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Journal of Arid Land  2021, Vol. 13 Issue (9): 891-904    DOI: 10.1007/s40333-021-0091-4     CSTR: 32276.14.s40333-021-0091-4
Research article     
Climate change impacts on the streamflow of Zarrineh River, Iran
Farhad YAZDANDOOST*(), Sogol MORADIAN
Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran
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Abstract  

Zarrineh River is located in the northwest of Iran, providing more than 40% of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth. Lake Urmia is a highly endangered ecosystem on the brink of desiccation. This paper studied the impacts of climate change on the streamflow of Zarrineh River. The streamflow was simulated and projected for the period 1992-2050 through seven CMIP5 (coupled model intercomparison project phase 5) data series (namely, BCC-CSM1-1, BNU-ESM, CSIRO-Mk3-6-0, GFDL-ESM2G, IPSL-CM5A-LR, MIROC-ESM and MIROC-ESM-CHEM) under RCP2.6 (RCP, representative concentration pathways) and RCP8.5. The model data series were statistically downscaled and bias corrected using an artificial neural network (ANN) technique and a Gamma based quantile mapping bias correction method. The best model (CSIRO-Mk3-6-0) was chosen by the TOPSIS (technique for order of preference by similarity to ideal solution) method from seven CMIP5 models based on statistical indices. For simulation of streamflow, a rainfall-runoff model, the hydrologiska byrans vattenavdelning (HBV-Light) model, was utilized. Results on hydro-climatological changes in Zarrineh River basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5, respectively. In the case of temperature, the numbers change from 12.33°C and 12.37°C in 2015 to 14.28°C and 14.32°C in 2050. Corresponding to these climate scenarios, this study projected a decrease of the annual streamflow of Zarrineh River by half from 2015 to 2050 as the results of climatic changes will lead to a decrease in the annual streamflow of Zarrineh River from 59.49 m3/s in 2015 to 22.61 and 23.19 m3/s in 2050. The finding is of important meaning for water resources planning purposes, management programs and strategies of the Lake's endangered ecosystem.



Key wordsclimate change      water resources management      climate model intercomparison project phase5 (CMIP5)      artificial neural network (ANN)      bias correction      hydrologiska byrans vattenavdelning (HBV-Light)      Zarrineh River     
Received: 19 January 2021      Published: 10 September 2021
Corresponding Authors: *Farhad YAZDANDOOST (E-mail: yazdandoost@kntu.ac.ir)
Cite this article:

Farhad YAZDANDOOST, Sogol MORADIAN. Climate change impacts on the streamflow of Zarrineh River, Iran. Journal of Arid Land, 2021, 13(9): 891-904.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0091-4     OR     http://jal.xjegi.com/Y2021/V13/I9/891

