Climate change poses a significant threat to agricultural productivity, particularly in regions vulnerable to extreme temperatures and water scarcity, such as Irbid, Jordan. This study assesses the future impacts of projected shifts in precipitation and temperature on wheat yields, using the Decision Support System for Agrotechnology Transfer (DSSAT) model for calibrating and validating under local agro-environmental conditions. Two shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5), representing high-emission and fossil-fuel-intensive futures, were evaluated across mid- and late-century periods (2030–2060 and 2070–2100). The DSSAT model was calibrated using local field data to simulate crop phenology, biomass accumulation, and nitrogen dynamics, showing strong agreement with observed grain yield and harvest index, thereby confirming its reliability for climate impact assessments. Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. The results showed that under SSP5-8.5 (2030–2060), precipitation was the dominant factor influencing yield variability, underscoring the critical role of water availability. In contrast, under SSP3-7.0 (2070–2100), rising maximum temperatures became the primary constraint, highlighting the growing risk of heat stress. Predictive accuracy was higher in precipitation-dominated scenarios (R² = 0.81) than in temperature-dominated cases (R² = 0.65–0.73), reflecting greater complexity under extreme warming. These findings emphasize the value of integrating well-calibrated crop models with climate projections and machine learning tools to support climate-resilient agricultural planning. Moreover, practical adaptation strategies, such as adjusting planting dates, using heat-tolerant varieties, and optimizing irrigation, are recommended to enhance resilience. Emerging techniques such as seed priming show promise and merit integration into future crop models. The findings support SDG 2 and SDG 13 by informing climate-resilient food production strategies.

Sediment accumulation in reservoirs represents a critical challenge for sustainable water resources management in semi-arid regions. In Jordan, accelerated sedimentation threatens the operational capacity of major dams, including the King Talal Dam (KTD), which serves as a key water resource in the Amman–Zarqa Basin (AZB). This study assesses sediment yield and erosion risk at the catchment scale using the Soil and Water Assessment Tool (SWAT) integrated with the Modified Universal Soil Loss Equation (MUSLE). The AZB was subdivided into 31 sub-basins and 586 Hydrological Response Units (HRUs) based on land use, soil characteristics, topography, and slope. The model was calibrated for the period 1993–2002 and validated for 2003–2012 using hydrological and sediment observations from 17 monitoring stations. Long-term simulations covering more than two decades were conducted to quantify spatial and temporal sediment yield patterns across the basin. Results indicate a mean annual sediment yield of 2.79 t ha⁻¹ yr⁻¹, corresponding to approximately 0.59 MCM yr⁻¹ of sediment inflow to the reservoir. These estimates closely agree with bathymetric survey results reported by the Jordan Valley Authority, which indicate sedimentation rates of 2.59 t ha⁻¹ yr⁻¹ (0.55 MCM yr⁻¹). Overall, the model demonstrates strong agreement between observed and simulated sediment loads, confirming its reliability for sediment dynamics assessment. The findings are relevant to Sustainable Development Goals (SDGs) 6 (clean water and sanitation) and 15 (life on land) by informing sustainable watershed and soil erosion management practices.

River crossing bridges in low-gradient floodplains can be affected by limited conveyance, backwater control, and scour-related foundation risk. This study evaluates a proposed IL 13 bridge replacement over the Mid Fork Saline River, Illinois, using HEC-RAS 1D steady-flow modeling, hydrologic inputs from USGS StreamStats for a drainage area of 236.45 mi², bridge opening analysis, multiple-opening interpretation, and HEC-18 scour assessment. Natural, existing, and proposed conditions were compared under design floods and Ohio River tailwater scenarios. The proposed bridge increased the effective waterway opening under all evaluated hydraulic scenarios, with increases of approximately 68.5–79.5% under the no-tailwater case, 76.1–76.9% under the 10-year Ohio River tailwater case, and 71.7–72.1% under the 50-year Ohio River tailwater case. Bridge opening velocity decreased by about one-third, indicating lower local hydraulic intensity and improved conveyance through the main opening. Contraction scour was not controlling, while computed pier scour decreased by approximately 8–10% and the controlling right abutment scour decreased slightly. Because empirical HEC-18 scour equations can have large uncertainty, commonly approaching an order of a factor of two in practical scour prediction, these reductions are interpreted only as comparative trends. They do not provide a basis for reducing foundation design requirements, but they indicate that the proposed replacement does not worsen the controlling scour response. Overall, the replacement improves hydraulic compatibility, reduces local hydraulic stress, and does not worsen the governing scour response. The study supports SDG 9, SDG 11, and SDG 13 in a hydraulic-infrastructure sense by promoting resilient bridge serviceability, safer transport connectivity, and adaptation-oriented flood risk assessment; however, full life-cycle carbon, cost, and network-resilience metrics were outside the scope.