Soil Moisture Conditions Research Articles

The importance of variance and microsite conditions for growth and hydraulic responses following long-term rewetting in pedunculate oak wood

Key messageEarly rewetting influenced growth variability and hydraulic uniformity in Pedunculate oak wood on disturbed peatland. Long-term study highlighted vessel widening's importance in adapting to water availability changes.Pedunculate oak (Quercus robur L.) is a widely recognized flood-tolerant tree that thrives on fertile and moist soil conditions, such as on or close to peatland ecosystems. In the frame of climate change counteracting policies, rewetting peatland ecosystems is gaining increasing interest, while the ecological consequences are not always clear. Whereas the effect of flooding on wood anatomical traits of pedunculate oak is widely documented, little is known about the effect of permanent rewetting. In this study, we investigated the wood anatomical responses of 12 pedunculate oak trees located on a formerly drained peatland in NE Germany, that experienced flooding and a consequent rewetting. Wood anatomical traits were analyzed via CARROT, a tool that employs the accuracy and efficiency of artificial intelligence to identify tree rings and vessels. Growth anatomical traits (e.g., tree ring width) showed a sudden increase after the rewetting started in 1995, while hydraulic traits (Dh and Ks) displayed a decreasing trend only after the rewetting process was fostered by the opening of an artificial canal in 2004. Variance analysis highlighted subtle changes in the trait’s distribution over time: high soil water content triggered variability in the growth anatomical traits and, simultaneously, homogeneity in the hydraulic traits. Results suggest the relevance of the “vessel widening” mechanisms to develop coping strategies in response to the later stage of the rewetting, and pose relevant insights concerning the importance of specific site conditions for the implementation of rewetting policies in peatlands with presence of pedunculate oak.

A vakondjáratok hatása az extenzív gyep talajfaktor mutatóira

We investigated the effects of mole walks in extensive grassland soil, focusing on changes in soil factor indicators, 3-3 times per year, in 2022-2023, in Karcag. Based on our results, we found that the drier year 2022 had verifiably higher carbon emission values, only on an annual average, than the wetter year 2023. We found no verifiable differences in soil temperature and soil moisture conditions. Based on our data, it can be concluded that the mammal of choice for the year 2023, the mole, does not have a significant impact on the carbon dioxide emissions of the grassland soil. Further studies in other habitat conditions are definitely warranted.

To Harvest or not to Harvest: Management Intensity did not Affect Greenhouse Gas Balances of Phalaris Arundinacea Paludiculture

The cultivation of flooding-tolerant grasses on wet or rewetted peatlands is a priority in climate change mitigation, balancing the trade-off between atmospheric decarbonisation and biomass production. However, effects of management intensities on greenhouse gas (GHG) emissions and the global warming potential (GWP) are widely unknown. This study assessed whether intensities of two and five annual harvest occurrences at fertilisation rates of 200 kg nitrogen ha− 1 yr− 1 affects GHG exchange dynamics compared to a ‘nature scenario’ with neither harvest nor fertilisation. Fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), using opaque and transparent chambers, were measured on a wet fen peatland with a mean water table depth of -10 cm below soil surface. Overall, no treatment effect was found on biomass yields and GHG emissions. Annual cumulative CH4 emissions were low, ranging between 0.3 and 0.5 t CO2-C eq ha− 1 yr− 1. Contrary to this, emissions of N2O were high, ranging between 1.1 and 1.5 t CO2-C eq ha− 1 yr− 1. For magnitudes of CH4 and N2O, soil moisture conditions and electrical peat properties were critical proxies. Atmospheric uptake of CO2 by net ecosystem exchange was higher for the treatments with management. However, this benefit was offset by the export of carbon in biomass compared to the treatment without management. In conclusion, the results highlighted a near-equal GWP in the range of 10.5–11.5 t CO2-C eq t ha− 1 yr− 1 for all treatments irrespectively of management. In a climate context, a restoration scenario but also intensive paludiculture practices were equal land-use options.

