Abstract
This study presents a robust approach to assess climate change impact variability on future extreme events (e.g., rainfall depth and river discharge) over Dehbar catchment in Iran. Climate change impact is assessed using five general circulation models (GCMs) including EC-EARTH, GFDL-CM3, HadGEM2-ES, MIROC5, and MPI-ESM-MR with several emission scenarios (e.g., RCP26, RCP45, and RCP85). Daily discharge data is simulated based on the distributed rainfall-runoff model called Soil and Water Assessment Tool (SWAT), while calibration and validation phases are performed using SWAT-CUP. Future annual extreme events (i.e., rainfall depth and river discharge) are computed by means of frequency analysis. Results show that future annual maximum values are increased significantly, where the most increase occurs in the future annual river discharge and rainfall depth according to the EC-EARTH-RCP85 as 142% and 81% with MPI-ESM-MR-RCP85 model. The highest future extreme river discharge and rainfall depth values through different return periods (50–1000 year) are obtained from EC-EARTH-RCP85 as 6.8~8.08 cms and 57.41~105.76 mm based on EC-EARTH-RCP45 model. Uncertainty analysis results indicate that climate models/scenarios have significant effect on the future extreme events variability, while the same for extreme river discharge is the least sensitive to different return periods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbaspour KC, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, Kløve B (2015) A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol 524:733–752. https://doi.org/10.1016/j.jhydrol.2015.03.027
Al Sudani ZA, Salih SQ, Yaseen ZM (2019) Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 73:1–12
American Society of Photogrammetry, P.S., American Society for Photogrammetry and Remote Sensing, KJ (2000) Photogrammetric engineering and remote sensing., PE&RS, Photogrammetric Engineering & Remote Sensing. American Society of Photogrammetry.
Amiri MJ, Eslamian SS (2010) Investigation of climate change in Iran. J Environ Sci Technol 3:208–216. https://doi.org/10.3923/jest.2010.208.216
Blanchette M, Rousseau AN, Foulon É, Savary S, Poulin M (2019) What would have been the impacts of wetlands on low flow support and high flow attenuation under steady state land cover conditions? J Environ Manag 234:448–457. https://doi.org/10.1016/J.JENVMAN.2018.12.095
Chhogyel N, Kumar L (2018) Climate change and potential impacts on agriculture in Bhutan: a discussion of pertinent issues. Agric Food Secur 7:79. https://doi.org/10.1186/s40066-018-0229-6
Dai A, Dai A (2006) Precipitation characteristics in eighteen coupled climate models. J Clim 19:4605–4630. https://doi.org/10.1175/JCLI3884.1
Dakhlalla AO, Parajuli PB (2019) Assessing model parameters sensitivity and uncertainty of streamflow, sediment, and nutrient transport using SWAT. Inf Process Agric 6:61–72. https://doi.org/10.1016/J.INPA.2018.08.007
Dile YT, Tekleab S, Ayana EK, Gebrehiwot SG, Worqlul AW, Bayabil HK, Yimam YT, Tilahun SA, Daggupati P, Karlberg L, Srinivasan R (2018) Advances in water resources research in the Upper Blue Nile basin and the way forward: a review. J Hydrol 560:407–423. https://doi.org/10.1016/J.JHYDROL.2018.03.042
Duan JG, Bai Y, Dominguez F, Rivera E, Meixner T (2017) Framework for incorporating climate change on flood magnitude and frequency analysis in the upper Santa Cruz River. J Hydrol 549:194–207. https://doi.org/10.1016/j.jhydrol.2017.03.042
Duan Z, Tuo Y, Liu J, Gao H, Song X, Zhang Z, Yang L, Mekonnen DF (2019) Hydrological evaluation of open-access precipitation and air temperature datasets using SWAT in a poorly gauged basin in Ethiopia. J Hydrol 569:612–626. https://doi.org/10.1016/J.JHYDROL.2018.12.026
Fiseha BM, Melesse AM, Romano E, Volpi E, Fiori A (2012) Statistical Downscaling of precipitation and temperature for the Upper Tiber Basin in Central Italy. Int J Water Sci 1:1. https://doi.org/10.5772/52890
Gao C, He Z, Pan S, Xuan W, Xu Y-P (2018) Effects of climate change on peak runoff and flood levels in Qu River Basin, East China. J Hydro Environ Res. https://doi.org/10.1016/J.JHER.2018.02.005
