Skip to main content

Advertisement

SpringerLink
Log in

Assessing the Vulnerability of a Deltaic Environment due to Climate Change Impact on Surface and Coastal Waters: The Case of Nestos River (Greece)

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

In deltaic areas, riverine and coastal waters interact; hence, these highly dynamic environments are particularly sensitive to climate change. This adds to existing anthropogenic pressures from irrigated agriculture, industrial infrastructure, urbanization, and touristic activities. The paper investigates the estimated future variations in the dynamics of surface and coastal water resources at a Mediterranean deltaic environment for the twenty-first century. Therefore, an Integrated Deltaic Risk Index (IDRI) is proposed as a vulnerability assessment tool to identify climate change impact (CCI) on the study area. For this purpose, three regional climate models (RCM) are used with representative concentration pathways (RCPs) 4.5 and 8.5 for short-term (2021–2050) and long-term (2071–2100) future periods. Extensive numerical modeling of river hydrology, storm surges, coastal inundation, water scarcity, and heat stress on irrigated agriculture is combined with available atmospheric data to estimate CCI on the Nestos river delta (Greece). The IDRI integrates modeling results about (i) freshwater availability covering agricultural demands for three water consumption scenarios, i.e., a reference (REF), a climate change (CC), and an extended irrigation (EXT) scenario, combining river discharges and hydropower dam operation; (ii) inundated coastal areas due to storm surges; and (iii) heat stress on cultivated crops. Sustainable practices on irrigated agriculture and established river basin management plans are also considered for the water demands under combinatory scenarios. The differentiations of model outputs driven by various RCM/RCP combinations are investigated. Increased deltaic vulnerability is found under the RCP8.5 scenario especially for the long-term future period. The projected IDRI demonstrates the need for integrated water resources management when compared with risk indexing of individual water processes in the study area.

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
Fig. 12

Similar content being viewed by others

Data Availability

The level of research datasets/material dissemination is set to “Confidential among Project Partners” within MEDAQCLIM Project, therefore data are not publicly accessible, but may be shared by the authors upon official request.

Notes

  1. https://rsis.ramsar.org/

  2. https://natura2000.eea.europa.eu/

  3. https://www.ktimatologio.gr/en

  4. https://lri.swri.gr/index.php/en/

  5. https://www.pamth.gov.gr/index.php/en/

  6. http://www.hnhs.gr/portal/page/portal/HNHS

  7. http://www.admie.gr/nc/en/home/

Abbreviations

AR5:

5th Assessment Report

CC:

Water consumption scenario for climate change

CCI:

Climate change impact

CFR:

Coastal Flood Risk Index

CMCC:

Climate Model CMCC-CCLM4-8-19 v.1

CNRM:

Climate Model CNRM-ALADIN52 v.1

CVI:

Coastal Vulnerability Index

EXT:

Water consumption scenario for EXTended irrigation

GUF:

Climate Model GUF-CCLM-NEMO4-8-18 v.1

HiReSS:

High-Resolution Storm Surge model

HPP:

Hydropower plant

HRP :

Hit-rate-of-percentiles

HSRI:

Heat Stress Risk Index

HT :

High temperature

HTP :

High temperature probability

IDRI:

Integrated Deltaic Risk Index

IPCC:

Intergovernmental Panel on Climate Change

LTF:

Long-term future

MeCSS:

Mediterranean Climatic Storm Surge model

MED-CORDEX:

MEDiterranean COordinated Regional climate Downscaling EXperiment

MODSUR:

MODelisation du SURface model

MSL:

Mean seal Level

RCM:

Regional climate model

RCP:

Representative concentration pathway

REF:

Reference water consumption scenario

RP:

Reference period

SLP :

Sea level pressure

SSH :

Sea surface height

SSI :

Storm Surge Index

STF :

Short-term future

WCS:

Water consumption scenario

WEAP :

Water evaluation and planning model

WFD:

Water Framework Directive

References

  1. Ericson, J. P., Vörösmarty, C. J., Dingman, S. L., Ward, L. G., & Meybeck, M. (2006). Effective sea-level rise and deltas: causes of change and human dimension implications. Global and Planetary Change, 50(1–2), 63–82.