Vegetation patterns associated with nutrient availability and supply in high-elevation tropical Andean ecosystems

Abstract. Plants absorb nutrients and water through their roots and modulate soil biogeochemical cycles. The mechanisms of water and nutrient uptake by plants depend on climatic and edaphic conditions, as well as the plant root system. Soil solution is the medium in which abiotic and biotic processes exchange nutrients, and nutrient concentrations vary with the abundance of reactive minerals and fluid residence times. High-altitude ecosystems of the tropical Andes are interesting for the study of the association between vegetation, soil hydrology, and mineral nutrient availability at the landscape scale for different reasons. First of all, because of low rock-derived nutrient stocks in intensely weathered volcanic soils, biocycling of essential nutrients by plants is expected to be important for plant nutrient acquisition. Second, the ecosystem is characterized by strong spatial patterns in vegetation type and density at the landscape scale and hence is optimal to study soil-water–vegetation interactions. Third, the area is characterized by high carbon stocks but low rates of organic decomposition that might vary with soil hydrology, soil development, and geochemistry, all interconnected with vegetation. The páramo landscape forms a vegetation mosaic of bunch grasses, cushion-forming plants, and forests. In the nutrient-depleted nonallophanic Andosols, the plant rooting depth varies with drainage and soil moisture conditions. Rooting depths were shallower in seasonally waterlogged soils under cushion plants and deeper in well-drained soils under forest and tussock grasses (>100 cm). Vegetation composition is a relevant indicator of rock-derived nutrient availability in soil solutions. The soil solute chemistry revealed patterns in plant-available nutrients that were not mimicking the distribution of total rock-derived nutrients nor the exchangeable nutrient pool but clearly resulted from strong biocycling of cations and removal of nutrients from the soil by plant uptake or deep leaching. Soils under cushion plants showed solute concentrations of Ca, Mg, and Na of about 3 times higher than forest and tussock grasses. Differences were even stronger for dissolved Si with solute concentrations that were 16 times higher than forest and 6 times higher than tussock grasses. Amongst the macronutrients derived from lithogenic sources, P was a limiting nutrient with very low solute concentrations (<1 µM) for all three vegetation types. In contrast K showed greater solute concentrations under forest soils with values that were 2 to 3 times higher than under cushion-forming plants or tussock grasses. Our findings have important implications for future management of Andean páramo ecosystems where vegetation type distributions are dynamically changing as a result of warming temperatures and land use change. Such alterations may lead not only to changes in soil hydrology and solute geochemistry but also to complex changes in weathering rates and solute export downstream with effects on nutrient concentrations in Andean rivers and high-mountain lakes.

Hydrometeorological controls of and social response to the 22 October 2019 catastrophic flash flood in Catalonia, north-eastern Spain

Abstract. On 22 October 2019, the Francolí River basin in Catalonia, north-eastern Spain, experienced a heavy precipitation event that resulted in a catastrophic flash flood, causing six fatalities. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors. The hydrometeorological factors are investigated using the new Triangle-based Regional Atmospheric Model, radar-derived precipitation estimates, post-flood field and gauge observations, and the Kinematic Local Excess Model. The social dimension is assessed by examining the relationship between catchment dynamics and warning response times and by quantifying human behaviour during the course of the flash flood through a post-event citizen science campaign. Results reveal that a persistent south-easterly airflow brought low-level moisture and established convective instability in the region, while local orography was instrumental to triggering deep moist convection. A convective train promoted intense, copious, and prolonged precipitation over the north-western catchment headwaters. Basin response was significantly modulated by the very dry initial soil moisture conditions. After the long-lasting rainfall, an acute burst of precipitation resulted in extreme flash flooding. Fast and abrupt increases in streamflow affect small spatial scales and leave limited time for the effective implementation of protective measures. The institutional organization–protection–prevention cycle unfolded at the spatial and temporal scales typically dominated by the meteorological rather than hydrological scales. Although the citizen science campaign reveals the effectiveness of the warnings in reaching the population living in the most affected areas, a significant proportion of the respondents expressed a lack of adequate information or were unfamiliar with the intended meaning. In addition, a majority of the interviewees did not perceive any significant threat to life or property. In view of these results, this study identifies potential areas for improving social preparedness for similar natural hazards in the future.

