Soil salinization presents a significant challenge, driven by factors such as inadequate drainage, shallow aquifers, and high evaporation rates, threatening global food security. The sunflower emerges as a key cash crop in such areas, providing the opportunity to convert its straw into biochar, which offers additional agronomic and environmental benefits. This study investigates the effectiveness of biochar interlayers in enhancing salt leaching and suppressing upward salt migration through integrated laboratory and field experiments. The effectiveness of varying biochar interlayer application rates was assessed in promoting salt leaching, decreasing soil electrical conductivity (EC), and enhancing crop performance in saline soils through a systematic approach that combines laboratory and field experiments. The biochar treatments included a control (CK) and different applications of 20 (BL20), 40 (BL40), 60 (BL60), and 80 (BL80) tons of biochar per hectare, all applied below a depth of 20 cm, with each treatment replicated three times. The laboratory and field experimental setups maintained consistency in terms of biochar treatments and interlayer placement methodology. During the laboratory column experiments, the soil columns were treated with deionized water, and their leachates were analyzed for EC and major ionic components. The results showed that columns with biochar interlayers exhibited significantly higher efflux rates compared to those of the control and notably accelerated the time required for the effluent EC to decrease to 2 dS m⁻¹. The CK required 43 days for full discharge and 38 days for EC stabilization below 2 dS m⁻¹. In contrast, biochar treatments notably reduced these times, with BL80 achieving discharge in just 7 days and EC stabilization in 10 days. Elution events occurred 20–36 days earlier in the biochar-treated columns, confirming biochar’s effectiveness in enhancing leaching efficiency in saline soils. The field experiment results supported the laboratory findings, indicating that increased biochar application rates significantly reduced soil EC and ion concentrations at depths of 0–20 cm and 20–40 cm, lowering the EC from 7.12 to 2.25 dS m⁻¹ and from 6.30 to 2.41 dS m⁻¹ in their respective layers. The application of biochar interlayers resulted in significant reductions in Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and HCO₃⁻ concentrations across both soil layers. In the 0–20 cm layer, Na⁺ decreased from 3.44 to 2.75 mg·g⁻¹, K⁺ from 0.24 to 0.11 mg·g⁻¹, Ca²⁺ from 0.35 to 0.20 mg·g⁻¹, Mg²⁺ from 0.31 to 0.24 mg·g⁻¹, Cl⁻ from 1.22 to 0.88 mg·g⁻¹, SO₄²⁻ from 1.91 to 1.30 mg·g⁻¹ and HCO₃⁻ from 0.39 to 0.18 mg·g⁻¹, respectively. Similarly, in the 20–40 cm layer, Na⁺ declined from 3.62 to 3.05 mg·g⁻¹, K⁺ from 0.28 to 0.12 mg·g⁻¹, Ca²⁺ from 0.39 to 0.26 mg·g⁻¹, Mg²⁺ from 0.36 to 0.27 mg·g⁻¹, Cl⁻ from 1.18 to 0.80 mg·g⁻¹, SO₄²⁻ from 1.95 to 1.33 mg·g⁻¹ and HCO₃⁻ from 0.42 to 0.21 mg·g⁻¹ under increasing biochar rates. Moreover, the use of biochar interlayers significantly improved the physiological traits of sunflowers, including their photosynthesis rates, stomatal conductance, and transpiration efficiency, thereby boosting biomass and achene yield. These results highlight the potential of biochar interlayers as a sustainable strategy for soil desalination, water conservation, and enhanced crop productivity. This approach is especially promising for managing salt-affected soils in regions like California, where soil salinization represents a considerable threat to agricultural sustainability.

It is essential to emphasize the significant impacts of climate change, which are evident in the form of severe and prolonged droughts, hurricanes, snowstorms, and other climatic disturbances. These challenges are particularly pronounced in urban environments and among human populations. The situation is further aggravated by the increasing utilization of available open spaces for residential and industrial development, leading to heightened energy consumption, elevated pollution levels, and increased carbon emissions, all of which negatively affect public health. The primary objective of this review article is to provide a comprehensive evaluation of current research, with a particular focus on the innovative use of residual biomass from urban vegetation for biochar production in the United States. This research entails an exhaustive review of existing literature to assess the implementation of a carbon-negative wood biomass biochar system as a strategic approach to sustainable urban management. By transforming urban wood waste—including tree trimmings, construction debris, and storm-damaged timber—into biochar through pyrolysis, a thermochemical process that sequesters carbon while generating renewable energy, we can leverage this valuable resource. The resulting biochar offers a range of co-benefits: it enhances soil health, improves water retention, reduces stormwater runoff, and lowers greenhouse gas emissions when applied in urban green spaces, agriculture, and land restoration projects. This review highlights the advantages and potential of converting urban wood waste into biochar while exploring how municipalities can strengthen their green ecosystems. Furthermore, it aims to provide a thorough understanding of how the utilization of woody biomass biochar can contribute to mitigating urban carbon emissions across the United States.