    Article  Google Scholar 

  2. Pont, D., Day, J. W., Hensel, P., Franquet, E., Torre, F., Rioual, P., et al. (2002). Response scenarios for the deltaic plain of the Rhône in the face of an acceleration in the rate of sea-level rise with special attention to Salicornia-type environments. Estuaries, 25(3), 337–358.

    Article  Google Scholar 

  3. Kuenzer, C., & Renaud, F. G. (2012). Climate and environmental change in river deltas globally: expected impacts, resilience, and adaptation. In F. Renaud, C. Kuenzer (Ed.) The Mekong Delta System. Springer Environmental Science and Engineering, Dordrecht.

  4. Adger, W. N. (2018). Ecosystem services, well-being and deltas: current knowledge and understanding. In R. Nicholls, et al. (Ed.) Ecosystem services for well-being in deltas. Palgrave Macmillan, Cham.

  5. Zhong, S., et al. (2019). Analyzing ecosystem services of freshwater lakes and their driving forces: the case of Erhai Lake, China. Environmental Science and Pollution Research, 26, 10219–10229.

    Article  Google Scholar 

  6. Szabo, S., et al. (2016). Population dynamics, delta vulnerability and environmental change: comparison of the Mekong, Ganges-Brahmaputra and Amazon delta regions. Sustainability Science, 11, 539–554.

    Article  Google Scholar 

  7. Lionello, P., & Scarascia, L. (2018). The relation between climate change in the Mediterranean region and global warming. Regional Environmental Change, 18(5), 1481–1493.

    Article  Google Scholar 

  8. Cramer, W., et al. (2018). Climate change and interconnected risks to sustainable development in the Mediterranean. Nature Climate Change, 8(11), 972–980.

    Article  Google Scholar 

  9. Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta, P., Dubash, N. K., & Edenhofer, O. (2014). IPCC Fifth Assessment Synthesis Report Climate Change 2014 Synthesis Report.

  10. Makarigakis, A. K., & Jimenez-Cisneros, B. E. (2019). UNESCO’s contribution to face global water challenges. Water, 11, 388.

    Article  Google Scholar 

  11. Boone, R. B., Conant, R. T., Sircely, J., Thornton, P. K., & Herrero, M. (2018). Climate change impacts on selected global rangeland ecosystem services. Global Change Biology, 24(3), 1382–1393.

    Article  Google Scholar 

  12. Iglesias, A., Mougou, R., Moneo, M., & Quiroga, S. (2011). Towards adaptation of agriculture to climate change in the Mediterranean. Regional Environment Change, 11(1), 159–166.

    Article  Google Scholar 

  13. Mollema, P., Antonellini, M., Gabbianelli, G., Laghi, M., Marconi, V., & Minchio, A. (2012). Climate and water budget change of a Mediterranean coastal watershed, Ravenna Italy. Environmental Earth Sciences, 65(1), 257–276.

    Article  Google Scholar 

  14. Faysse, N., et al. (2014). Participatory analysis for adaptation to climate change in Mediterranean agricultural systems: possible choices in process design. Regional Environmental Change, 14(1), 57–70.

    Article  Google Scholar 

  15. Ronco, P., Zennaro, F., Torresan, S., Critto, A., Santini, M., Trabucco, A., et al. (2017). A risk assessment framework for irrigated agriculture under climate change. Advances in Water Resources, 110, 562–578.

    Article  Google Scholar 

  16. Ahuja, I., de Vos, R. C. H., Bones, A. M., & Hall, R. D. (2010). Plant molecular stress responses face climate change. Trends in Plant Science, 15, 664–674.

    Article  CAS  Google Scholar 

  17. McClung, C. R., & Davis, S. J. (2010). Ambient thermometers in plants: from physiological outputs towards mechanisms of thermal sensing. Current Biology, 20, 1086–1092.

    Article  CAS  Google Scholar 

  18. Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333, 616–620.

    Article  CAS  Google Scholar 

  19. Hasanuzzaman, M., Nahar, K., Alam, Md., Roychowdhury, R., & Fujita, M. (2013). Physiological, biochemical, and molecular mechanisms of heat stress tolerance in plants. International Journal of Molecular Sciences, 14(5), 9643–9684.

