Assessing the effects of climate and land use/land cover changes on runoff in the Mangrove Forests of the Northern Persian Gulf

Abstract

Mangrove forests are increasingly threatened by rapid Land Use/Land Cover (LULC) changes and climate variability. This study analyzes spatio-temporal dynamics of the Nayband mangrove forests in the northern Persian Gulf, Iran, for 1990, 2003, 2022, and a projected 2030, using a Markov chain and Multi-Layer Perceptron Artificial Neural Network. Additionally, maximum 24-h rainfall data (2003–2017) were analyzed using the Weibull method to estimate return periods of 2, 5, 10, 50, and 100 years. To assess climate change impacts, daily rainfall data from ACCESS-ESM1-5, HadGEM3-GC31-LL, and MRI-ESM2-0 models under SSP126, SSP245, and SSP585 scenarios were downscaled using the LARS-WG model. The Soil Conservation Service method estimated runoff height and volume under various climate change and LULC conditions. Under 2030 LULC projections relative to 2022, runoff volumes are expected to decline by 4.88 %, 3.15 %, 2.44 %, 1.09 %, and 0.96 % across the aforementioned return periods under SSP126. SSP245 shows smaller reductions of 1.51 %, 1.02 %, 0.78 %, 0.15 %, and 0.65 %. In contrast, the SSP585 scenario projects an increase in runoff volume, with corresponding rises of 16.28 %, 12.09 %, 21.32 %, 9.59 %, and 9.21 %. The analysis revealed that runoff variability was more significantly affected by climate change than by LULC change. Accordingly, the findings of this study provide a valuable foundation for shaping management strategies focused on the restoration and expansion of mangrove forests, while also supporting informed development planning within this predominantly industrial region, defined by petrochemical, oil, and gas operations.