A modified biological-integrated electrocoagulation method was explored to treat municipal wastewater (MWW) for irrigation purposes. To use treated wastewater for irrigation purposes a wide range of contaminants removal was focused on in this study (turbidity, hardness, conductivity, TDS, TSS, chloride, Ammonia nitrogen, BOD, COD, and total coliform). Raw municipal wastewater (RMWW) was treated in a modified Bio-Electrocoagulation (BEC) cell. The cell was operated in a continuous flow mode and consisted of an electrocoagulation stage using aluminum (Al) electrodes followed by a bioremediation stage using a fixed bio-filter (BF), the design of the cell was further modified by the addition of a sand filter (SF). The effect of several parameters such as applied voltage (22, 26, and 30 V), inlet flow rate (1, 3, and 5 Lh^-1), and initial pH (pH 3, 5, 7, 7.4, and 9) was investigated to determine the optimum operating conditions for selected responses. The most effective operating conditions for the BEC were investigated for the different irrigation water quality (WQ) indicators. It was observed that pH 7.4 and 26 V provide maximum removal efficiency of contaminants at the flow rate of 1 Lh^-1. A fixed film BF plays a positive role to improve the degradation of contaminants after the EC unit up to 4% of NH₃-N, 9.3% of BOD, and 7.8% of COD. In addition, using the SF improved the turbidity removal to 42.6%. The WQ specifications of the treated MWW using the BEC cell were compared with the standard specifications for restricted and unrestricted agricultural irrigation water. The overall operating cost of MWW treatment for irrigation purposes by using a modified bio-integrated electrocoagulation method was 0.76 $m^-3.

Drought, a natural phenomenon intricately intertwined with the broader canvas of climate change, exacts a heavy toll by ushering in acute terrestrial water scarcity. Its ramifications reverberate most acutely within the agricultural heartlands, particularly those nestled in arid regions. To address this pressing issue, this study harnesses the temperature vegetation dryness index (TVDI) as a robust drought indicator, enabling a granular estimation of land water content trends. This endeavor unfolds through the sophisticated integration of geographic information systems (GISs) and remote sensing technologies (RSTs). The methodology bedrock lies in the judicious utilization of 72 high-resolution satellite images captured by the Landsat 7 and 8 platforms. These images serve as the foundational building blocks for computing TVDI values, a key metric that encapsulates the dynamic interplay between the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The findings resonate with significance, unveiling a conspicuous and statistically significant uptick in the TVDI time series. This shift, observed at a confidence level of 0.05 (Z_S = 1.648), raises a crucial alarm. Remarkably, this notable surge in the TVDI exists in tandem with relatively insignificant upticks in short-term precipitation rates and LST, at statistically comparable significance levels. The implications are both pivotal and starkly clear: this profound upswing in the TVDI within agricultural domains harbors tangible environmental threats, particularly to groundwater resources, which form the lifeblood of these regions. The call to action resounds strongly, imploring judicious water management practices and a conscientious reduction in water withdrawal from reservoirs. These measures, embraced in unison, represent the imperative steps needed to defuse the looming crisis.

The present investigation utilized artificial neural networks (ANN) and gene expression programming (GEP) in comparison with the two-point method (TPM) to develop a generalized solution for predicting infiltrated water volume (∀_Z) across various soil types under furrow conditions. This work assesses infiltration behavior with respect to experimental data from several temporal contexts. Data distribution and model performance are evaluated via descriptive statistics and correlation tests. Artificial intelligence (AI) models (ANN and GEP) trained and evaluated utilizing input variables—inflow rate ($Q_{in}$); furrow length ($L$); waterfront advance time at the end of the furrow ($T_L$); infiltration opportunity time ($T_o$); and cross-sectional area of the inflow ($A_o$) are compared with TPM performance. More precisely and consistently than the water advance power function, AI-based algorithms hope to be invading water volume. Statistical analysis shows that ANN and GEP have lower error metrics, increased generalizability, and better representation of complex infiltration dynamics. The determination coefficient (R²) of ANN data produced 98.1% for testing and 97.8% for validation, while TPM showed accuracy reductions of 2.5% and 4.6%, respectively. On the other side, the R² of GEP produced 95.7% for testing and 96.1% for validation, while TPM showed accuracy reductions of 0.7% and 3%, respectively. During ANN model computation, TPMs root mean square error (RMSE) of 0.0135 m³/m exceeded all mean values. Errors within 10% relative deviation were displayed using the ANN model ${\forall }_Z$. Particularly, ANN and GEP, the study revealed that AI techniques predict furrow irrigation penetration of water volume better than the water advance power function. These models advance soil and furrow adaptation, extrapolation, and accuracy. Results show that AI-driven modeling may maximize hydrological assessments and irrigation control.

