Journal of Early Childhood Literacy

Abstract The Caspian and Black Seas are adjacent inland bodies of water, each with its unique palaeogeographic history. The Black Sea has been connected to the World Ocean throughout its Quaternary history. In contrast, the Caspian Sea has been completely isolated since the beginning of the Middle Pleistocene. Since then, the Caspian Sea has occasionally discharged its excess waters through the Manych Depression into the Azov-Black Sea Basin. As a result of this isolation, unique species have developed in the Caspian Sea. The transgressive-regressive cycles of the Caspian Sea are associated with global and regional climate fluctuations since its sea level depends on its water balance. Due to the penetration of Caspian species into the Manych Depression and further into the Azov-Black Sea Basin, it is possible to determine episodes of its transgressive water discharge, assess the characteristics of the existing basins, and correlate these episodes not only with events within the Mediterranean—Black Sea—Caspian corridor but also with global events. The study of the connection between the Black and Caspian Sea basins dates back to the 18th century. Thereafter, numerous publications have addressed various aspects of the palaeogeography of the region. However, the events within these two basins during Marine Isotope Stage 5 (MIS 5) and their scales and characteristics remain debated. This research focused on studying the MIS 5 Epoch, which includes the Holocene-analogous Eemian interglacial period and the transition to the Early Weichselian glacial Epoch. To reconstruct the events in the Black Sea—Manych Depression—Caspian Sea during MIS 5, we conducted lithological, malacofaunistic, and geochronological analysis on six borehole sections in the Northern Caspian, four in the Manych Depression, and three in the northeastern sector of the Black Sea to identify events that occurred in basins, assess their environmental characteristics, conditions of sedimentation and time frames. We have discovered that the Karangatian transgression (analogous to the Eemian transgression in the Mediterranean Sea) ingressed into the Manych Depression 125–110 ka BP (MIS 5e–d) during its maximum phase and formed a gulf there with a water salinity of up to 18‰, featuring Mediterranean mollusc fauna. Later, the waters of the Hyrcanian transgression of the Caspian Sea exceeded the water divide of the Azov-Black Sea and Caspian Sea basins and began to spill over into the Manych Depression. This influx of water resulted in the desalination of the sea gulf that was present there, reducing its salinity to values of less than 14‰. This event occurred around 110–105 ka BP (MIS 5c). The Karangatian waters eventually left the Manych Depression, but Hyrcanian waters continued to flow into the Black Sea until ~ 100 ka BP. This is evidenced by characteristic Hyrcanian species, such as Didacna cristata and D. subcatillus, found in the upper part of Karangatian sediments in the Manych Depression and on the northeastern shelf of the Black Sea. The Hyrcanian water discharge marked the initial flow of Caspian water into the Black Sea during the late Pleistocene, occurring unilaterally without any exchange between these two basins. Reconstructing the transition from the interglacial to the glacial period during MIS 5 can provide valuable insights into the expected changes in the Black Sea—Manych Depression—Caspian Sea system as we move from the current Holocene interglacial into a new glacial period.

Abstract 16 different satellite soil moisture (SM) datasets (passive, active, combined, and model data) were compared at the European scale. We hypothesized that SM should be reflected by a variety of environmental factors, such as topography, hydroclimatology, soil characteristics, and biomass. Robust correlation was used to explore the relationship among the satellite data products, and the Recursive Feature Elimination method combined with the Random Forest Regression (RFR) algorithm was used to find the most important variables. Variations in SM-values were analyzed using extended triple collocation analysis (ETC), while the accuracy metrics of the RFR models were summarized through UMAP dimension reduction. The result showed that generally, correlations among the SM products were low (r < 0.5) with some exceptions. GLDAS had the weakest correlation with the other SM products. Using SM as the dependent variable in regression models, model testing showed that GLDAS’s SM was explained with the highest accuracy based on the Nash-Sutcliffe Efficiency (0.631), followed by the SMOPS (0.624). SSM demonstrated the lowest environmental influence (NSE: 0.288). Using UMAP, ETC, it was determined that SMOPS exhibited superior performance in terms of error variance and model accuracy; however, based on the ETC results, GRD.P was deemed the most suitable option. Results called the attention of varying SM values by products, being biased by various environmental factors and the applied technology of the satellites.

Abstract This paper analyses the impact of heterogeneity in the horizontal hydraulic conductivity field ( $${K}_{hf}$$ ) on the optimal pumping scenarios in a coastal aquifer and presents a multi-objective management framework to select robust optimal scenarios under high levels of uncertainty. Model speed is significantly improved by training an M5 Decision Tree (MDT) algorithm as a fast surrogate model for the density-dependent flow (DDF) in the SEAWAT code. The developed Tree model was linked to a non-dominated genetic algorithm (NSGAII) to determine Pareto optimal solutions, with the aim of maximizing total pumping volume and minimizing saltwater intrusion in a real case study, i.e., the Qom-Kahak aquifer, Iran. A linear sensitivity analysis explores the relationship between Pareto curves in response to variations in calibrated values of $${K}_{hf}$$ to quantify robust scenarios by a robust decision-making technique. Finally, the conflict resolution between minimum saltwater intrusion length, maximum pumping rate and robustness values is solved using a non-cooperative Nash bargaining theory. Results indicate that maintaining discharge from the pumping wells located far from 3 observation points in the case study, especially near the Salt Lake boundary, increases uncertainty in the Pareto solutions, where increasing $${K}_{hf}$$ by up to 30% of calibrated values induces a maximum 12% shift in the Pareto front for the scenario which led to high saltwater intrusion lengths. Moreover, the non-robust scenario causes the saltwater intrusion $$\overline{SWI }$$ zone to sharply advance to the area with a large number of pumping wells, while the scenario with high Nash product values led to a relatively uniform salinized zone which satisfies the allowed SWI length in 5 agricultural zones. In total, the developed MDT-NSGAII model is a computationally effective simulation–optimization model to find the Pareto front with 55 decision variables while achieving a 95% reduction in CPU time compared to the SEAWAT-NSGAII technique.