    Article  CAS  Google Scholar 

  20. Masia, S., Sušnik, J., Marras, S., Mereu, S., Spano, D., & Trabucco, A. (2018). Assessment of irrigated agriculture vulnerability under climate change in Southern Italy. Water, 10, 209.

    Article  Google Scholar 

  21. Wang, X., Zhang, J., Ali, M., Shahid, S., He, R., Xia, W., & Jiang, Z. (2016). Impact of climate change on regional irrigation water demand in Baojixia irrigation district of China. Mitigation and Adaptation Strategies for Global Change, 21, 233–247.

    Article  Google Scholar 

  22. Riediger, J., Breckling, B., Svoboda, N., & Schröder, W. (2016). Modelling regional variability of irrigation requirements due to climate change in Northern Germany. Science of the Total Environment, 541, 329–340.

    Article  CAS  Google Scholar 

  23. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration - guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.

    Google Scholar 

  24. Jiří, M. L. S. (1980). Effective rainfall estimation. Journal of Hydrology, 45(3–4), 305–311.

    Google Scholar 

  25. FAO. (1986). Irrigation water management: irrigation water needs. Training manual 3, Food and Agriculture Organization of the United Nations, Rome, Italy.

  26. Kew, S. F., Selten, F. M., Lenderink, G., & Hazeleger, W. (2013). The simultaneous occurrence of surge and discharge extremes for the Rhine delta. Natural Hazards and Earth System Sciences, 13(8), 2017–2029.

    Article  Google Scholar 

  27. Auerbach, L. W., Goodbred, S. L., Jr., Mondal, D. R., Wilson, C. A., Ahmed, K. R., Roy, K., et al. (2015). Flood risk of natural and embanked landscapes on the Ganges-Brahmaputra tidal delta plain. Nature Climate Change, 5(2), 153.

    Article  Google Scholar 

  28. Oude Essink, G. H. P., Van Baaren, E. S., De Louw, P. G. (2010). Effects of climate change on coastal groundwater systems: a modeling study in the Netherlands. Water Resour Res, 46(10).

  29. Sánchez-Arcilla, A., Mösso, C., Sierra, J. P., Mestres, M., Harzallah, A., Senouci, M., & El Raey, M. (2011). Climatic drivers of potential hazards in Mediterranean coasts. Regional Environmental Change, 11(3), 617–636.

    Article  Google Scholar 

  30. Jordà, G., Gomis, D., Álvarez-Fanjul, E., & Somot, S. (2012). Atmospheric contribution to Mediterranean and nearby Atlantic sea level variability under different climate change scenarios. Global and Planetary Change, 80, 198–214.

    Article  Google Scholar 

  31. Calafat, F. M., Jordà, G., Marcos, M., & Gomis, D. (2012). Comparison of Mediterranean sea level variability as given by three baroclinic models. Journal of Geophysical Research, 117, C02009.

    Article  Google Scholar 

  32. Conte, D., & Lionello, P. (2013). Characteristics of large positive and negative surges in the Mediterranean Sea and their attenuation in future climate scenarios. Global and Planetary Change, 111, 159–173.

    Article  Google Scholar 

  33. Androulidakis, Y., Kombiadou, K., Makris, C., Baltikas, V., & Krestenitis, Y. (2015). Storm surges in the Mediterranean Sea: variability and trends under future climatic conditions. Dynamics Atmospheres and Oceans, 71, 56–82.

    Article  Google Scholar 

  34. Makris, C. V., Androulidakis, Y. S., Krestenitis, Y. N., Kombiadou, K. D., Baltikas, V. N. (2015). Numerical modelling of storm surges in the Mediterranean sea under climate change. Proc 36th IAHR World Cong, The Hague, The Netherlands.

  35. Makris, C., et al. (2016). Climate change effects on the marine characteristics of the Aegean and the Ionian seas. Ocean Dynamics, 66(12), 1603–1635.

    Article  Google Scholar 

  36. Galiatsatou, P., Makris, C., Prinos, P., & Kokkinos, D. (2019). Nonstationary joint probability analysis of extreme marine variables to assess design water levels at the shoreline in a changing climate. Natural Hazards, 98, 1051–1089.