Hashmi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Env Res Risk A 25:475–484. https://doi.org/10.1007/s00477-010-0416-x
IPCC (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Geneva
Jha PK, Athanasiadis P, Gualdi S, Trabucco A, Mereu V, Shelia V, Hoogenboom G (2019) Using daily data from seasonal forecasts in dynamic crop models for yield prediction: a case study for rice in Nepal’s Terai. Agric For Meteorol 265:349–358. https://doi.org/10.1016/J.AGRFORMET.2018.11.029
Kephe PN, Petja BM, Kabanda TA (2016) Spatial and inter-seasonal behaviour of rainfall in the Soutpansberg region of South Africa as attributed to the changing climate. Theor Appl Climatol 126:233–245
Khazaei MR, Zahabiyoun B, Saghafian B (2012) Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. Int J Climatol 32:1997–2006. https://doi.org/10.1002/joc.2416
Kourgialas NN, Koubouris GC, Dokou Z (2019) Optimal irrigation planning for addressing current or future water scarcity in Mediterranean tree crops. Sci Total Environ 654:616–632. https://doi.org/10.1016/J.SCITOTENV.2018.11.118
Lenderink G, Mok HY, Lee TC, van Oldenborgh GJ (2011) Scaling and trends of hourly precipitation extremes in two different climate zones – Hong Kong and the Netherlands. Hydrol Earth Syst Sci 15:3033–3041. https://doi.org/10.5194/hess-15-3033-2011
Li L, Yao N, Li Y, Liu DL, Wang B, Ayantobo OO (2019) Future projections of extreme temperature events in different sub-regions of China. Atmos Res 217:150–164. https://doi.org/10.1016/J.ATMOSRES.2018.10.019
Ma T, Duan Z, Li R, Song X (2019) Enhancing SWAT with remotely sensed LAI for improved modelling of ecohydrological process in subtropics. J Hydrol 570:802–815. https://doi.org/10.1016/J.JHYDROL.2019.01.024
Maghsood FF, Moradi H, Massah Bavani AR, Panahi M, Berndtsson R, Hashemi H (2019) Climate change impact on flood frequency and source area in Northern Iran under CMIP5 scenarios. Water 11:273. https://doi.org/10.3390/w11020273
Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Chen Y, Connors S, Gomis M (2018) IPCC, 2018: Summary for policymakers. Glob Warm 1
Memarian H, Bilondi MP, Komeh Z (2019) Parameter optimization of KINEROS2 using particle swarm optimization algorithm within r environment for rainfall–runoff simulation. Spat Model GIS R Earth Environ Sci:117–146. https://doi.org/10.1016/B978-0-12-815226-3.00005-3
Nabaei S, Sharafati A, Yaseen ZM, Shahid S (2019) Copula based assessment of meteorological drought characteristics: regional investigation of Iran. Agric For Meteorol 276:107611
Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2005) Soil and Water Assessment Tool user’s manual version 2005. Diffus Pollut Conf Dublin 494
Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2011) Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute
Nigussie TA, Altunkaynak A (2019) Impacts of climate change on the trends of extreme rainfall indices and values of maximum precipitation at Olimpiyat Station, Istanbul, Turkey. Theor Appl Climatol 135:1501–1515
Nilawar AP, Waikar ML (2019) Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: a case study in Purna river basin, India. Sci Total Environ 650:2685–2696. https://doi.org/10.1016/J.SCITOTENV.2018.09.334
Park S, Nielsen A, Bailey RT, Trolle D, Bieger K (2019) A QGIS-based graphical user interface for application and evaluation of SWAT-MODFLOW models. Environ Model Softw 111:493–497. https://doi.org/10.1016/J.ENVSOFT.2018.10.017
Pauling A, Luterbacher J, Casty C, Wanner H (2006) Five hundred years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation. Clim Dyn 26:387–405. https://doi.org/10.1007/s00382-005-0090-8
Qi J, Zhang X, Wang Q (2019) Improving hydrological simulation in the Upper Mississippi River Basin through enhanced freeze-thaw cycle representation. J Hydrol 571:605–618. https://doi.org/10.1016/J.JHYDROL.2019.02.020
Qutbudin I, Shiru MS, Sharafati A, Ahmed K, Al-Ansari N, Yaseen ZM, Shahid S, Wang X (2019) Seasonal drought pattern changes due to climate variability: case study in Afghanistan. Water 11:1096
Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41. https://doi.org/10.1016/0304-3800(91)90053-4
Rahimi J, Ebrahimpour M, Khalili A (2013) Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theor Appl Climatol 112:409–418
Ren F-M, Trewin B, Brunet M, Dushmanta P, Walter A, Baddour O, Korber M (2018) A research progress review on regional extreme events. Adv Clim Chang Res 9:161–169. https://doi.org/10.1016/J.ACCRE.2018.08.001
Ren Y, Song L, Xiao Y, Du L (2019) Underestimated interannual variability of East Asian summer rainfall under climate change. Theor Appl Climatol 135:911–920
Rivas-Tabares D, Tarquis AM, Willaarts B, De Miguel Á (2019) An accurate evaluation of water availability in sub-arid Mediterranean watersheds through SWAT: Cega-Eresma-Adaja. Agric Water Manag 212:211–225. https://doi.org/10.1016/J.AGWAT.2018.09.012
Root TL, Schneider SH, Kurihara Y, Changnon SA, Karl TR, Mearns LO (1995) Ecology and climate: research strategies and implications. Science 269:334–341. https://doi.org/10.1126/science.269.5222.334
Rosenzweig C, Iglesias A, Yang XB, Epstein PR, Chivian E (2001) Climate change and extreme weather events; implications for food production, plant diseases, and pests. Glob Chang Hum Health 2:90–104. https://doi.org/10.1023/A:1015086831467
Roxburgh N, Guan D, Shin KJ, Rand W, Managi S, Lovelace R, Meng J (2019) Characterising climate change discourse on social media during extreme weather events. Glob Environ Chang 54:50–60. https://doi.org/10.1016/J.GLOENVCHA.2018.11.004
Rulfová Z, Beranová R, Kyselý J (2017) Climate change scenarios of convective and large-scale precipitation in the Czech Republic based on EURO-CORDEX data. Int J Climatol 37:2451–2465. https://doi.org/10.1002/joc.4857
Saha TK, Pal S (2019) Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches. Ecol Indic 98:251–265. https://doi.org/10.1016/J.ECOLIND.2018.11.009
Sánchez E, Gallardo C, Gaertner MA, Arribas A, Castro M (2004) Future climate extreme events in the Mediterranean simulated by a regional climate model: a first approach. Glob Planet Chang 44:163–180. https://doi.org/10.1016/J.GLOPLACHA.2004.06.010
Sayari N, Bannayan M, Alizadeh A, Farid A (2013) Using drought indices to assess climate change impacts on drought conditions in the northeast of Iran (case study: Kashafrood basin). Meteorol Appl 20:115–127
Sayl KN, Muhammad NS, Yaseen ZM, El-shafie A (2016) Estimation the physical variables of rainwater harvesting system using integrated GIS-based remote sensing approach. Water Resour. Manag. 30:3299–3313. https://doi.org/10.1007/s11269-016-1350-6
Schoener G, Stone MC (2019) Impact of antecedent soil moisture on runoff from a semiarid catchment. J Hydrol 569:627–636. https://doi.org/10.1016/J.JHYDROL.2018.12.025
Semenov MA, Barrow EM (2002) A stochastic weather generator for use in climate impact studies. User Manual, Hertfordshire, UK 0–27
Semenov M, Brooks R (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Clim Res 11:137–148. https://doi.org/10.3354/cr011137
Semenov M, Brooks R, Barrow E, Richardson C (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107. https://doi.org/10.3354/cr010095
Senapati N, Brown HE, Semenov MA (2019) Raising genetic yield potential in high productive countries: designing wheat ideotypes under climate change. Agric For Meteorol 271:33–45. https://doi.org/10.1016/J.AGRFORMET.2019.02.025
Senent-Aparicio J, Jimeno-Sáez P, Bueno-Crespo A, Pérez-Sánchez J, Pulido-Velázquez D (2019) Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction. Biosyst Eng 177:67–77. https://doi.org/10.1016/J.BIOSYSTEMSENG.2018.04.022
Sha J, Li X, Wang Z-L (2019) Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios. Theor Appl Climatol 1–13:3027–3039. https://doi.org/10.1007/s00704-019-02781-4
Shagega FP, Munishi SE, Kongo VM (2018) Prediction of future climate in Ngerengere river catchment, Tanzania. Phys Chem Earth Parts A/B/C. https://doi.org/10.1016/J.PCE.2018.12.002
Sharafati A, Azamathulla HM (2018) Assessment of dam overtopping reliability using SUFI based overtopping threshold curve. Water Resour Manag 32:2369–2383
