied widely, the F1 hybrids showed larger variation in aerenchyma formation in rhizomes than their parental ecotypes. In the common garden experiments, F1 hybrids showed high plasticity of aerenchyma formation in rhizomes, and their growth was similar to that of C-type and E-type under drained and flooded conditions, respectively. The results demonstrate that F1 hybrids of I. cylindrica exhibit plasticity in aerenchyma development in response to varying local soil moisture content. This characteristic allows the hybrids to thrive in diverse soil moisture conditions.

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN.

This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET's regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study's implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

Spring biomass burning in Indochina enhances summer Yangtze River Valley rainfall through land–atmosphere interactions

Indochina is one of the regions with the most severe biomass burning (BB) in the world, which produces enormous amounts of atmospheric aerosols, mostly in spring. Moreover, the spring soil moisture anomalies in Indochina have been found to have a significant influence on the summer rainfall in the Yangtze River Valley (YRV). It is still partially unclear how spring BB in Indochina influences the local and regional climate and land–atmosphere interactions. Here, we use regional model experiments and observational data to show that the radiative effect of BB aerosols in Indochina stabilizes the atmosphere and reduces local precipitation and soil moisture. The dry soil in Indochina persists from spring to summer, which warms the land surface and the atmosphere. As a result, the western Pacific subtropical high (WPSH) is stronger and extends westward from spring to summer. This leads to stronger moisture transport to and more precipitation in the YRV (increases of approximately 5% in July and 10% in August). Thus, the effect of BB aerosols on the YRV rainfall is similar to that of the Indochina dry soil anomaly. Additionally, the increase in the YRV summer rainfall caused by the Indochina spring dry soil anomaly is almost doubled when there are BB aerosols compared to that without BB aerosols, suggesting the importance of BB aerosols in regulating the regional land–atmosphere interactions.

Anomalously Darker Land Surfaces Become Wetter Due To Mesoscale Circulations

Abstract“Land radiative management” (LRM)—intentionally increasing land surface albedo to reduce regional temperatures—has been proposed as a form of geoengineering. Its effects on local precipitation and soil moisture over long timescales are not well understood. We use idealized cloud‐permitting simulations and a conceptual model to understand the response of precipitation and soil moisture to a mesoscale albedo anomaly at equilibrium. Initially, differential heating between a high‐albedo anomaly and the lower‐albedo surrounding environment drives mesoscale circulations, increasing precipitation and soil moisture in the surrounding environment. However, over time, increasing soil moisture reduces the differential heating, eliminating the mesoscale circulations. At equilibrium, the fractional increase in simulated soil moisture is up to 1.3 times the fractional increase in co‐albedo (one minus albedo). Thus, LRM may increase precipitation and soil moisture in surrounding regions, enhancing evaporative cooling and spreading the benefits of LRM over a wider region than previously recognized.

Using thermal imagery and changes in stem radius to assess water stress in two coniferous tree species

With a warming climate and greater evaporative demand, many forest ecosystems are increasingly affected by water limitation as prolonged water deficits reduce tree-level growth and survival. Water deficit can be monitored in several ways, including from daily measurements of sap flow or changes in stem radius from automated sensors mounted on individual trees. As an alternative approach, we evaluated the use of airborne thermal imagery from unmanned aerial vehicles as a rapid, scalable tool for assessing tree-level water stress. Plant water stress leads to higher leaf temperatures when soil moisture is low and evaporative demand is high. To detect this response, we modeled the difference between leaf and air temperature (∆T) as a function of local soil moisture, vapor pressure deficit, and wind speed for two tree species, lodgepole pine (Pinus contorta var. latifolia) and white spruce (Picea glauca). We used those same weather and soil conditions to model dendrometer-based measurements of daily changes in internal tree water deficit (∆TWD). While canopy leaf temperature and daily change in tree water deficit showed little direct correlation with one another, these variables both responded to soil moisture, vapor pressure deficit, and wind speed in a manner that reflects responses to water stress as soil became progressively drier over the summer months. The two species showed some differences related to species-specific strategies for drought avoidance. The application of thermal imagery to detect water stress in natural forest ecosystems can improve understanding of how trees experience water stress across species and environmental conditions.

