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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.

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Irrigation agriculture in Ethiopia can be improved by applying appropriate irrigation levels. Since water scarcity is the major problem in Ethiopia, and farmers apply water without knowledge of the amount of water to be applied, appropriate irrigation levels for maize crops should be investigated in the central Gondar zone, Ethiopia. This paper aims to investigate the effect of deficit levels of irrigation on crop parameters and evaluate the AquaCrop model for its predictability potential of water productivity. The experiment has four levels of water application (Full Irrigation (100%), 75%, 50%, and 25% of crop evapotranspiration) at 10 days of irrigation interval using Randomized Complete Block Design with three replications. Data collected in two experiments in the different seasons were soil moisture, canopy cover, biomass, and final yield. As high R 2 (0.93) and Nash–Sutcliffe Efficiency (NSE) (0.91) values indicated, the model performed well in simulating canopy cover, above-ground biomass, and yield in all treatments except 25% full irrigation (FI) with prolonged water deficit. Grain yield measured from experiment 2 was within the range of 4.6 t/ha to 7.4 t/ha. Even though a high yield was found from FI, the measured water use efficiency was better in 75% FI treatment, indicating a potential for water-saving by this treatment than FI. Higher grain yield was observed for maize sown in January at experiment 1. This was attributed to the rainfall impact on the experiment since it was spring season in Ethiopia at which some rainfall in the region is pronounced. In addition, AquaCrop thoroughly underestimated the seasonal evapotranspiration values and the deviations were commonly bigger as stress levels increased. Therefore, AquaCrop can be used in the simulation of crop parameters, prediction of irrigated outputs, and assessing the impact of irrigation scheduling.

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