Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Abbaspour KC, Faramarzi M, Ghasemi SS, Yang H (2009) Assessing the impact of climate change on water resources in Iran. Water Resour Res 45:1–16

    Article  Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  Google Scholar 

  • AliZahed M, Rouhani F, Mohajeri S, Bateni F, Mohajeri L (2010) An overview of Iranian mangrove ecosystems, northern part of the Persian Gulf and Oman Sea. Acta Ecol Sin 30:240–244

    Article  Google Scholar 

  • Alongi DM (2008) Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuar Coast Shelf Sci 76:1–13

    Article  Google Scholar 

  • Andrews TJ, Clough BF, Muller GJ (1984) Photosynthetic gas exchange properties and carbon isotope ratios of some mangroves in North Queensland. In: Teas HJ (ed) Physiology and management of mangroves. W Junk, The Hague, pp 15–23

    Chapter  Google Scholar 

  • Ball MC, Cowan IR, Farquhar GD (1988) Maintenance of leaf temperature and the optimization of carbon gain in relation to water loss in a tropical mangrove forest. Aust J Plant Physiol 15:263–276

    Article  Google Scholar 

  • Brandsma T, Buishand TA (1998) Simulation of extreme precipitation in the Rhine basin by nearest-neighbor resembling. Hydrol Earth Syst Sci 2(2/3):195–209

    Article  Google Scholar 

  • Breithaupt JL, Smoak JM, Smith TJ, Sanders CJ, Hoare A (2012) Mangrove organic carbon burial rates: strengthening the global budget. Glob Biogeochem Cycles 26:GB3011

    Article  Google Scholar 

  • Christensen J, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli R, Kwon W-T, Laprise R, Rueda VM, Mearns L, Menéndez C, Rنisنnen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Quin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 847–940

    Google Scholar 

  • Clough BF, Attiwill PM (1982) Primary productivity of mangroves. In: Clough BF (ed) Mangroves ecosystems in Australia. Chapter 12. Australian Institute of Marine Science. Australian National University Press, Canberra, pp 213–222

    Google Scholar 

  • Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London

    Book  Google Scholar 

  • Cooley D et al (2007) Bayesian spatial modeling of extreme precipitation return levels. J Am Stat Assoc 102:824–840

    Article  Google Scholar 

  • Demarta S, McNeil AJ (2005) The t copula and related copulas. Int Stat Rev 73(1):111–129

    Article  Google Scholar 

  • Duarte CM, Cebrian J (1996) The fate of marine autotrophic production. Limnol Oceanogr 41:1758–1766

    Article  Google Scholar 

  • Duarte CM, Losada IJ, Hendriks IE, Mazarrasa I, Marbà N (2013) The role of coastal plant communities for climate change mitigation and adaptation. Nat Clim Chang 3:961–968. doi:10.1038/nclimate1970

    Article  Google Scholar 

  • El Adlouni S, Bobeé B, Ouarda TBMJ (2008) On the tails of extreme event distributions in hydrology. J Hydrol 355:16–33

    Article  Google Scholar 

  • Ellison J (2000) How South Pacific mangroves may respond to predicted climate change and sea level rise. In: Gillespie A, Burns W (eds) Climate Change in the South Pacific: Impacts and Responses in Australia, New Zealand, and Small Islands States. Kluwer Academic Publishers, Dordrecht, pp 289–301, Chapter 15

    Chapter  Google Scholar 

  • Ellison J (2004) Vulnerability of Fiji’s mangroves and associated coral reefs to climate change. Prepared for the World Wildlife Fund. University of Tasmania, Launceston

    Google Scholar 

  • Etemadi H, Samadi SZ, Sharifikia M (2012) Statistical downscaling of climatic variables in Shadegan Wetland Iran. Earth Sci Clim Chang 1:508. doi:10.4172/scientificreports.508

    Google Scholar 

  • Etemadi H, Samadi S, Sharifikia M (2014) Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Clim Dyn. doi:10.1007/s00382-013-1855-0

    Google Scholar 

  • Gilleland E, Katz RW (2014) Extremes 2.0: an extreme value analysis package in r. submitted to journal of statistical software

  • Gilman EL, Ellison J, Duke NC, Field C (2008) Threats to mangroves from 9 climate change and adaptation options: a review. Aquat Bot 89:237–250

    Article  Google Scholar 

  • Gumbel EJ (1958) Statistics of extremes, Mineola, NY: Dover., (1958) statistics of extremes. Columbia University Press, New York

    Google Scholar 

  • Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143

    Article  Google Scholar 

  • Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Chapman and Hall, London

    Google Scholar 

  • Hipel KW, McLeod AI (1994) Time series modeling of water resources and environmental systems. Elsevier, Amsterdam