Extreme heatwave over Eastern China in summer 2022: the role of three oceans and local soil moisture feedback

Eastern China experienced persistent regional extreme heatwaves in the summer of 2022, with disparate spatial features and formation mechanisms in different months. We quantitatively assessed the relative contributions of three oceans, i.e. tropical Indian Ocean and Pacific and North Atlantic, and the local soil moisture–temperature feedback using linear regression. The results showed that the monthly mean atmospheric circulation anomalies failed to explain the extreme heatwave in June 2022. The combined contribution of the tropical Indo-Pacific and North Atlantic sea surface temperature anomalies (SSTAs), together with the local soil moisture–temperature feedback, explaining approximately 10% of the temperature anomalies. In July, the tropical Indo-Pacific SSTAs promoted anomalous atmospheric circulation and extreme heat via meridional circulation originating in the Maritime Continent, accounting for approximately 10% of the temperature anomalies, with North Atlantic SSTAs contributing the same percentage by a mid-latitude steady Rossby wave. Local soil moisture–temperature feedback accounted for 42% of the anomalies. The tropical Indo-Pacific SSTAs produced a strong western North Pacific anticyclone in August, but their direct contribution to the temperature anomalies was negligible. The North Atlantic SSTAs contributed 9% of the total via the mid-latitude steady Rossby wave. Local soil moisture–temperature feedback contributed 66%, suggesting that the July heatwave and drought exerted a significant impact on the subsequent August extreme heatwave. Global warming has greatly facilitated extreme heatwaves, accounting for about 30%–40% of these events in summer 2022. These results also suggest that the climatic effects of tropical Indo-Pacific and North Atlantic SSTAs on Eastern China are evident in the month-to-month variation in summer. Our results thus contribute to the understanding and prediction of extreme heatwaves in Eastern China.

Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index

ABSTRACT This study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local characteristics of soil moisture development. An advanced antecedent precipitation index (API) is utilized to generate a global soil moisture product of high temporal resolution with the Global Precipitation Measurement (GPM) Missions Integrated Multi-Satellite Retrievals for GPM (IMERG) as main driver. The resulting global GPM API data set is compared against in situ measurements from the International Soil Moisture Network (ISMN) and is also evaluated against the soil moisture data set from the European Space Agency’s Climate Change Initiative (ESA CCI SM). The study shows that with empirically derived dampening factors the GPM API achieves a mean ubRMSD across the utilized in situ stations in different climates and vegetation zones of 4.68 and a bias of 0.88 . The data set clearly represents the local soil moisture schemes with seasonal variations. When comparing with ESA CCI SM, the GPM API does perform better at the measurement sites concerning bias, correlation and error values. The data set is in most parts negatively biased compared to the ESA CCI SM, however better matches the mean soil moisture at ISMN stations. Overall, the GPM API delivers a very promising global, hourly surface soil moisture product at 0.1 0.1 spatial resolution.

Spatiotemporal variability of soil moisture over Ethiopia and its teleconnections with remote and local drivers

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient r=0.95\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r=0.95$$\\end{document}, rmsd=0.04m3m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$rmsd=0.04{\\mathrm{m}}^{3}{\\mathrm{m}}^{-3}$$\\end{document} with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies.

Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications

We present Multiscale Extrapolative Learning Algorithm (MELA) as a novel artificial-intelligence (AI)-based data extrapolator. MELA is capable of extending temporally limited local hydroclimatic measurements at fine spatial resolution to longer periods, using remotely-sensed hydroclimatic data readily available for longer periods but at coarse spatial resolution. We demonstrate the implementation of MELA to extrapolate the monthly local soil moisture measurements at multiple depths from 2015–2021 to 1958–2021 in a semi-arid region. Such data extrapolators are imperative to generate longer historical data needed to adequately train and test AI models while enhancing the chance of capturing the effects of extreme climates on spatially variable soil moisture. The MELA-extrapolated local soil moisture subsequently allowed the construction of monthly time-series of field-scale soil moisture distributions with a normalized accuracy of 72% and prediction of countywide annual winter wheat yields – using MELA-extrapolated soil moisture data and eXplainable AI (XAI) – with a normalized accuracy of 81%. Furthermore, the XAI model ranked the predictors based on their importance in estimating winter wheat yields, in which the soil moisture near the surface and in the root zone and precipitation totals were found to be more influential than temperature on crop yields in the semi-arid region. The XAI model also unveiled the inflection points of the predictors beyond which crop yields would increase or decrease. Moreover, the AI-based analyses in conjunction with climate projections from global climate models suggest potential reductions in rainfed crop yields in the study area by 2050 and 2100 in the absence of climate-resilient mitigation and adaptation plans.