How Much Does Land–Atmosphere Coupling Influence Summertime Temperature Variability in the Western United States?

Abstract Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading empirical orthogonal function that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture–temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature–soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of 3 on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land–atmosphere interactions play an important role in interannual summertime temperature variability in the western United States; soil moisture variations cause distal temperature anomalies and impart predictability at time scales longer than one season. Our results indicate that 40% of the observed warming trend across the western United States since 1981 has been driven by wintertime precipitation trends in the U.S. southwest. Significance Statement Year-to-year fluctuations in summertime temperatures have a large impact on drought, wildfire, and extreme heat across the Western United States. We find strong evidence that soil moisture deficits in the preceding spring may be the primary driver of higher-than-average summer temperatures in this region. Our results suggest that memory in the water cycle may lead to greater predictability in the climate system from season to season and that trends in southwest precipitation have exerted a considerable influence on observed warming over the past 40 years.

AgroSense: An IoT-Based Manual Crops Selection Farming

This capstone project introduces an Intelligent Irri-gation System leveraging IoT technology to enhance agriculture. Using the ESP32 microcontroller and various sensors for soil moisture, water levels, and environmental conditions, the system automates irrigation based on real-time data. It communicates through the Blynk platform, allowing remote monitoring via a mobile app. The project includes a smart algorithm for crop selection and irrigation control, displayed on an LCD and acces- sible through the Blynk app. By considering soil moisture and water availability, the system adapts to different crops like rice, wheat, potato, and corn. The project promotes sustainability by optimizing water usage and encourages efficient crop growth. The integration of a manual crop count for field feedback enhances decision-making. Overall, this system presents a user-friendly and innovative solution for precision agriculture, showcasing the transformative potential of IoT, data analytics, and machine learning in modernizing farming practices.

Preliminary Experiences in Determining the Soil–Water Characteristic Curve of a Sandy Soil Using Physical Slope Modeling

Relating soil moisture content to soil suction, the soil–water characteristic curve (SWCC) represents an essential feature in unsaturated soil mechanics that enables estimation of different unsaturated soil property functions and modeling of the macro-scale soil behavior. However, depending on the soil and processes under consideration, proper hydraulic characterization of a soil through direct laboratory measurements can be difficult, time-consuming, and involve many uncertainties. In the case of uniformly graded sands, there is a highly nonlinear and steep shape of the SWCC, with only a few kPa of soil suction separating saturated and residual soil moisture conditions, which makes measurements for determinations of SWCC especially challenging. This study encompasses an investigation of the sandy type of soil’s behavior and presents some preliminary results and experiences on the determination of SWCC through the use of physical slope model tests. The 30 cm deep slope, inclined at 35 degrees and instrumented with soil moisture and pore water pressure sensors, was exposed to series of rainfall intensities, ranging from 37 up to 300 mm/h. The results indicated that the data on hydraulic response in monitored points are not only useful for the determination of SWCC, but that the approach is useful for investigation of hydraulic hysteresis phenomena, as well as its effects on soil moisture and pore water pressure conditions, which also affects the stability conditions of a slope. In particular, the best-fit parameters of the van Genuchten model suggested air entry values of 1.6 and 1.1 kPa for the drying and the wetting curves of the SWCC, respectively, with the two branches shifted by about 1 kPa of soil suction.