    Article  Google Scholar 

  37. Marcos, M., Rohmer, J., Vousdoukas, M. I., Mentaschi, L., Le Cozannet, G., & Amores, A. (2019). Increased extreme coastal water levels due to the combined action of storm surges and wind waves. Geophysical Research Letters, 46(8), 4356–4364.

    Article  Google Scholar 

  38. Kaniewski, D., Marriner, N., Morhange, C., Faivre, S., Otto, T., & Van Campo, E. (2016). Solar pacing of storm surges, coastal flooding and agricultural losses in the Central Mediterranean. Scientific Reports, 6, 25197.

    Article  CAS  Google Scholar 

  39. Paprotny, D., Vousdoukas, M. I., Morales-Nápoles, O., Jonkman, S. N., & Feyen, L. (2020). Pan-European hydrodynamic models and their ability to identify compound floods. Natural Hazards, 101(3), 933–957.

    Article  Google Scholar 

  40. Sudha Rani, N. N. V., Satyanarayana, A. N. V., & Bhaskaran, P. K. (2015). Coastal vulnerability assessment studies over India: a review. Natural Hazards, 77, 405–428.

    Article  Google Scholar 

  41. Ramieri, E., Hartley, A., Barbanti, A., Santos, F., Gomes, A., Hildén, M., et al. (2011). Methods for assessing coastal vulnerability to climate change. ETC CCA Technical Paper, 1(2011), 1–93.

    Google Scholar 

  42. Gornitz, V. (1990). Vulnerability of the East Coast, USA to future sea level rise. Journal of Coastal research, 201–237.

  43. Balica, S. F., Wright, N. G., & van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64, 73–105.

    Article  Google Scholar 

  44. Torresan, S., Critto, A., Rizzi, J., & Marcomini, A. (2012). Assessment of coastal vulnerability to climate change hazards at the regional scale: the case study of the North Adriatic Sea. Natural Hazards and Earth Systems Sciences, 12, 2347–2368.

    Article  Google Scholar 

  45. Satta, A., Snoussi, M., Puddu, M., Flayou, L., & Hout, R. (2016). An index-based method to assess risks of climate-related hazards in coastal zones: the case of Tetouan. Estuar Coast Shelf S, 175, 93–105.

    Article  Google Scholar 

  46. Mani Murali, R., Ankita, M., Amrita, S., & Vethamony, P. (2013). Coastal vulnerability assessment of Puducherry coast, India, using the analytical hierarchical process. Natural Hazards and Earth Systems Sciences, 13, 3291–3311.

    Article  Google Scholar 

  47. Pereira, L. S., Cordely, I., & Iacovides, I. (2009). Coping with water scarcity. Springer, The Netherlands: Addressing the challenges.

    Book  Google Scholar 

  48. Beguería, S., Vicente-Serrano, S. M., Reig, F., & Latorre, B. (2014). Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International journal of climatology, 34(10), 3001–3023.

    Article  Google Scholar 

  49. Zarch, M. A. A., Sivakumar, B., & Sharma, A. (2015). Droughts in a warming climate: a global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of Hydrology, 526, 183–195.

    Article  Google Scholar 

  50. Safavi, H. R., Esfahani, M. K., & Zamani, A. R. (2014). Integrated index for assessment of vulnerability to drought, case study: Zayandehrood River Basin Iran. Water Resources Management, 28, 1671–1688.

    Article  Google Scholar 

  51. Thomas, T., Jaiswal, R. K., & Galkate, R. (2016). Drought indicators-based integrated assessment of drought vulnerability: a case study of Bundelkhand droughts in central India. Natural Hazards, 81, 1627–1652.

    Article  Google Scholar 

  52. Sönmez, F. K., Kömüscü, A. Ü., & Erkan, A. (2005). An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Natural Hazards, 35, 243–264.

    Article  Google Scholar 

  53. Lybbert, T., & Carter, M. (2015). Bundling drought tolerance and index insurance to reduce rural household vulnerability to drought. Sustainable Economic Development, 22, 401–414.