Evaluation of single-sided nuclear magnetic resonance technology for usage in geosciences

Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T 1) and transverse (T 2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T 1 relaxation data, unbiased SiS NMR results for T 2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below . This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the μm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks.

Hydrological influence on the variation of the terrestrial gravity field in Manaus, Amazonas, Brazil

The influence of groundwater on the hydrological cycle has been studied at large-scale across the Amazon Basin, one of the largest freshwater reservoirs in the world. Considering this Basin's importance in the global hydrological cycle, local studies also are critical. Here, we focus on the local hydrological cycle in Manaus, at the center of the Amazon Basin. We observed the variability of local gravity, fluvial height, soil moisture, precipitation, evapotranspiration and groundwater in the rainy (December–May) and dry (June–November) seasons, during drought (2016) and flood (2017) years. In general, in the beginning of the rainy season, soil moisture is instantaneously influenced by the first precipitation, increasing gravity. After, the soil becomes saturated and the variation in soil moisture achieves maximum values, which are sustained until the end of rainy season. After soil saturation by infiltration, the groundwater starts to be fed by percolation and the water table starts to rise. Thus, variation in local gravity reflects mainly variations in groundwater level. After the dry season starts, the soil loses moisture (soil moisture values decrease), while the gravity signal maintains high values, representing the sustained behavior of the groundwater. Modeling the vertical gravity component influenced by soil moisture and by groundwater results, respectively, in 49.63 nm/s2 and 314.70 nm/s2. In comparison with the gravity measurement of 368.02 nm/s2, we confirm that the groundwater is the main contributor to the gravity signal. We also find a strong correlation between fluvial height and the gravity signal, but demonstrate with 3D modeling that the Negro River height variation does not directly influence the gravity signal. Instead, we explain the similarity in fluvial height and the gravity signal by the connection between fluvial height and groundwater, and the correlation between groundwater and gravity variations.

The relative contribution of large-scale circulation and local soil moisture to summer precipitation over Asian mid-low latitudes

The relative contribution of large-scale circulation and local soil moisture to summer precipitation over Asian mid-low latitudes

The Impact of the Direct Radiative Effect of Increased CO2 on the West African Monsoon

Abstract Projections of future West African monsoon (WAM) precipitation change in response to increasing greenhouse gases are uncertain, and an improved understanding of the drivers of WAM precipitation change is needed to help aid model development and better inform adaptation policies in the region. This paper addresses one of those drivers, the direct radiative effect of increased CO2 (i.e., the impact of increased CO2 in the absence of SST warming and changes in plant physiology). An atmosphere-only model is used to examine both the equilibrium response and the evolution of the change over the days following the instantaneous CO2 increase. In response to the direct radiative effect, WAM precipitation increases due to a weakening of the shallow meridional circulation over North Africa, advecting less dry air into the convective column associated with the monsoon. Changes in the shallow circulation are associated with atmospheric and surface warming patterns over North Africa. A large-scale atmospheric warming pattern, whereby North Africa warms more than the monsoon region, leads to a northward shift in the Saharan heat low. In response to increased precipitation in the Sahel, local soil moisture feedbacks play a key role in determining the low-level circulation change and the location of the intertropical discontinuity. The large-scale warming patterns over North Africa result from differing levels of constraint applied by convective quasi-equilibrium. While this constraint acts strongly in the equatorial WAM region, preventing the region from warming in response to the direct radiative effect, North Africa is not strongly constrained and is therefore able to warm.