Robotic irrigation complex for crop cultivation in irrigated areas

Intelligent technologies used in digital agriculture, such as remote sensing, serve as a necessary tool for collecting bio-object data. It has been established that data collected through remote sensing and the Internet of Things (IoT), including comprehensive imaging of agricultural bio-objects, and their subsequent analysis will help solve numerous challenges in agricultural production. (Research purpose) The research aims to develop and validate a robotic irrigation complex for crop cultivation. This system will optimize irrigation processes by considering various environmental factors such as soil moisture, air temperature, wind speed, and other relevant conditions. (Materials and methods) The paper evaluates the signifi cance of developing a design that can optimize the process of crop cultivation and details the applications and databases integrated within the central computing center. The central computing hub monitors the current state of the irrigation area, performs calculations and develops management decisions. Utilizing mobile communications, this hub receives, processes, and transmits commands to the control panels of sprinklers, the pumping station, and the dispatcher for the machine-tractor team. (Results and discussion) The research has validated the design and main components of a robotic irrigation system for crop cultivation. Furthermore, an algorithm has been established to orchestrate the processes of data collection, calculation, and the control and management of operations. It was found that such a complex would optimize the irrigation process, reduce costs and resource consumption in the irrigated area, and decrease environmental pollution. (Conclusions) The implementation of a robotic irrigation complex will reduce water consumption, as well as material and energy costs associated with irrigation. By maintaining soil moisture within optimal ranges, this complex will enable the consistent production of high-quality crops under various weather conditions.

Geochemical characteristics of saline soils formed during the recent retreat of the Caspian Sea

The objectives of this study are: 1- Determining geochemical characteristics and soil formation processes that lead to soil salinity. 2- To investigate the variability of soil properties across different geomorphic units. The coastal landforms of the Caspian Sea have experienced several seawater retreat and progress in recent decades, which has caused changes in terms of pedology and geomorphology. Routine soil properties, including soil cations and anions (Ca2+, Mg2+, Na+, K+, Cl-, SO42-, CO32–, and HCO3–), and total and available forms of some micro-elements (Fe, Mn, Zn, and Cu) of 136 samples collected from 0-20 cm of soils were analyzed according to standard methods. Mapping of the obtained results was done using inverse distance weighting (IDW) in ArcGIS 10.8. The results showed that although the main source of cations and anions in the soil solution is seawater, their concentration and changes in the soil solution are different depending on the processes carried out in soil. Mg2+, Na+, and Cl- were highly enriched in saturated soil extracts compared to seawater (261 %, 153 %, and 149 % respectively). While, K+ and Ca2+, increased by 81 % and 47 % accordingly, and HCO3– and SO42-, decreased by −56 % and −25 % due to precipitation of carbonate and gypsum in the soil. Total and available forms of micro-nutrients were high across the study area. The distribution pattern of the total micro-nutrients follows the type of landforms (affecting drainage condition) and their clay contents (mineralogical composition and abundance), however, the available form of micro-nutrients depends on soil moisture conditions and proximity or distance to the coastline. The results showed that the chemistry of seawater is the main factor controlling the chemical and physical properties of soils and their distribution patterns. The geochemical processes of soil formation cause special interactions between the components of soil solution, which leads to the removal of carbonate, bicarbonate, and sulfate from the soil solution and precipitation with calcium as calcium carbonate and calcium sulfate.

Severity of topsoil compaction controls the impact of skid trails on soil ecological processes

Abstract Skid trails are a major management‐induced disturbance in temperate forest ecosystems with considerable impact on soil ecological processes that are so far poorly understood. In German forests, skid trails comprise 10%–20% of the forest area that is potentially affected by soil compaction through heavy machinery. We systematically investigated the influence of skid trails on physical, chemical and microbiological soil parameters at 84 paired plots across four Central European forest types. In low mountain forests with steeper topography, skid trails had more drastic effects than in lowland forests. Skid trails in low mountain areas showed a decrease in the C to N ratio of microbial biomass (MBC/MBN), as well as increased microbial (MBC/SOC) and enzyme activities leading to faster carbon turnover (lower C/N, EOC/EN) and increased CO2 losses (CO2/SOC) from the soil. The overall effects of the skid trails in lowland forests were small. On base‐poor soils, we found an increase in the MBC/MBN ratio, while skid trails in base‐rich lowland soils showed a reduction in CO2/SOC, suggesting a proportional increase in soil carbon storage. Regardless of region‐specific effects, the relative increase in the bulk density of the fine soil was identified as a ‘golden trait’ that determined the effects of skid trails on many soil parameters, as shown by negative correlations with SOC, N, MBC, MBN, MBP, MBC/SOC and CO2/SOC and positive ones with the activities of certain hydrolytic enzymes. Synthesis and applications: Our data clearly showed that carbon conversion processes and soil respiration leading to significant carbon and nutrient losses increased significantly on skid trails in low mountain regions with relatively steep slopes, which was in sharp contrast to lowland sites. The strong context dependence of our findings suggests that the mapping of soil conditions in terms of slope, substrate and moisture with high spatial resolution is mandatory to assess the vulnerability of sites to soil compaction by heavy machinery. Based on such vulnerability analysis, negative impacts can be minimised through the designation of permanently fixed skid trails, technical adaptation of vehicles (e.g. wide base tyres) as well as careful planning and timing of management operations that should be restricted to dry weather and soil moisture conditions or periods of frost.

Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling

Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.

Machine learning-based modeling of interface creep behavior of grouted soil anchors with varying soil moistures

The interface creep behavior of the grouted soil anchor subject to varying soil moisture was investigated using the combined incorporation of experimental and data-driven modeling methods to establish an efficient and robust forecasting framework. This study carried out the rapid and creep pullout tests of element anchor specimens at various saturations and then utilized machine learning methods to predict the development of interface creep displacement. The stepwise loading strategy and nonlinear superposition method were combined to generate the interface shear creep curves of the element anchor specimens. A total of 936 data groups of the interface shear displacement were collected with changing soil moisture contents, interface shear time, and interface shearing stress. Next, this study explored the Back Propagation Neural Network (BPNN) and four other machine learning algorithms in predicting the interface creep behavior of the grouted soil anchor under various moisture conditions. As for the hyperparameters, the beetle antennae search (BAS) approach was employed to optimize the BPNN and random forest (RF) models. Finally, the boxplot and Taylor diagrams proved the BAS-BPNN demonstrated a better performance than BAS-RF in predicting the interface creep behavior. The consequent correlation coefficients ranged from 0.9613 to 0.9805 for BPNN, indicating the accuracy and reliability of the interface creep prediction. A partial dependence plot (PDP) was also introduced to visualize the established machine learning model. The threshold of moisture content near 28.7 % is found to switch the interface shear stress-displacement response from strain-stabilizing to strain-softening behavior and to result in the main moisture-increase-induced interface strength degradation. The soil moisture fluctuation leads to the development of interface shear displacement mainly observed in the early phase of 20 h after the onset of moisture change. The uncovered coupled impact of soil moisture condition and interface shear stress state can provide insights into the evaluation of the time-dependent in-service performance of grouted soil anchors embedded in clayey soils.

Soil respiration across a variety of tree-covered urban green spaces in Helsinki, Finland

Abstract. As an increasing share of the human population is being clustered in cities, urban areas have swiftly become the epicentres of anthropogenic carbon (C) emissions. Understanding different parts of the biogenic C cycle in urban ecosystems is needed in order to assess the potential to enhance their C stocks as a cost-efficient means to balance the C emissions and mitigate climate change. Here, we conducted a field measurement campaign over three consecutive growing seasons to examine soil respiration carbon dioxide (CO2) fluxes and soil organic carbon (SOC) stocks at four measurement sites in Helsinki, representing different types of tree-covered urban green space commonly found in northern European cities. We expected to find variation in the main drivers of soil respiration – soil temperature, soil moisture, and SOC – as a result of the heterogeneity of urban landscape and that this variation would be reflected in the measured soil respiration rates. In the end, we could see fairly constant statistically significant differences between the sites in terms of soil temperature but only sporadic and seemingly momentary differences in soil moisture and soil respiration. There were also statistically significant differences in SOC stocks: the highest SOC stock was found in inactively managed deciduous urban forest and the lowest under managed streetside lawn with common linden trees. We studied the impacts of the urban heat island (UHI) effect and irrigation on heterotrophic soil respiration with process-based model simulations and found that the variation created by the UHI is relatively minor compared to the increase associated with active irrigation, especially during dry summers. We conclude that, within our study area, the observed variation in soil temperature alone was not enough to cause variation in soil respiration rates between the studied green space types, perhaps because the soil moisture conditions were uniform. Thus, irrigation could potentially be a key factor in altering the soil respiration dynamics in urban green space both within the urban area and in comparison to non-urban ecosystems.