    Google Scholar 

  • IPCC (2013) Climate change, 2013. The physical science basis. Summary for policymakers. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 18

    Google Scholar 

  • IPCC (Intergovernmental Panel on Climate Change) (2007) Climate change 2007: the physical science basis. In: Solomon SD, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 1056

    Google Scholar 

  • Kairo JG (2001) Ecology and restoration of mangrove systems in Kenya. PhD thesis, APNA. Vrije Universiteit Brussels, Belgium

    Google Scholar 

  • Kathiresan K, Bingham BL (2001) Biology of mangroves and mangrove ecosystems. Adv Mar Biol 40:81–251

    Article  Google Scholar 

  • Katz R et al (2002) Statistics of extremes in hydrology. Adv Water Resour 25:1287–1304

    Article  Google Scholar 

  • Khan MS, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382

    Article  Google Scholar 

  • Krauss KW, Lovelock CE, McKee KL, Lopez-Hoffman L, Ewe SML, Sousa WP (2008) Environmental drivers in mangrove establishment and early development: a review. Aquat Bot 89:105–127

    Article  Google Scholar 

  • Larson DL (1995) Effects of climate on numbers of northern prairie wetlands. Climate Change 30:169–180

    Article  Google Scholar 

  • Leadbetter MR, Lindgren G, Rootzen H (1983) Extremes and related properties of random sequences and processes, Springer series in statistics. Springer, New York

    Book  Google Scholar 

  • Leclerc M, Ouarda TBMJ (2007) Non-stationarity regional flood frequency analysis at ungauged sites. J Hydrol 343:254–265

    Article  Google Scholar 

  • McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. CRC Press, Boca Raton

    Book  Google Scholar 

  • McMillan C (1971) Environmental factors affecting seedling establishment of the black mangrove on the central Texas coast. Ecology 52:927–930

    Article  Google Scholar 

  • Meehl G, Stocker T, Collins W, Friedlingstein P, Gaye A, Gregory J, Kitoh A, Knutti R, Murphy J, Noda A, Raper S, Watterson I, Weaver A, Zhao Z-C (2007) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 747–846

    Google Scholar 

  • Mehrotra R, Sharma A (2009) Evaluating spatio-temporal representations in daily rainfall sequences from three stochastic multi-site weather generation approaches. Adv Water Resour 32(6):948–962

    Article  Google Scholar 

  • Mezghani A, Hingray B (2009) A combined downscaling-disaggregation weather generator for stochastic generation of multisite hourly weather variables over complex terrain: development and multi-scale validation for the upper hone river basin. J Hydrol 377(3–4):245–260

    Article  Google Scholar 

  • Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management. Science 319:573–574. doi:10.1126/science.1151915

    Article  Google Scholar 

  • Moore RT, Miller PC, Ehleringer J, Lawrence W (1973) Seasonal trends in gas exchange characteristics of three mangrove species. Photosynthetica 7:387–394

    Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith T. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50 (3):885–900 DOI: 10.13031/2013.23153

  • Mosaedi A, Zare Abyane H, Ghabaei Sough M, Samadi SZ (2015) Long-lead drought forecasting using equiprobability transformation function for reconnaissance drought index. Water Resour Manag. doi:10.1007/s11269-015-0944-8

    Google Scholar 

  • Murakami Y, Tsuyama M, Kobayashi Y, Kodama H, Iba K (2000) Trienoic fatty acids and plant tolerance of high temperature. Science 287:476–479

    Article  Google Scholar 

  • Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Gruebler A, Yong Jung T, Kram T, Lebre La Rovere E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) IPCC special report on emissions scenarios. Cambridge University Press, Cambridge

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part 1. A discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Pettitt AN (1979) A non-parametric approach to the change-point problem. Appl Stat 28:126–135

    Article  Google Scholar 

  • Poiani KA, Johnson CW (1991) Global warming and prairie wetlands. Bioscience 41:611–618

    Article  Google Scholar 

  • Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41

    Article  Google Scholar 

  • Renard B, Lang M, Bois P (2006) Statistical analysis of extreme events in a nonstationary context via a Bayesian framework: case study with peak-over threshold data. Stoch Env Res Risk A 21:97–112

    Article  Google Scholar 

  • Samadi S, Wilson CAME, Moradkhani H (2013) Uncertainty analysis of statistical downscaling models using Hadley centre coupled model. Theor Appl Climatol 114:673–690