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

AbstractAimImproving species distribution models (SDMs) and species abundance models (SAMs) of woody shrubs is critical for predicting biodiversity changes in the Arctic, which is experiencing especially high warming rates. Yet, it remains relatively unexplored if SDMs and SAMs can explain local scale patterns. We aim to identify predictor differences for the distribution versus abundance of two widespread Arctic shrub species with high resolution models and to compare validation approaches to assess the models’ predictive abilities.LocationNuup Kangerlua (NK) and Kangerluarsunnguaq (K), two adjacent fjords in Southwest Greenland.MethodsWe conducted two separate field surveys in either fjord to construct high resolution (~90 m) SDMs and SAMs forBetula nanaandSalix glauca, analysing the predictive influences of local scale climate, topography and soil moisture indicators. We then alternatively trained and validated models in either NK or K fjord and compared these results with the common split‐sample validation approach. Finally, we assessed if including local scale biotic predictors improves SAM performance.ResultsTemperature extremes and precipitation best predicted the distributions of both species, whereas insolation and soil moisture indicators best predicted abundances. Compared to split‐sample validation, both SDM and SAM performance was substantially reduced with separate survey validation. Regardless of validation approach, models performed poor to moderately well, and including local scale biotic parameters improved SAM performance.Main conclusionsSubstantial differences in model performance between validation approaches highlight the usefulness of using a separate survey for validating model predictive performance. We discuss various factors that might have caused poor model performance, such as not capturing all relevant predictors or enough local scale heterogeneity in predictor or response variables. We emphasise the need to include predictors relevant at the spatial scale of study, such as local scale biotic interactions, for improved predictions at high spatial resolution.

Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation.

Changes in Great Plains Low‐Level Jet Structure and Associated Precipitation Over the 20th Century

AbstractThe U.S. Great Plains warm season climate is inextricably linked to the frequency and structure of the region's southerly low‐level jet. In the present‐day climate (1977–2009), low‐level jets are shown to occur on 26%, 46%, and 62% of May–September days in the northern (NGP), central (CGP) and southern (SGP) Great Plains, respectively, and account for at least 26%, 25%, and 36% of those region's precipitation during the same period. A shortcoming of previous research has been a failure to treat upper‐level dynamically coupled, or cyclone‐induced jets, separately from jets that are relatively uncoupled from synoptic flow. Differentiating between jet types is essential to proper mechanistic diagnosis and attribution of jet‐related wind, precipitation, and temperature changes to their local land or remote oceanic forcing. Using a new CERA‐20C objective dynamical jet classification dataset, this study achieves the first quantitative assessment of changes in coupled and uncoupled jets between 1901 and 2010 for NGP, CGP, and SGP. Declines in warm season jet frequency are pinpointed to July–September jets. In the NGP and CGP, both jet types have undergone significant increases in speed and height with concomitant decreases in CAPE and precipitation. NGP uncoupled jet and CGP coupled jet precipitation has decreased by 0.5 and 0.8 mm day−1, respectively, which accounts for 41%–44% of total May–September precipitation decreases between 1905–1937 and 1977–2009. A dynamic situation in which synoptic and local soil moisture changes drive opposite jet responses is discussed.

ГЕОМЕТРИЯ ЛОКАЛЬНЫХ КОНТУРОВ КАПЕЛЬНОГО УВЛАЖНЕНИЯ ПОЧВЫ, ФОРМИРУЮЩИХСЯ В ЮЖНЫХ ЧЕРНОЗЕМАХ

Purpose: to determine empirical dependencies that allow predicting the geometric parameters of local soil moisture contours formed during drip irrigation of southern chernozems. Materials and methods. The empirical basis of the study was the data of experimental measurements of the moisture contour parameters of southern chernozems, formed during drip irrigation, on 10 experimental sites, characterized by: the content of physical clay from 29.0 to 71.1 % of the dry soil mass (DSM), the field moisture capacity from 20.0 to 30.3 % of DSM, soil density from 1.29 to 1.4 t/cub. m. Soil characteristics were studied using the methods of N. A. Kachinsky, “flooded areas” and “cutting ring”. The moisture content of soil samples was determined according to GOST 28268-89. Results. Analytical processing of 10 experimental moisture contour outlines made it possible to obtain a system of empirical dependencies for determining the outline, diameter, horizontal projection area and volume of the local soil moisture contour. Dependencies take into account soil characteristics – the content of physical clay, the field moisture capacity and bulk density, as well as the technological parameters of drip irrigation – dripper consumption, moisture depth, levels of pre-irrigation and post-irrigation soil moisture and irrigation rate. The obtained dependencies were tested on the experimental data of other researchers. As a result, the deviations of the calculated values of the moisture contour diameter from the experimental ones did not exceed 11.3 % and averaged 4.8 %. Forecasting the area of the horizontal projection showed a deviation of the calculation from the experiment on average 8.9 %, and for the volume of the moisture contour – 8.8 %. Conclusions. Empirical dependences that allow predicting the shape, linear, areal and volumetric dimensions of soil moisture contours formed in southern chernozems during drip irrigation have been established. The results of testing help conclude that the proposed dependencies are acceptable for practical use.