    Article  Google Scholar 

  54. Skoulikaris, C., & Zafirakou, A. (2019). River basin management plans as a tool for sustainable transboundary river basins’ management. Environmental Science and Pollution Research, 26(15), 14835–14848.

    Article  Google Scholar 

  55. Ruti, P. M., et al. (2016). MED-CORDEX initiative for Mediterranean climate studies. Bulletin of the American Meteorological Society, 97(7), 1187–1208.

    Article  Google Scholar 

  56. Pavlidis, V., Katragkou, E., Zanis, P., Karacostas, T. S. (2017). Evaluation of summer temperature and precipitation of EURO-CORDEX regional climate simulations. In: T. Karacostas, A. Bais, P. Nastos (Ed.) Perspectives on atmospheric sciences, Springer Atmospheric Sciences. Springer, Cham.

  57. Hay, L. E., Wilby, R. L., & Leavesley, G. H. (2000). A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. Journal of the American Water Resources Association, 36(2), 387–397.

    Article  Google Scholar 

  58. Böhm, U., Gerstengarbe, F. W., Hauffe, D., Kücken, M., Österle, H., & Werner, P. C. (2003). Dynamic regional climate modeling and sensitivity experiments for the northeast of Brazil (pp. 153–170). Berlin, Heidelberg: Glob Change Reg Impacts. Springer.

    Google Scholar 

  59. Rockel, B., Will, A., & Hense, A. (2008). The regional climate model COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17(4), 347–348.

    Article  Google Scholar 

  60. Radnóti, G., et al. (1995). The spectral limited area model ARPEGE/ALADIN. PWPR Report Series, 7, 111–117.

    Google Scholar 

  61. Madec, G., et al. (2012). NEMO ocean engine. Note du Pole de modélisation de l’Institut Pierre-Simon Laplace, France, 27, 1288–1619. In French.

    Google Scholar 

  62. De Vries, H., Breton, M., de Mulder, T., Krestenitis, Y., Proctor, R., Ruddick, K., et al. (1995). A comparison of 2D storm surge models applied to three shallow European seas. Environ Softw, 10(1), 23–42.

    Article  Google Scholar 

  63. Krestenitis, Y., Androulidakis, Y., Kombiadou, K., Makris, C., Baltikas, V. (2014). Modeling storm surges in the Mediterranean Sea under the A1B climate scenario. Proc 12th Int Conf Meteorol, Climatol Atm Phys (COMECAP), Heraklion (Crete), Greece, pp. 91–95. Part of ISBN: 978–960–524–430–9.

  64. Krestenitis, Y., Makris, C., Androulidakis, Y., Kombiadou, K., Baltikas, V. (2015.) Variability of storm surge extremes in the Greek seas under climate change. Proc 2015 ASLO Aquatic Science Meeting, Granada, Spain.

  65. Krestenitis, Y., et al. (2017). Severe weather events and sea level variability over the Mediterranean Sea: the WaveForUs operational platform. In T. Karacostas, A. Bais, & P. Nastos (Eds.), Perspectives on atmospheric sciences, Springer Atmospheric Sciences (pp. 63–68). Cham: Springer.

    Chapter  Google Scholar 

  66. Bates, P. D., Dawson, R. J., Hall, J. W., Horritt, M. S., Nicholls, R. J., Wicks, J., & Hassan, M. (2005). Simplified two-dimensional numerical modelling of coastal flooding and example applications. Coastal Engineering, 52(9), 793–810.

    Article  Google Scholar 

  67. Hunter, N. M., Horritt, M. S., Bates, P. D., Wilson, M. D., & Werner, M. G. (2005). An adaptive time step solution for raster-based storage cell modelling of floodplain inundation. Advance in Water Resources, 28(9), 975–991.

    Article  Google Scholar 

  68. Bates, P. D., Horritt, M. S., & Fewtrell, T. J. (2010). A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. Journal of Hydrology, 387(1–2), 33–45.

    Article  Google Scholar 

  69. Neal, J., Schumann, G., Fewtrell, T., Budimir, M., Bates, P., & Mason, D. (2011). Evaluating a new LISFLOOD-FP formulation with data from the summer 2007 floods in Tewkesbury UK. Journal of Flood Risk Management, 4(2), 88–95.

