article · Journal of Hydrology
Floods cause severe damage and loss of life across the Maghreb countries of Algeria, Morocco, and Tunisia. Limited access to river discharge records previously hindered regional flood hazard studies and frequency estimation tools. To address this, a regional dataset of daily discharge spanning 1960 to 2018 across 98 basins was compiled. Trend analysis of flood events revealed no significant regional changes in flood frequency or size over this period. Using this data, an envelope curve of maximum floods relative to catchment area was created, alongside models to estimate flood quantiles at ungauged locations using physical catchment features such as soil types, land use, and elevation. Testing multiple linear regression and machine learning methods showed that Lasso regression offered the best predictive performance, relying heavily on variables such as topographic wetness index, altitude, rainfall, and soil bulk density.
Designing safe infrastructure like bridges, dams, and drainage requires knowing expected flood levels, but many river locations lack measurement gauges. By identifying reliable statistical methods and key catchment features to estimate flood risks across North Africa, planners and engineers can better prepare for extreme water events even where direct river flow data does not exist.
The findings could directly assist civil engineering firms, water management authorities, and infrastructure planners tasked with sizing hydraulic structures at ungauged river sites. Because the models have been developed and tested against historic regional data, the work represents an applied statistical tool ready to inform operational engineering guidelines, though adoption depends on standardisation into local building and planning codes.
AI-generated from the published abstract. Always read the original work before citing.
The Maghreb countries located in North Africa are strongly impacted by floods, causing extended damage and numerous deaths. Until now, the lack of accessibility of river discharge data prevented regional studies on potential changes in flood hazards or the development of regional flood frequency estimation methods. A new database of daily river discharge data for 98 river basins located in Algeria, Morocco, and Tunisia, has been compiled, with an average of 36 years of complete records over the time period 1960–2018. A peaks-over-threshold sampling of flood events is considered first to detect trends in the annual frequency and the magnitude of floods. The trend analysis results revealed no significant changes in flood frequency or magnitude at the regional level, with only a few spurious trends due to isolated extreme or clustered events. An envelope curve relating maximum floods for a range of catchment areas in North Africa has been developed, for the first time in this region with such a large database. Then, regional estimation methods for flood quantiles were compared. The regional estimation from multiple catchment characteristics (including soil types, land use, elevation, and geology) was performed by comparing two multiple linear regression methods, Stepwise regression and Lasso regression, and a machine learning algorithm, Random Forests. Results indicate a better performance of the Lasso regression to estimate flood quantiles at ungauged locations, with mean absolute relative errors close to 50 % and relative bias close to 20 %. The most relevant catchment predictors identified by the regression models are the topographic wetness index, which provides better estimates than catchment area, but also altitude, mean annual rainfall, and soil bulk density. The results of this study could be useful to improve operational procedures for sizing hydraulic structures at ungauged sites.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.jhydrol.2024.130678
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
