Title

Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran.

SelectedWorks Author Profiles:

Joseph M. Smoak

Document Type

Article

Publication Date

2016

Date Issued

January 2016

Date Available

November 2016

ISSN

1434-4483

Abstract

Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968–2011) and future (2011–2030, 2045–2065, and 2080–2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011–2030, 2045–2065, and 2080–2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (∼+3.03 °C) and maximum (∼+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann–Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods (p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation (p value = 0.0027), minimum (p value = 0.000000029) and maximum (p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.

Comments

Abstract only. Full-text article is available through licensed access provided by the publisher. Published in Theoretical and Applied Climatology 126(1), 35-49. doi: 10.1007/s00704-015-1552-5. Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

Springer

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.