    Article  Google Scholar 

  70. Seenath, A., Wilson, M., & Miller, K. (2016). Hydrodynamic versus GIS modelling for coastal flood vulnerability assessment: Which is better for guiding coastal management? Ocean Coast Manage, 120, 99–109.

    Article  Google Scholar 

  71. Brufau, P., García-Navarro, P., & Vázquez-Cendón, M. E. (2004). Zero mass error using unsteady wetting–drying conditions in shallow flows over dry irregular topography. International Journal for Numerical Methods in Fluids, 45(10), 1047–1082.

    Article  Google Scholar 

  72. Castro, M. J., Ferreiro, A. F., García-Rodríguez, J. A., González-Vida, J. M., Macías, J., Parés, C., & Vázquez-Cendón, M. E. (2005). The numerical treatment of wet/dry fronts in shallow flows: application to one-layer and two-layer systems. Mathematical and Computer Modelling, 42(3–4), 419–439.

    Article  Google Scholar 

  73. Ledoux, E., Girard, G., de Marsily, G., Deschenes, J. (1989). Spatially distributed modelling: conceptual approach, coupling surface water and ground water in unsaturated flow hydrologic modelling—theory and practice. Morel-Seytoux HJ (ed.), NATO ASI Series S275, Kluwer Academic: Boston, MA, USA, pp 435–454.

  74. Skoulikaris, C., Anagnostopoulou, C., & Lazoglou, G. (2020). Hydrological modeling response to climate model spatial analysis of a South Eastern Europe International Basin. Climate, 8, 1.

    Article  Google Scholar 

  75. Lazoglou, G., Anagnostopoulou, C., Skoulikaris, C., & Tolika, K. (2019). Bias correction of climate model’s precipitation using the copula method and its application in River Basin Simulation. Water, 11, 600.

    Article  Google Scholar 

  76. Yates, D., Sieber, J., Purkey, D., Huber-Lee, A. (2005a). WEAP21 - a demand-, priority-, and preference-driven water planning model. Part 1: model characteristics. Water International, 30(4): 487–500.

  77. Skoulikaris, Ch., & Ganoulis, J. (2017). Multipurpose hydropower projects economic assessment under climate change conditions. Fresenious Environmental Bull, 26(9), 5599–5607.

    CAS  Google Scholar 

  78. Tsoukalas, I., & Makropoulos, C. (2015). A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem. Water Resources Management, 29(13), 4719–4734.

    Article  Google Scholar 

  79. Yates, D., Purkey, D., Sieber, J., Huber-Lee, A., & Galbraith, H. (2005). Weap21—a demand- priority-, and preference-driven water planning model: part 2: aiding freshwater ecosystem service evaluation. Water International, 30(4), 501–512.

    Article  Google Scholar 

  80. Hargreaves, G. L., Hargreaves, G. H., & Riley, J. P. (1985). Irrigation water requirements for Senegal River Basin. Journal of Irrigation and Drainage Engineering, 111(3), 265–275.

    Article  Google Scholar 

  81. Aschonitis, V., Lekakis, E., Tziachris, P., Doulgeris, C., Papadopoulos, F., Papadopoulos, A., & Papamichail, D. (2019). A ranking system for comparing models’ performance combining multiple statistical criteria and scenarios: the case of reference evapotranspiration models. Environmental Modelling and Software, 114, 98–111.

    Article  Google Scholar 

  82. Ebrahimian, A., Wadzuk, B., & Traver, R. (2019). Evapotranspiration in green stormwater infrastructure systems. Science of the Total Environment, 688, 797–810.

    Article  CAS  Google Scholar 

  83. Valle Junior, L. C., Ventura, T., Gomes, R., Nogueira, J., Lobo, D. A., & F., Vourlitis, G., Rodrigues, T. . (2020). Comparative assessment of modelled and empirical reference evapotranspiration methods for a brazilian savanna. Agricultural Water Management, 232, 106040.

    Article  Google Scholar 

  84. Special Secretariat for Water. (2013). The river basin management plan of Thrace Water District (GR12). Athens, Greece (in Greek): Ministry of Environment and Energy.