    Article  Google Scholar 

  • Sanders CJ, Smoak JM, Naidu AS, Sanders LM, Patchineelam SR (2010) Organic carbon burial in a mangrove forest, margin and intertidal mud flat. Estuar Coast Shelf Sci 90(3):168–172. doi:10.1016/j.ecss.2010.08.013

    Article  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464

    Article  Google Scholar 

  • Semenov MA, Brooks RJ (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Clim Res 11:137–148

    Article  Google Scholar 

  • Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS- WG stochastic weather generators for diverse climates. Clim Res 10:95–107

    Article  Google Scholar 

  • Sharifi F, Samadi SZ, Wilson CAME (2012) Causes and consequences of recent floods in the Golestan catchments and Caspian Sea regions of Iran. Nat Hazards 61:533–550

    Article  Google Scholar 

  • Singh J, Knapp HV, Demissie M (2004) Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004–08. Champaign, Ill.: Illinois State Water Survey. Available at: www.sws.uiuc.edu/pubdoc/CR

  • Snedaker SC, Araujo RJ (1998) Stomatal conductance and gas exchange in four species of Caribbean mangroves exposed to ambient and increased CO2. Mar Freshw Res 49:325–327

    Article  Google Scholar 

  • Snedaker SC, Meeder JF, Ross MS, Ford RG (1994) Discussion of Ellison, Joanna C. and Stoddart, David R., 1991 mangrove ecosystem collapse during predicted sea-level rise: Holocene analogues and implications. J Coast Res 10:497–498

    Google Scholar 

  • Sokolov AP, Stone PH, Forest CE, Prinn R, Sarofim MC, Webster M, Paltsev S, Schlosser CA, Kicklighter D, Dutkiewicz S, Reilly J, Wang C, Felzer B, Jacoby HD (2009) Probabilistic forecast for 21st century climate based on uncertainties in emissions (without Policy) and climate parameters. Report no. 169. Joint Program on the Science and Policy of Global Change, Cambridge

    Google Scholar 

  • Stillman JH (2003) Acclimation capacity underlies susceptibility to climate change. Science 301:65

    Article  Google Scholar 

  • Tiab Z, Danehkar A (2008) Investigation of mangrove structure over Jask Mangrove protected areas. Master of Science thesis. Azad university of Bandar Abbas, Bandar Abbas, pp 20–45

    Google Scholar 

  • Tomanek L, Somero GN (1999) Evolutionary and acclimation-induced variation in the heat-shock responses of congeneric marine snails (genus Tegula) from different thermal habitats: implications for limits of thermotolerance and biogeography. J Exp Biol 202:2925–2936

    Google Scholar 

  • Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin JM, Hoegh-Guldberg O, Bairlein F (2002) Ecological responses to recent climate change. Nature 416:389–395

    Article  Google Scholar 

  • Westra S, Alexander LV, Zwiers FW (2013) Global increasing trends in annual maximum daily precipitation. J Climatol 26:3904–3918

    Article  Google Scholar 

  • Wilby RL, Tomlinson OJ, Dawson CW (2003) Multi-site simulation of precipitation by conditional resampling. Clim Res 23:183–194

    Article  Google Scholar 

  • Withey P, Kooten GC (2011) The effect of climate change on optimal wetlands and waterfowl management in Western Canada. Ecol Econ 70:798–805

    Article  Google Scholar 

Download references

Acknowledgments

This research was sponsored by Tarbiat Modares University (TMU) Department of Environmental Science. The authors appreciate those persons and agencies that assisted in accessing research data and field study. Special thanks are due to USGS and University of South Florida for helping in sample analysis. The authors would like to thank extRemes developers, Eric Gilleland, and Richard W. Katz from the National Center for Atmospheric Research (NCAR) for making this package freely available in R.

Author information

Authors and Affiliations

  1. Department of Environmental Science, Persian Gulf Research Institute, Persian Gulf University, P.O. Box: 7516913817, Bushehr, Iran

    Halimeh Etemadi

  2. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, 29208, USA

    S. Zahra Samadi

  3. Department of Remote Sensing, Tarbiat Modares University, Tehran, Iran

    Mohammad Sharifikia

  4. Department of Environmental Science, Policy and Geography, University of South Florida, Florida, 33701, USA

    Joseph M. Smoak

Authors
  1. Halimeh Etemadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Zahra Samadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Sharifikia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph M. Smoak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Halimeh Etemadi or Mohammad Sharifikia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Etemadi, H., Samadi, S.Z., Sharifikia, M. et al. Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran. Theor Appl Climatol 126, 35–49 (2016). https://doi.org/10.1007/s00704-015-1552-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-015-1552-5

Keywords

Search

Navigation