Abstract Due to Florida’s exposure to hurricanes originating from both the Atlantic Ocean and the Gulf of Mexico, hurricane risk assessments serve as a critical tool for mitigating potential impacts. This is the first novel study to develop a machine learning based risk assessment for hurricane induced flood damage, which demonstrates the potential of granular building level insurance data from 1985 to 2024, enriched with remote sensing derived variables. The stacked ensemble machine learning model predicted hurricane flood damage with an MAE of 11.3% at a granular ZIP Code Tabulation Area level (ZCTA). The model’s explainability tools determined that building property value was a significant predictor of hurricane damage, as it correlated with property size, complex architectural design, and proximity to waterfront locations, all of which affect potential repair costs. Other predictive factors include construction year, occupancy type, and flood zone designation. Partial dependency plots (PDPs) identified that northwest Florida is particularly susceptible to hurricane damage, attributed to the Gulf of Mexico’s warm and shallow waters than eastern Florida’s cooler Atlantic conditions and steep ocean floor. Miami’s significant coastal urbanisation, rendered it a hotspot despite southeast Florida’s overall low hurricane risk. Similarly Jacksonville in north-eastern Florida was identified as a hotspot due to compounded flooding from storm surge and nearby river systems. Partial dependency plots also quantified the significant positive impact of 1970s building code regulation. Future studies should examine coastal morphology, landfall angle, and proximity to barrier islands. A study limitation is that insurance data may be an imperfect representation of Florida, due to underinsurance and inability to afford insurance.

Abstract Floods are among the most severe natural disasters globally, particularly in densely populated areas with extensive agriculture, concentrated rivers, and abundant rainfall. In recent years, human activities have altered river confluence conditions, exacerbating the frequency and severity of floods. To address the limitations of existing multi-tributary stream flow prediction models, which suffer from poor real-time performance and low prediction accuracy, we developed a bio-inspired neural network (Bio-NN) model motivated by a cooperative regulation mechanism in biological systems. Considering the problem that there is less feedback information in existing neural networks, the proposed model combines a biohormone multi-level nonlinear feedback regulation mechanism with a neural network. This enhances traditional neural networks by improving network structure and dynamically incorporating feedback information, allowing real-time optimization and improving optimization speed and precision over time. We tested the Bio-NN model by applying it to predict river flow along the lower Murray River in Australia. To obtain deeper insights into the performance of Bio-NN, indicators such as NSE, RSR, PCC, and KGE, were determined in the basin. The simulation demonstrated its superior performance, achieving a Nash-Sutcliffe efficiency coefficient (NSE) of 0.991, root mean squared to standard deviation ratio (RSR) of 0.096, a Pearson’s correlation coefficient (PCC) of 0.996, and a Kling-Gupta efficiency coefficient (KGE) of 0.995. Compared to a back propagation neural network (BP-NN), a dynamic learning BP-NN, and a self-feedback BP-NN, the Bio-NN showed significant improvements in prediction performance: improved by 8-65% (NSE), 4-28% (PCC), 67-85% (RSR), 9-27% (KGE). The results underscore Bio-NN’s capability to significantly enhance the accuracy and stability of flood prediction models.

Soil health is a key indicator of agronomic, economic, and environmental functions, yet its significance in deeper soil layers, especially in deep-rooted orchard systems, remains largely unexplored. This study examines soil health index (SHI) in topsoil and subsoil across various soil classes in long-term apple orchards. We measured 27 soil health indicators to develop a soil health framework for apple orchards using 240 soil samples. Significant differences (p < 0.05) were found between topsoil and subsoil for most indicators, including soil stability index (93 to 132%), organic carbon (100 to 130%), macronutrients (40 to 147%), and heavy metals (8 to 75%), while bulk density (1 to 5%), total soil pore spaces (4 to 12%), pH (0.03 to 0.12 unit), calcium carbonates (6 to 21%), and cation exchange capacity (7 to 18%) showed no significant differences. In the topsoil, the liner-SHI and non-linear-SHI scores ranged between 0.5 and 0.9 and 0.3 to 0.6 compared with the subsoil with corresponding ranges of 0.4 to 0.77 and 0.32 to 0.56, respectively. Both the liner-SHI and non- linear-SHI scores were higher in the topsoil, explaining 25–29% of the variance in apple productivity. Our results bring further insight into a quantitative method for assessing soil health at the soil type-scale and creatively analyzes the changes of SHIs with soil depth and their relationship with product performance in the apple orchards under long-term continuous intensive practices.