    Google Scholar 

  85. Edreira, J. I. R., & Otegui, M. E. (2012). Heat stress in temperate and tropical maize hybrids: differences in crop growth, biomass partitioning and reserves use. Field Crops Research, 130, 87–98.

    Article  Google Scholar 

  86. Suwa, R., et al. (2010). High temperature effects on photosynthetic partitioning and sugar metabolism during ear expansion in maize (Zea mays L.) genotypes. Plant Physiology and Biochemistry, 48, 124–130.

    Article  CAS  Google Scholar 

  87. Rahman, M. A., Chikushi, J., Yoshida, S., & Karim, A. J. M. S. (2009). Growth and yield components of wheat genotypes exposed to high temperature stress under control environment. Bangladesh Journal of Agricultural Research, 34, 361–372.

    Google Scholar 

  88. Djanaguiraman, M., Prasad, P. V. V., & Al-Khatib, K. (2011). Ethylene perception inhibitor 1-MCP decreases oxidative damage of leaves through enhanced antioxidant defense mechanisms in soybean plants grown under high temperature stress. Environmental and Experimental Botany, 71, 215–223.

    Article  CAS  Google Scholar 

  89. Yin, Y., Li, S., Liao, W., Lu, Q., Wen, X., & Lu, C. (2010). Photosystem II photochemistry, photoinhibition, and the xanthophyll cycle in heat-stressed rice leaves. Journal of Plant Physiology, 167, 959–966.

    Article  CAS  Google Scholar 

  90. Helm, P. (1996). Integrated risk management for natural and technological disasters. Tephra, 15(1), 4–13.

    Google Scholar 

  91. Carillo, A., Sannino, G., Artale, V., Ruti, P. M., Calmanti, S., & Dell’Aquila, A. (2012). Steric sea level rise over the Mediterranean Sea: present climate and scenario simulations. Climate Dynamics, 39(9–10), 2167–2184.

    Article  Google Scholar 

  92. Pawlowicz, R., Beardsley, B., & Lentz, S. (2002). Classical tidal harmonic analysis including error estimates in MATLAB using T-TIDE. Computers & Geosciences, 28, 929–937.

    Article  Google Scholar 

  93. Obeysekera, J., Kuebler, L., Ahmed, S., Chang, M.-L., Engel, V., Langevin, C., et al. (2011). Use of hydrologic and hydrodynamic modeling for ecosystem restoration. Critical Reviews in Environmental Science and Technology, 41, 447–488.

    Article  Google Scholar 

  94. Yang, Z., Wang, T., Voisin, N., & Copping, A. (2014). Estuarine response to river flow and sea-level rise under future climate change and human development. Estuarine, Coastal and Shelf Science, 156, 19–30.

    Article  Google Scholar 

  95. Hanington, P., To, Q. T., Van, P. D. T., Doan, N. A. V., & Kiem, A. S. (2017). A hydrological model for interprovincial water resource planning and management: a case study in the Long Xuyen Quadrangle, Mekong Delta Vietnam. Journal of Hydrology, 547, 1–9.

    Article  Google Scholar 

  96. Wester, S. J., Grimson, R., Minotti, P. G., Booij, M. J., & Brugnach, M. (2018). Hydrodynamic modelling of a tidal delta wetland using an enhanced quasi-2D model. Journal of Hydrology, 559, 315–326.

    Article  Google Scholar 

  97. Hoch, J. M., Eilander, D., Ikeuchi, H., Baart, F., & Winsemius, H. C. (2019). Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic-hydrodynamic model coupling framework. Natural and Hazards Earth System Sciences, 19(8), 1723–1735.

    Article  Google Scholar 

  98. Varela-Ortega, C., et al. (2016). How can irrigated agriculture adapt to climate change? Insights from the Guadiana Basin in Spain. Regional Environmental Change, 16, 59–70.

    Article  Google Scholar 

  99. Vermeulen, S. J., Dinesh, D., Howden, S. M., Cramer, L., & Thornton, P. K. (2018). Transformation in practice: a review of empirical cases of transformational adaptation in agriculture under climate change. Frontiers in Sustainable Food Systems, 2, 65.

