Earth

Coastal flooding poses a major threat to urbanized coastlines, where extreme marine forcing interacts with complex coastal morphology and concentrated human activities. This study investigates storm-induced marine flooding along the Sidi Moussa-Salé coastline, Morocco, using the two-dimensional Iber hydrodynamic model. Simulations were conducted for the January 2014 and February 2017 storm events using atmospheric, wave, water-level, topographic, and bathymetric data. The results reveal a spatially heterogeneous response, with the 2014 event producing a maximum inland inundation extent of approximately 0.20 km and a flooded area of 0.20 km², compared with 0.15 km and 0.10 km², respectively, in 2017. The simulated inundation patterns highlight the influence of coastal morphology, localized depressions, and preferential flow pathways on flood propagation. Spatial exposure mapping indicates an overlap between flood-prone areas and coastal roads, residential and commercial zones, recreational facilities, and other public assets. Field observations provide qualitative contextual evidence of storm-related impacts, although the absence of surveyed flood boundaries and measured water levels limits quantitative model validation. Future simulations, based on the February 2017 storm forcing and prescribed sea-level-rise increments, indicate an expanded inundation footprint under the examined scenarios. Under RCP8.5-2100, the maximum inland extent reaches 0.42 km and the flooded area 0.32 km², compared with 0.15 km and 0.10 km² under the 2017 baseline. These results constitute conditional sensitivity assessments rather than deterministic forecasts, given the uncertainties in model inputs and the omission of future changes in storm forcing and coastal morphology. The study provides a site-specific basis for identifying potentially exposed coastal sectors and supporting hazard-informed coastal planning, while highlighting the need for improved observational validation and systematic uncertainty assessment.

Forest loss in the Amazon links land conversion to carbon emissions and may alter the climatic conditions that support agriculture. This study examines how these relationships unfold within and across years using regional observations for 1990–2023. Exploratory analyses and a Bayesian vector autoregression relate deforestation, temperature, precipitation, gross land use emissions, and agricultural value added, accounting for Pacific and Atlantic climate variability and a linear trend. Deforestation and emissions remain grouped after removing trends, whereas the broader climatic and agricultural groupings change. Changes in deforestation and emissions are positively associated within the same year: a one standard deviation unexpected increase in annual deforestation change corresponds to an estimated emissions change response of 115.87 million tCO₂e (95% credible interval: 56.33–192.20 million). Subsequent responses are too uncertain to establish how long this association persists. Temperature, precipitation, and agricultural responses also remain imprecisely estimated. The principal contribution is an integrated assessment distinguishing detectable annual forest loss–inventory co-movement from unresolved later hydroclimatic and agricultural responses. Related land change inputs in the emissions inventory limit the independence of the carbon finding. These results support joint monitoring of forest loss and gross emissions, complemented by spatial and seasonal indicators of climatic regulation and agricultural vulnerability.

Quantifying and knowing land use and land cover (LULC) change remains significant for tracking environmental changes and evaluating changes in a watershed’s hydrological response. This study applies the Natural Resources Conservation Service Curve Number (NRCS-CN) method to estimate surface runoff response to LULC change in the uMlaas catchment between 1995 and 2025, using four LULC scenarios (1995, 2005, 2015, and 2025), hydrological soil groups (HSGs), and fixed mean monthly and annual rainfall depths. Because the curve number (CN) values are assigned according to LULC types and HSGs, runoff estimates under this method are structurally linked to LULC composition; thus, this study quantifies the magnitude of this linkage by examining LULC change over the study period, rather than testing it as an independent hypothesis. LULC change analysis shows rapid and sustained urban expansion at the expense of grassland and cultivated land, while natural and planted forests increased modestly over the same period. The urban expansion is closely mirrored by the model-derived annual runoff, which rose from 442.55 mm to a peak of 485.44 mm in 2015 before declining slightly by 472.96 mm in 2025, remaining higher than in 1995. Simulated runoff peaks in October–April, consistent with rainfall seasons, and concentrated in the central to eastern region, coinciding with urban expansion. These results illustrate how, under the assumptions of the NRCS-CN method, the observed LULC transitions in uMlaas, particularly urban expansion and loss of vegetated land cover, translate into elevated simulated runoff. Land use planning within the catchment should prioritise runoff control, particularly in the central to eastern region (in the city of Durban).