Bisrat Elias Cholo

Conceptual diagram of three input streams: satellite remote sensing, IoT sensors, and citizen science. These inputs are processed using data fusion and a machine learning layer to provide near real-time water quality outputs and alerts. In semiarid and tropical regions, freshwater ecosystems are facing an increasing pressure from agricultural run-off, urban effluents, hydrologic alterations, and climate variability; yet, effective monitoring is hindered by sparse, irregular observations and limited institutional capacity. A total of 44 peer-reviewed studies from 2005 to 2025 (based on PRISMA 2020 guidelines) were collected for this systematic review, and the thematic synthesis of the results focuses on remote sensing (RS), machine learning (ML), Internet of Things (IoT) sensors, big data (BD) platforms, and citizen science to create near real-time monitoring capabilities for surface water quality. All studies originated from Sub-Saharan Africa (47%), Asia (32%), South America (14%), and other parts of the world (7%). RS had the highest percentage of use as a technology at 38%, ML and predictive modeling had a 25% use rate, IoT was at 14%, BD platforms accounted for 11%, citizen science made up 7%, and empirical methods at the watershed level made up the remaining 5%. RS-based models obtained estimates for chlorophyll a and turbidity using the Sentinel-2 and Landsat 8 satellites with R2 values between 0.72 and 0.94, while ML models (specifically random forest and support vector machine) maintained consistency in achieving root mean square error values below 10%. IoT and citizen science have the capacity to collect valuable local data but have not been adequately incorporated into satellite analytics or local calibrations with ML models. The main challenges to integrating these data sources into a coherent monitoring framework include (1) the absence of consistent time series; (2) the low resolution of satellite images; (3) very few ML models can be transferred between study areas; and (4) there is no interoperable platform to integrate and analyze multiple sources of information that can be used for surface water quality monitoring. Transitioning to a modular RS–ML–IoT framework that includes standardized citizen science methodologies and local calibrations of the ML models would enable scalable, context-specific early warning systems across all data-scarce regions, such as the East African Rift Valley.

Understanding the sources of precipitation and their impacts is crucial for basin-wide water balance research. Previous research concentrated on the sources of moisture in Ethiopia. The southern part’s moisture sources, however, were not investigated. The primary objective of this study is to trace the source of atmospheric moisture in the Abaya-Chamo sub-basin of southern Ethiopia using numerical water vapor tracers like Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Exploring the possible regions of atmospheric vapor roots and the path of moist air initiating rainfall that reaches the basin was feasible for the year 2018–2020. The anticyclone from the Arabian High, which is positioned in the Arabian and Mediterranean seas, was the primary source of moisture supply in the study area during the Belg (March to May) season, according to the back trajectory cluster analysis results. Additionally, the Indian Ocean adds moisture resulting from Mascarene highs brought by equatorial easterlies. Furthermore, during Kiremt (June to September), air masses from the Congo basin were the potential moisture source region for the study areas in combination with air masses originating from the Mascarene highs, located in the South Indian Ocean, and the St. Helena high, centered in the subtropical southern Atlantic Ocean. This study primarily focuses on the complex dynamics of atmospheric moisture sources around Abaya-Chamo sub-basin of southern Ethiopia, offering insight into seasonal fluctuations and contributing various components. These findings contribute to basin-specific water balance research by filling gaps in the previous studies.

Stable isotopes in precipitation and vapor are a powerful tool for tracing the origin of moisture and mixing processes. This paper discusses time and space variation of δ18O in precipitation and controlling features over upper Blue Nile Basin using data from GNIP, observed data in 2014 and simulated data by AGCM. IsoGSM simulation in precipitation was verified with observation. The δ18O variation shows clear seasonality with the lowest 18O values in August and dry season, and enriched 18O in spring, June and September. Spring sample is enriched compared to summer, and assumed to be related with moisture sources. More enriched isotopes in spring and lower d-excess could be related to the source of air masses in short travel path from North Indian Ocean, Mediterranean and Red sea while summer rain is depleted with larger d-excess could be related to longer travel path of moisture from south Indian Ocean with mixing of potential evaporated moisture from open surface and transpired moisture from Congo vegetation and also from Gulf of Guinea. The isotopic statistics of three stations shows maximum, minimum and average value of (8.23‰, -11.73‰, 0.04‰) in Addis Ababa, (5.26‰, -12.74‰, and -2.52‰) in Entoto Hill and (4.08‰, -9.65‰, 2.41‰) in Debremarkos respectively. The δ2H- δ18O relationships, monthly weighted d-excess variation in the Basin revealed the temporal variation of δ18O in precipitation is essentially shaped by the source of the moisture and spatial differences is due to Rayleigh rainout effect along the moisture trajectory. The source of moisture is primarily controlled by the north south movement of ITCZ within the Basin. The study recommends the use of model simulated δ18O as good alternative for hydrological and hydrologeological investigations when needed.