    Article  Google Scholar 

  100. Iglesias, A., & Garrote, L. (2015). Adaptation strategies for agricultural water management under climate change in Europe. Agricultural Water Management, 155, 113–124.

    Article  Google Scholar 

  101. Vano, J. A., et al. (2010). Climate change impacts on water management and irrigated agriculture in the Yakima River Basin, Washington, USA. Climate Change, 102(1–2), 287–317.

    Article  Google Scholar 

  102. Hallegate, S. (2009). Strategies to adapt to an uncertain climate change. Global Environmental Change, 19, 240–247.

    Article  Google Scholar 

  103. Taoyuan, W., Tianyi, Z., Karianne, B., Solveig, G., & Qinghua, S. (2016). Extreme weather impacts on maize yield: the case of Shanxi Province in China. Sustainability-Basel, 9(1), 41.

    Article  Google Scholar 

  104. Hong, X., Tracy, T., & Evan, G. (2016). Climate change and maize yield in Iowa. PLoS ONE, 11, e0156083.

    Article  CAS  Google Scholar 

  105. Sordo-Ward, A., Granados, A., Iglesias, A., Garrote, L., & Bejarano, M. D. (2019). Adaptation effort and performance of water management strategies to face climate change impacts in six representative basins of Southern Europe. Water, 11, 1078.

    Article  Google Scholar 

  106. Teutschbein, C., & Seibert, J. (2010). Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geography Compass, 4(7), 834–860.

    Article  Google Scholar 

  107. Skoulikaris, Ch., Ganoulis, J., Tolika, K., Anagnostopoulou, Ch., & Velikou, K. (2017). Assessment of agriculture reclamation projects with the use of regional climate models. Water Utility Journal, 16, 7–16.

    Google Scholar 

  108. Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29.

    Article  Google Scholar 

  109. Diaz-Nieto, J., & Wilby, R. L. (2005). A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames United Kingdom. Climatic Change, 69(2–3), 245–268.

    Article  Google Scholar 

  110. Fayaed, S. S., et al. (2019). Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model. Sustainability-Basel, 11(19), 5367.

    Article  Google Scholar 

Download references

Funding

This research is part of the MEDAQCLIM project: Integrated Quantitative Assessment of Climate Change Impacts on Mediterranean Coastal Water Resources and Socioeconomic Vulnerability Mapping, which is financed by the National Action Plan: “European R&D Cooperation—Grant Act of Greek partners successfully participating in Joint Calls for Proposals of the European Networks ERA-NETS” and the “Competitiveness, Entrepreneurship & Innovation” Program.

Author information

Authors and Affiliations

  1. UNESCO, Chair INWEB, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Charalampos Skoulikaris

  2. Division of Hydraulics and Environmental Engineering, School of Civil Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Christos Makris, Margarita Katirtzidou, Vasilios Baltikas & Yannis Krestenitis

Authors
  1. Charalampos Skoulikaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christos Makris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Margarita Katirtzidou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vasilios Baltikas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yannis Krestenitis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Charalampos Skoulikaris, Christos Makris, Margarita Katirtzidou, Vasilios Baltikas, Yannis Krestenitis; methodology: Charalampos Skoulikaris, Christos Makris, Margarita Katirtzidou; formal analysis and investigation: Charalampos Skoulikaris, Christos Makris, Margarita Katirtzidou, Vasilios Baltikas; writing—original draft preparation: Charalampos Skoulikaris, Christos Makris, Margarita Katirtzidou; writing—review and editing: Charalampos Skoulikaris, Christos Makris; funding acquisition: Yannis Krestenitis; resources: Charalampos Skoulikaris, Christos Makris, Margarita Katirtzidou, Vasilios Baltikas; supervision: Charalampos Skoulikaris, Yannis Krestenitis.

Corresponding author

Correspondence to Charalampos Skoulikaris.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Skoulikaris, C., Makris, C., Katirtzidou, M. et al. Assessing the Vulnerability of a Deltaic Environment due to Climate Change Impact on Surface and Coastal Waters: The Case of Nestos River (Greece). Environ Model Assess 26, 459–486 (2021). https://doi.org/10.1007/s10666-020-09746-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-020-09746-2

Keywords

Search

Navigation