Soil health is fundamental to sustainable agriculture, crop productivity, and vital ecosystem services. However, climate change increasingly threatens the functional integrity of soil due to rising temperatures, altered precipitation patterns, elevated atmospheric carbon dioxide levels, and extreme weather events. These factors accelerate soil degradation and disrupt the physical, chemical, and biological processes essential for maintaining soil health. This review synthesizes recent evidence regarding the impacts of climate change on soil health and crop production, while also evaluating sustainable management strategies designed to enhance agricultural resilience. Techniques such as conservation agriculture, organic matter management, diversified cropping systems, and integrated nutrient management can improve soil structure, nutrient cycling, water retention, carbon sequestration, and overall crop productivity. Furthermore, emerging technologies offer new opportunities for real-time, site-specific, and data-driven soil management. These technologies include remote sensing, geographic information systems, artificial intelligence, digital agriculture, and advanced soil monitoring tools. Additionally, policy and socioeconomic factors—particularly financial incentives, extension services, and institutional support—are critical in encouraging farmers to adopt these practices. Despite recent advancements, challenges such as economic barriers, technological limitations, regional variability, and a lack of long-term evidence hinder the widespread implementation of sustainable soil health practices. This review provides an integrated perspective that links climate-driven changes in soil’s physical, chemical, and biological properties with sustainable management practices, emerging technologies, and policy support. It highlights the interactions and trade-offs among these components to promote climate-resilient and sustainable agricultural systems.

Extreme precipitation events in the southeastern United States pose significant challenges for numerical weather prediction because subgrid-scale processes strongly influence rainfall intensity, spatial distribution, and timing. This study assesses the sensitivity of Weather Research and Forecasting (WRF) Model v4.7 precipitation simulations during an intense rainfall episode over Alabama from 27 April to 3 May 2014. Stage IV observations indicated localized precipitation totals exceeding 200 mm, with a domain-mean accumulation of 42.48 mm. A 12-member physics ensemble was created by pairing two double-moment microphysics schemes, WDM6 and Morrison, with six cumulus parameterization schemes: Grell–Devenyi (GD), Grell–Freitas (GF), Betts–Miller–Janjic (BMJ), Kain–Fritsch (KF), KF-CuP, and Tiedtke. Model outputs were regridded to the 4 km Stage IV analysis grid and evaluated using conventional error statistics alongside spatial verification metrics, such as the Fractions Skill Score (FSS), spatial correlation, and temporal diagnostics. This multifaceted evaluation mitigated sensitivity to minor spatial displacement errors, enabling a more nuanced assessment of forecast performance. In addition, two simulations were conducted without cumulus parameterization as a bounding check on this modeling choice at the 4 km, gray-zone grid spacing. Within this experimental design, the choice of cumulus parameterization was the largest source of spread in precipitation forecast skill, strongly affecting storm-total rainfall, spatial distribution, and peak precipitation timing. Configurations using the KF family of schemes typically provided the most accurate representation of organized heavy rainfall characteristics, whereas the BMJ scheme notably underestimated both precipitation intensity and areal coverage. Although the effects of microphysics were secondary, distinct patterns emerged: the Morrison scheme generally improved volumetric precipitation accuracy, whereas WDM6 concentrated extreme rainfall into more localized cores when paired with aggressively triggered cumulus schemes. Spatial verification techniques uncovered performance differences that traditional grid-point statistics did not fully capture, allowing us to identify the most effective physics configuration among those tested. However, overall forecast skill remained limited; none of the configurations met the commonly accepted useful-skill threshold (FSS ≥ 0.5) for the ≥200 mm precipitation category at any neighborhood scale analyzed. This underscores the ongoing challenges in accurately representing the most intense rainfall cores. Because this analysis is based on a single extreme event, caution is warranted when extrapolating these findings. Further evaluations across a wider array of storm environments are essential to substantiate the identified model sensitivity.