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Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data-worth analysis in order to conduct a three-dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start-up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.

Estimating spatiotemporal maps of greenhouse gases (GHGs) is important for understanding climate change and developing mitigation strategies. However, current methods face challenges, including the coarse resolution of numerical models, and gaps in satellite data, making it essential to improve the spatiotemporal estimation of GHGs. This study aims to develop an advanced technique to produce high-fidelity (1 km) maps of CO2 and CH4 over the Arabian Peninsula, a highly vulnerable region to climate change. Using XGBoost, columnar carbon dioxide (XCO2) and methane (XCH4) concentrations using satellite data from OCO-2 and Sentinel-5P (the target variables) were downscaled, with ancillary data from CarbonTracker, MODIS Terra, and ERA-5 (the input variables). The model is trained and validated against these datasets, achieving high performance for XCO2 (R2 = 0.98, RMSE = 0.58 ppm) and moderate accuracy for XCH4 (R2 = 0.63, RMSE = 13.26 ppb). Seasonal cycles and long-term trends were identified, with higher concentrations observed in summer, and emission hotspots in urban and industrial areas. Comparisons with the EDGAR inventory highlighted the significant contributions of the power, oil, and transportation sectors to GHG emissions. These results demonstrate the value of high-resolution data for local-scale monitoring, supporting targeted mitigation strategies and sustainable policymaking in the region. Future work could integrate ground-based observations to further enhance GHG monitoring accuracy. [ABSTRACT FROM AUTHOR]

The potential impacts of projected future climate change scenarios on the hydrologic response of a water-stressed Mediterranean river basin (Upper Litani River Basin in Lebanon) are quantified and assessed using the Water Evaluation and Planning (WEAP) model. Projected basin-level changes in water availability are then compared to multi-sector demands estimated under six basin-level development scenarios. The sustainability under these scenarios and the resilience of the system in the face of the projected climatic changes are then assessed in terms of a water resources index, demand reliability, demand satisfaction index, demand reliability index and the average duration of failure. The results reveal that the basin is expected to experience significant alteration in its hydrologic cycle and that current plans envisioning an increase in irrigated areas within the basin, is non-sustainable and will lead to a highly water stressed system. A conservative basin-level plan that integrates both supply- and demand-side measures is proposed in an effort to achieve a more sustainable system.

The American University of Beirut's Interfaculty Graduate Environmental Sciences Program was launched in 1997 as a means of addressing salient issues on the environment and development in Lebanon and the Arab World using an interdisciplinary approach. The programme adopts a student-centred learning approach and aims to develop critical and systems thinking skills to produce socially and environmentally conscious leaders and agents of change in the Arab World. In this paper, we provide an evaluation of the programme's ESD dimensions using the criteria of interdisciplinarity, local relevance and competence-based learning. This is followed by a critical analysis of the programme's potential for use as a model in the Arab World. We find that, while the model may be useful in providing inspiration and a good practice case, its transfer "as is" to institutions of higher education in the Arab World or even in Lebanon is unlikely, and perhaps undesirable, in view of the inherently diverse nature of Arab universities.