Modeling soil moisture from in situ portable X-ray spectrometer measurements: A novel approach for correcting geochemical data across different environments and climatic conditions

The portable X-ray fluorescence (pXRF) spectrometer is widely employed for in situ analysis of both contaminated and uncontaminated soils. However, the accuracy of the measurements can be significantly affected by soil moisture, resulting in unreliable soil pollution monitoring. This effect has already been studied and quantified, but this is ineffective if the soil moisture in the field is unknown. Given the considerable variability of soil moisture conditions across time and space, significant bias during in situ investigations remains a main issue. This study introduces a novel method to estimate soil moisture directly from pXRF field measurements, enabling its reliable use in almost any field condition. The study was conducted using soil samples and in situ pXRF soil surface measurements in Estarreja (Portugal) and Vicdessos (France). In the first experiment, the innovative approach involved modeling soil moisture directly from the raw XRF measurement errors obtained in moist soils using multiple regression. In the second experiment, metal concentrations were modeled as an exponential function of the moisture content. The final model integrates both approaches to correct field data from geochemical mapping in diverse environments, including a coastal region in Portugal and a mountainous region in France. Our findings demonstrate that this simple, efficient and cost-effective method accurately predicts soil moisture (U) using pXRF, as shown by the equation Umeasured = 1.0028 x Uestimated (r2 = 0.9715). The model effectively corrected up to 70% of moisture-induced errors in metal concentrations in the wettest soils and produced more reliable soil Fe, Pb, and Zn maps. Specifically, the accuracy improvement was at least 32% in drier soils (Portugal) and at least 55% in wetter soils (France). This study offers a cost-effective, efficient solution for employing pXRF in geochemical mapping across different climatic conditions and soil environments.

A Study on the Possible Brown Ocean Effect: Impacts of an Antecedent Tropical Cyclone on the Rapid Intensification of the 1999 Odisha Supercyclone

AbstractOdhisa, a state of India, bore the disastrous consequences of two consequent Tropical Cyclones (TCs), TC 04B and TC 05B (Odisha 1999 supercyclone), which formed over the Bay of Bengal and experienced landfall in October 1999, with a time gap of fewer than two weeks, over the same region. It is suggested that the first TC, TC 04B, provided an “ocean‐like situation” over the coastal land region, thus delivering the appropriate land conditions that would facilitate the intensification of the following second tropical cyclone, TC 05B; a clear illustration of the “Brown Ocean effect.” Two Weather Research Forecasting (WRF) simulations were conducted, with the control and experimental runs differing solely in the following aspect: the initial cyclonic vortex corresponding to the first TC at the initial time was removed in the experimental run, whereas it was retained in the control run. Both simulations were analyzed to reveal the “Brown Ocean Effect” role. The experimental run result indicates that the minimum central sea level pressure of the second TC was 35 hPa higher than the second TC simulation in the control run. The heavy rainfall associated with TC 04B led to increased soil moisture conditions, providing the second TC (TC 05B) with the necessary conditions for its intensification by the “Brown Ocean Effect.” The results of this study appear to strongly suggest that the “Brown Ocean Effect” could provide one of the main reasons for the extraordinary intensities associated with the 1999 Odisha supercyclone.

