Fahad Khan Khadim

Sea surface salinity ( SSS ) is a key parameter in the thermohaline circulation of global oceans. Near the megadeltas, inland streamflow through large catchments plays a crucial role in mediating salinity. While some regional studies have investigated how SSS is impacted through water cycle and climate components, a global scale quantification of inland streamflow contribution on SSS variability is lacking. Here, we utilized remote sensing and observation-driven datasets to quantify the statistical associations between SSS and streamflow ( Q basin ) at 48 megadeltas worldwide. This study uncovers a robust negative association between Q basin and SSS , with correlation coefficients R less than - 0.6 for seasonal data found in 26 of the 48 megadeltas, and less than - 0.4 for deseasoned data in 21 megadeltas. The anticorrelation relationship is more pronounced in large deltas, particularly near tropical climates and in river-influenced deltas. The study also underscores the significant roles of climate, morphological, and anthropogenic stratification in impacting the natural influence of freshwater discharge on SSS . By highlighting the interconnected impacts of alterations in terrestrial water cycle upstream and SSS , this work contributes to enhancing our understanding of global ocean and climate circulation patterns and in tackling environmental issues pertaining to marine ecosystems.

Climate change effects on long-term groundwater (GW) resource developments in the Tana Basin, Ethiopia, are a growing concern. Efforts to provide estimates under various climatic uncertainties are lacking in the region. To address this need, we deployed a fine-resolution (500 m) GW model using MODFLOW-NWT for the Tana Basin, Upper the Blue Nile region. The GW model was calibrated based on 98 historical instantaneous well-level measurements (RMSE = 16.36 m, 1.6% of range), and 38 years of monthly lake level data (RMSE = 0.2 m, 6.7% of range). We used the model to simulate long-term climate change impacts by considering two representative concentration pathways, (RCPs) 4.5 and 8.5, from the two extreme global circulation models (MIROC5 for wetter conditions and CSIRO-Mk3 for drier conditions) available in the region. While the MIROC5 simulated GW table (GWT) was found to be stable, the CSIRO-Mk3 simulated GWT exhibited large fluctuations within +2 m to −4 m by 2100 due to climate change. More critical impacts were predicted for the lake, where total lake releases from the baseline scenario were foreseen to be changed by +50% (MIROC5) or −22% (CSIRO-Mk3) by the end of 2100.

Engaging youth and women in data-scarce, least developed countries (LDCs) is gaining attention in the Sustainable Development Goal (SDG) arena, as is using citizen science as a multi-faceted mechanism for data collection, engendering personal empowerment and agency. Involving these populations in citizen science is a powerful synergy that simultaneously addresses the Leave-No-One-Behind promise in the United Nations’ 2030 Agenda, yet most citizen science takes place in the Global North, and attention to LDCs is needed. This article highlights a four-year, four-location, hydrology-focused, interdisciplinary citizen science initiative (CSI) in the Upper Blue Nile Basin, Ethiopia. Through a systematic evaluation, we explore scientific applications of the hydrologic data, as well as the social dimensions in the CSI, towards building a social and technical capacity that supports the SDGs at the local and international scale. In the CSI, Ethiopian high school students received training from local university faculty and graduate students, collecting river stage and groundwater level measurements, and farmers conducted soil resistivity measurements using a novel sensor technology developed for the study area. We found the datasets to be ample for use to locally validate regional groundwater models and seasonal forecasts on soil moisture and streamflow. We conducted written interviews with the students, which revealed their ability to perceive benefits of engagement in the CSI, as well as recognize their increased individual technical capacity. An analysis of the hydrological data demonstrates the readiness of the datasets to be used for evaluating water-related interventions that facilitate the SDGs, broadly, by building synergies between individuals and institutions. As such, we map how both the hydrologic data and experiences of the citizen scientists support the SDGs at the Goal and Target-level, while forging new social and technical pathways.

In view of Ethiopia’s significant renewable energy (RE) potential and the dynamic interactions among the components of the Water–Energy–Food (WEF) Nexus, we attempted to incorporate solar and small-scale hydropower into the optimal design of an environmentally friendly microgrid with the primary goal of ensuring the sustainability of irrigation water pumping, while taking advantage of existing infrastructure in various small administrative units (kebele). Any additional generated energy would be made available to the community for other needs, such as lighting and cooking, to support health and food security and improve the general quality of life. The novelty of the study stems from the utilization of in situ social data, retrieved during fieldwork interviews conducted in the kebele of interest, to ascertain the actual needs and habits of the local people. Based on these combined efforts, we were able to formulate a realistic energy demand plan for climatic conditions typical of Sub-Saharan Africa agricultural communities and analyze four different scenarios of the microgrid’s potential functionality and capital cost, given different tolerance levels of scheduled outages. We demonstrated that the RE-based microgrid would be socially and environmentally beneficial and its capital cost sensitive to the incorporation of individual or communal machines and appliances. Ultimately, the social impact investigation revealed the design would be welcomed by the local community, whose members already implement tailor-made solutions to support their agricultural activities. Finally, we argue that extended educational programs and unambiguous policies should be in place before any implementation to ensure the venture’s sustainability and functionality.

A Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved in this study to optimize environmental sustainability of flood control, drainage, and irrigation (FCDI) projects in the deltaic regions of Bangladesh. The model optimizes the value of integrated resource benefit, a dimensionless variable defined to measure the environmental sustainability based on the water, agricultural and ecological resources, with a set of project interventions being the major drivers. The resource benefits were evaluated with the help of several indicators, such as flood, navigability, salinity, water logging, cropping intensity, land loss and vegetation. The solution of MINLP model provided optimal values of the decision variables, which are the quantities of project interventions (e.g. length and height of dike, number of sluices and drainage inlets, lengths of drainage canals, erosion protection and afforestation works). The approach and the MINLP formulation presented in this study can be used for any real-life FCDI project improvements.

The salinity of Florida Bay has been increasing for decades due to anthropogenic interventions and natural disturbances of natural sheetflow through the Everglades and adjacent natural systems. Yet little data has been gathered and geospatially analyzed. This study developed a set of multivariate hydro-spatial statistical models to predict the dry and wet season surface salinity concentrations in Florida Bay. The modeling was carried out using in-situ salinity observations from USGS for 2004-2006, and processed Landsat TM 4-5 images for the closest 21 days. To improve the accuracy, the study area was clustered in 3 sub-regions leading to 6 cases (3 sub-regions * 2 seasons). The statistical model produced R² values ranging from 0.53 to 0.83, which are acceptable considering the large size and geographic complexity of the study domain. The two salinity maps created for the Florida Bay area provide a visual representation of the temporal and spatial variation between seasons.