Environmental and anthropogenic gravity contributions at the Þeistareykir geothermal field, North Iceland

Continuous high-resolution gravimetry is increasingly used to monitor mass distribution changes in volcanic, hydrothermal or other complex geosystems. To quantify the often small target signals, gravity contributions from, e.g. atmospheric mass changes, global and local hydrology should be accounted for. We set up three iGrav superconducting gravity meters for continuous monitoring of the Þeistareykir geothermal field in North Island. Additionally, we installed a set of hydrometeorological sensors at each station for continuous observation of local pressure changes, soil moisture, snow and vertical surface displacement. We show that the contribution of these environmental parameters to the gravity signal does not exceed 10 µGal (1 µGal = 10–8 m s−2), mainly resulting from vertical displacement and snow accumulation. The seasonal gravity contributions (global atmosphere, local and global hydrology) are in the order of ± 2 µGal at each station. Using the environmental observations together with standard gravity corrections for instrumental drift and tidal effects, we comprehensively reduced the iGrav time-series. The gravity residuals were compared to groundwater level changes and geothermal mass flow rates (extraction and injection) of the Þeistareykir power plant. The direct response of the groundwater levels and a time-delayed response of the gravity signal to changes in extraction and injection suggest that the geothermal system is subject to a partially confined aquifer. Our observations indicate that a sustainable “equilibrium” state of the reservoir is reached at extraction flow rates below 240 kg s−1 and injection flow rates below 160 kg s−1. For a first-order approximation of the gravity contributions from extracted and injected masses, we applied a simplified forward gravity model. Comparison to the observed gravity signals suggest that most of the reinjected fluid is drained off through the nearby fracture system.

Weather- and human-related shifts in feeding conditions promote the use of built-up areas by an avian opportunist

Human activities benefit a range of animal species, the resulting presence of which in cities can have negative societal consequences. One example are food subsidies, which buffer natural variation in food availability and allow these species to maintain larger populations. These buffers will likely gain importance under future environmental change whereby natural food sources become decreasingly available. To inform on the current importance of different habitats for a bird reliant on human-made food subsidies (Lesser Black-backed Gull Larus fuscus), and its possible population response toward changes in climate and the availability of these subsidies, we characterized population-level short-term responses to variation in drivers of local food availability, both natural (weather related) and anthropogenic (fisheries activity). We expected foraging effort to vary in relation to local wind speed and soil moisture, as well as to the alternation of fisheries activity between weekdays and weekends. Individuals were predicted to adjust their foraging habitat use in response to these environmentally driven variations in effort. To this end, we analyzed GPS tracking data of 45 breeding individuals, between 2013 and 2018, nesting in the Port of Zeebrugge, Belgium. Effort was approximated as the energy expenditure rate per trip, the daily time spent away from the colony and the trip frequency, which were analyzed by means of linear mixed effects models. Habitat use per trip was compared between marine, agricultural fields and built-up areas (cities, industry and cattle farms), in a multinomial logistic model. Marine areas and agricultural fields were most frequently exploited, but all considered stressors (wind, dry conditions and inactivity of fisheries) resulted in a higher use of built-up areas. Stronger winds increased the energetic cost of foraging at sea, and thus diminished the use of marine areas, as also did the inactivity of fisheries in weekends. Dry conditions diminished the use of fields and decreased trip frequency. Built-up areas thus constitute a buffer for the variation in food availability at sea and in agricultural fields. The expected increase in frequency and severity of extreme weather events (storms and drought) under global change, combined with the disappearance of discards, may therefore result in a long-term increase in the use of urban habitats by opportunistic large Gull species.