Assessing crop yield and water balance in crop rotation irrigation systems: Exploring sensitivity to soil hydraulic characteristics and initial moisture conditions in the North China Plain

Multiple cropping is an effective measure to enhance the intensity of land use. The North China Plain is one of China’s most important grain production areas, with 70 % of the arable land under double rotation of winter wheat and summer maize. The allocation of irrigation water between two crop seasons depends on soil water flow and crop water consumption. This is because the amount of moisture left in the soil at the end of one season affects how much moisture is in the soil at the beginning of the next season. As a result, the amount of crops produced and the amount of water needed for irrigation are influenced by the initial soil moisture conditions and the soil’s hydraulic properties. This study aims to analyze how various factors affect crop yield when faced with water scarcity. The factors considered include initial soil moisture condition, soil texture, and irrigation scheduling. The simulation results indicate that, in most cases, initial soil moisture conditions have a more significant impact on crop yield than soil hydraulic characteristics. The impact of irrigation can differ based on the irrigation method and availability of water. Hence, when distributing irrigation water over a year, it is crucial to consider the soil’s water transport between two crop cycles to achieve the ideal full and deficit irrigation in a crop rotation system. Additionally, a joint optimal irrigation plan can significantly reduce the adverse effects of unfavorable soil hydraulic characteristics. Moreover, the optimal irrigation strategy can enhance crop water productivity and food security simultaneously.

Impacts of soil type and drought stress on growth and cesium accumulation in Napier grass.

In our previous study, the decontamination efficiency of cesium-137 (137Cs) by Napier grass (Pennisetum purpureum Schum.) in the field was shown to be variable and often influenced by natural environmental factors. To elucidate the factors influencing this variable 137Cs-decontamination efficiency, we investigated the influences of soil type and drought stress on Cs accumulation using cesium-133 (133Cs) in Napier grass grown in plastic containers. The experiment was performed using two soil types (Soil A and B) and three different soil moisture conditions: well-watered control (CL), slight drought stress (SD), and moderate drought stress (MD). Overall, our results indicate that soil type and drought have a significant impact on plant growth and 133Cs accumulation in Napier grass. Plant height (PH), tiller number (TN), leaf width (Wleaf), and dry matter weight of aboveground parts (DWabove) and root parts (DWroot) in Soil B were greater than those in Soil A. Drought stress negatively affected chlorophyll fluorescence parameters (maximal quantum efficiency of photosystem (PS) II photochemistry and potential activity of PS II), PH, TN, Wleaf, DWabove, DWroot, and total 133Cs content (TCs), but it had a positive effect on 133Cs concentration. The 133Cs concentration in the aboveground parts (Csabove) was increased by MD approximately 1.62-fold in Soil A and 1.11-fold in Soil B compared to each CL counterpart. The TCs in the aboveground parts (TCsabove) decreased due to drought by approximately 19.9%-39.0% in Soil A and 49.9%-62.7% in Soil B; however, there was no significant effect on TCsabove due to soil type. The results of this study indicate that soil moisture is a key factor in maintaining Napier grass 137Cs-decontamination efficiency.

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms.

Influence of biochar application on carbon dioxide (CO2) emission under soybean crop

Abstract Biochar, an organic solid produced through pyrolysis, exhibits promising potential as a soil amendment. The utilization of biochar as a soil amendment is geared towards the reduction of CO2 emissions and the sequestration of carbon in soil, thus playing a pivotal role in addressing climate change. This research was undertaken to quantify the extent of carbon emissions resulting from various biochar application treatments in soybean cultivation. The field study was structured in a randomized non-factorial design, encompassing five distinct biochar types: rice husk at application rates of 20 and 30 ton ha−1, pulai wood (Alstonia scholaris) at rates of 20 and 30 ton ha−1, and a control group serving as a reference. Each treatment was replicated three times. The quantification of greenhouse gas emissions involved the utilization of a CO2 gas detector (Digital type HT-2000) placed within a chamber measuring 66 cm x 46 cm x 78 cm. The findings elucidated that the application of biochar had a discernible influence on the levels of CO2 gas emissions. Specifically, the application of 30 ton ha−1 of rice husk and 20 ton ha−1 of pulai wood yielded higher emissions in comparison to the control group. This phenomenon is attributed to the favorable environment created by biochar within wetter soil conditions, fostering the proliferation of microorganisms, and subsequently contributing to increased emissions through microbial respiration.