Water, often described as the elixir of life, is a critical resource that sustains life on Earth. The acute water scarcity in the major basins of the Arabian Peninsula has been further aggravated by rapid population growth, urbanization, and the impacts of climate change. This situation underscores the urgent need for a comprehensive analysis of the region’s morphometric characteristics. Such an analysis is essential for informed decision-making in water resource management, infrastructure development, and conservation efforts. This study provides a foundational basis for implementing sustainable water management strategies and preserving ecological systems by deepening the understanding of the unique hydrological processes within the Arabian Peninsula. Additionally, this research offers valuable insights to policymakers for developing effective flood mitigation strategies by identifying vulnerable areas. The study focuses on an extensive investigation and assessment of morphometric parameters in the primary basins of the Arabian Peninsula, emphasizing their critical role in addressing water scarcity and promoting sustainable water management practices. The findings reveal that the Arabian Peninsula comprises 12 major basins, collectively forming a seventh-order drainage system and covering a total land area of 3.24 million km². Statistical analysis demonstrates a strong correlation between stream order and cumulative stream length, as well as a negative correlation between stream order and stream number (R² = 99%). Further analysis indicates that many of these basins exhibit a high bifurcation ratio, suggesting the presence of impermeable rocks and steep slopes. The hypsometric integral (HI) of the Peninsula is calculated to be 60%, with an erosion integral (EI) of 40%, indicating that the basin is in a mature stage of geomorphological development. Importantly, the region is characterized by a predominantly coarse drainage texture, limited infiltration, significant surface runoff, and steep slopes, all of which have critical implications for water resource management.

The conflicts among the landscape water demand and other urban water requirements are motivating improvements in water sustainability in arid urban areas. The accurate estimation of urban landscape plants’ evapotranspiration (ET_PLT) is crucial for effective irrigation management practices. This study examined two factor-based approaches—the Water Use Classification of Landscape Species (WUCOLS) and the Landscape Irrigation Management Program (LIMP)—in conjunction with the formula developed by Penman–Monteith to calculate the landscape irrigation water demand in Saudi Arabia. The reference evapotranspiration (ET_r) was calculated utilizing 40 years of recorded meteorological data from various locations in Saudi Arabia. Notable variations in ET_r were observed both geographically between different regions and seasonally within regions. The highest, lowest, and moderate ET_r values were recorded in Riyadh, Mecca, and Asir, measuring 9.5, 6.7, and 5.3 mm, respectively. Regarding the decoupling approaches, the moderate species factor (K_s) of WUCOLS was compared to the three levels of managed stress (K_sm) in LIMP, categorized as “low, moderate, and high”. The statistical analysis revealed a significant advantage of K_s moderate over K_sm low, with a 37.5% reduction in the average ET_PLT. Although no significant differences were observed between moderate K_s and K_sm, the ET_PLT derived from WUCOLS was 16.7% lower than that from LIMP. Conversely, the advantage shifted towards K_sm high, which demonstrated a 20% decrease in the ET_PLT estimates. These results support the Saudi Green Initiative by furnishing essential data for sustainable water management in arid regions, promoting a science-driven approach to enhance water use efficiency and alleviate water scarcity.