Sharafati A, Zahabiyoun B (2013) Stochastic generation of storm pattern. Life Sci J 10
Sharafati A, Zahabiyoun B (2014) Rainfall threshold curves extraction by considering rainfall-runoff model uncertainty. Arab J Sci Eng 39:6835–6849. https://doi.org/10.1007/s13369-014-1246-9
Sharafati A, Yasa R, Azamathulla HM (2018) Assessment of stochastic approaches in prediction of wave-induced pipeline scour depth. J Pipeline Syst Eng Pract:9. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000347
Sharafati A, Khosravi K, Khosravinia P, Ahmed K, Salman SA, Mundher Z, Shamsuddin Y (2019) The potential of novel data mining models for global solar radiation prediction. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-019-02344-0
Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K, Brokopf R, Fast I, Kinne S, Kornblueh L, Lohmann U, Pincus R, Reichler T, Roeckner E (2013) Atmospheric component of the MPI-M Earth System Model: ECHAM6. J Adv Model Earth Syst 5:146–172. https://doi.org/10.1002/jame.20015
Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640. https://doi.org/10.1016/J.JHYDROL.2006.04.030
Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang H, Abbaspour KC (2019) The future of extreme climate in Iran. Sci Rep 9:1464. https://doi.org/10.1038/s41598-018-38071-8
Verma RR, Srivastava TK, Singh P (2019) Climate change impacts on rainfall and temperature in sugarcane growing Upper Gangetic Plains of India. Theor Appl Climatol 135:279–292
Wang Q, Liu R, Men C, Guo L, Miao Y (2019) Temporal-spatial analysis of water environmental capacity based on the couple of SWAT model and differential evolution algorithm. J Hydrol 569:155–166. https://doi.org/10.1016/J.JHYDROL.2018.12.003
Wu D, Cui Y, Wang Y, Chen M, Luo Y, Zhang L (2019a) Reuse of return flows and its scale effect in irrigation systems based on modified SWAT model. Agric Water Manag 213:280–288. https://doi.org/10.1016/J.AGWAT.2018.10.025
Wu D, Cui Y, Xie X, Luo Y (2019b) Improvement and testing of SWAT for multi-source irrigation systems with paddy rice. J Hydrol 568:1031–1041. https://doi.org/10.1016/J.JHYDROL.2018.11.057
Xiao C, Wu P, Zhang L, Clark RT (2018) Increasing flash floods in a drying climate over Southwest China. Adv Atmos Sci 35:1094–1099. https://doi.org/10.1007/s00376-018-7275-7
Yaseen Z, Ehteram M, Sharafati A, Shahid S, Al-Ansari N, El-Shafie A (2018a) The integration of nature-inspired algorithms with least square support vector regression models: application to modeling river dissolved oxygen concentration. Water 10:1124
Yaseen ZM, Awadh SM, Sharafati A, Shahid S (2018b) Complementary data-intelligence model for river flow simulation. J Hydrol 567:180–190. https://doi.org/10.1016/j.jhydrol.2018.10.020
Yaseen ZM, Sulaiman SO, Deo RC, Chau K-W (2018c) An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069
Yuan X-C, Wei Y-M, Wang B, Mi Z (2017) Risk management of extreme events under climate change. J Clean Prod 166:1169–1174. https://doi.org/10.1016/J.JCLEPRO.2017.07.209
Yuan Z, Xu J, Wang Y (2018) Projection of future extreme precipitation and flood changes of the Jinsha River Basin in China based on CMIP5 climate models. Int J Environ Res Public Health 15. https://doi.org/10.3390/IJERPH15112491
Zahabiyoun B, Goodarzi MR, Bavani ARM, Azamathulla HM (2013) Assessment of climate change impact on the gharesou river basin using SWAT hydrological model. CLEAN Soil Air Water 41:601–609. https://doi.org/10.1002/clen.201100652
Zapata-Sierra AJ, Manzano-Agugliaro F (2019) Proposed methodology for evaluation of small hydropower sustainability in a Mediterranean climate. J Clean Prod 214:717–729. https://doi.org/10.1016/J.JCLEPRO.2018.12.327
Acknowledgments
The authors would like to reveal their gratitude and appreciation to the data providers, Iranian Meteorological Organization and Iran Water Resources Management Company.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sharafati, A., Pezeshki, E. A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theor Appl Climatol 139, 389–402 (2020). https://doi.org/10.1007/s00704-019-02979-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-019-02979-6