Fig. 1 Location of the Zarrineh River basin in Iran (a) and the distributions of the meteorological and stream gauging stations in the study area (b)
Fig. 2 Urmia Lake shrinkage over 1993-2018 acquired by Land Remote-Sensing Satellite using Normalized Difference Water Index
Station Location Altitude above sea level (m) Period of record
Name Type Longitude Latitude
Maragheh Meteorological 46°16′E 37°24′N 1477.7 1987-2019
Saghez Meteorological 46°16′E 36°15′N 1522.8 1987-2019
Takab Meteorological 47°06′E 36°24′N 1817.2 1992-2019
Sarighmish Stream gauging 46°29′E 36°29′N 1372.0 1955-2019
Gezkerpi Stream gauging 46°33′E 36°37′N 1350.0 1956-2019
Miandoab Stream gauging 46°07′E 36°57′N 1290.0 1964-2019
Nezamabad Stream gauging 46°03′E 37°03′N 1290.0 1993-2019
Table 1 Location of the meteorological and stream gauging stations
Model Complete name Resolution
Atmospheric grid Ocean grid
Longitude Latitude Longitude Latitude
BCC-CSM1-1 Beijing Climate Center Climate System Model 2.8125° 2.7906° 1.0000° 0.3333°, 1.0000°
BNU-ESM Beijing Normal University Earth System Model 2.8125° 2.7906° 1.0000° 0.3344°, 1.0000°
CSIRO-Mk3-6-0 Commonwealth Scientific and Industrial Research Organization 1.8750° 1.8653° 1.8750° 0.9327°, 0.9457°
GFDL-ESM2G Geophysical Fluid Dynamics Laboratory 2.0000° 2.0225° 1.0000° 0.3750°, 0.5000°
IPSL-CM5A-LR IPSL Earth System Model for the 5th IPCC report- Low resolution 3.7500° 1.8947° lon (i, j) lat (i, j)
MIROC-ESM Model for Interdisciplinary Research on Climate- earth system model 2.8125° 2.7906° 1.4063° 0.5582°, 1.7111°
MIROC-ESM-CHEM An atmospheric chemistry coupled version of MIROC-ESM 2.8125° 2.7906° 1.4063° 0.5582°, 1.7111°
Table 2 GCMs (global climate models) used in this study
Fig. 3 Flowchart of the study. CMIP5, climate model intercomparison project phase 5; GCMs, global climate models; RCPs, representative concentration pathways; TOPSIS, technique for order of preference by similarity to ideal solution.
Model Temperature Precipitation
MAE (°C) R2 Mean (°C) Observed mean (°C) MAE (mm) R2 Mean (mm) Observed mean (mm)
BCC-CSM1-1 6.95 0.68 15.12 13.26 1.21 0.02 0.60 0.80
BNU-ESM 6.68 0.89 18.92 1.05 0.03 0.43
CSIRO-Mk3-6-0 7.44 0.66 15.03 1.25 0.04 0.64
GFDL-ESM2G 7.80 0.71 18.53 1.36 0.01 0.74
IPSL-CM5A-LR 6.35 0.77 9.22 1.30 0.02 0.73
MIROC-ESM 6.57 0.90 19.38 1.25 0.01 0.64
MIROC-ESM-CHEM 6.40 0.90 19.20 1.25 0.05 0.68
Table 3 Statistical evaluation of the raw GCMs' simulations for daily temperature and precipitation data from 1992 to 2005 in cell number 1 as a sample
Model Temperature Precipitation
MAE (°C) R2 Mean (°C) Observed mean (°C) MAE (mm) R2 Mean (mm) Observed mean (mm)
BCC-CSM1-1 2.86 0.72 15.12 13.26 1.02 0.30 0.74 0.80
BNU-ESM 2.77 0.93 18.92 0.87 0.30 0.74
CSIRO-Mk3-6-0 2.36 0.69 15.03 1.09 0.32 0.77
GFDL-ESM2G 2.85 0.76 18.53 1.21 0.30 0.77
IPSL-CM5A-LR 2.41 0.81 9.22 1.11 0.30 0.74
MIROC-ESM 2.53 0.95 19.38 1.05 0.30 0.74
MIROC-ESM-CHEM 2.54 0.94 19.20 1.06 0.30 0.74
Table 4 Statistical evaluation of the bias-corrected ANN (artificial neural network) simulations for daily temperature and precipitation data from 1992 to 2005 in cell number 1 as a sample
Model Precipitation Temperature TOPSIS
MAE (mm) R2 Mean error (mm) MAE (°C) R2 Mean error (°C) Cli* Rank
BCC-CSM1-1 1.02 0.30 0.06 2.86 0.72 1.86 0.787 2
BNU-ESM 0.87 0.30 0.06 2.87 0.93 5.66 0.106 5
CSIRO-Mk3-6-0 1.09 0.32 0.03 2.36 0.69 1.77 0.971 1
GFDL-ESM2G 1.21 0.30 0.03 2.85 0.76 5.27 0.289 4
IPSL-CM5A-LR 1.11 0.30 0.06 2.41 0.81 4.04 0.450 3
MIROC-ESM 1.05 0.30 0.06 2.53 0.95 6.12 0.028 7
MIROC-ESM-CHEM 1.06 0.30 0.06 2.54 0.94 5.94 0.048 6
Table 5 The result of TOPSIS (technique for order of preference by similarity to ideal solution) method for choosing the best CMIP5 (coupled model intercomparison project phase 5) model during 1992-2005
Parameter Time
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Pobs (mm) 1.22 1.69 1.72 1.07 1.18 1.16 1.07 0.71 0.85 0.81 1.37 1.26 1.36 1.07
Pcor (mm) 1.30 1.34 1.29 1.19 1.13 1.08 1.01 0.96 0.96 1.01 1.09 1.13 1.12 1.00
Tobs (°C) 8.52 10.02 11.16 11.18 11.55 10.27 11.54 12.20 11.49 12.31 11.45 11.50 11.54 11.61
Tcor (°C) 9.76 11.10 12.12 12.56 12.42 12.20 12.46 12.86 12.95 12.96 12.87 12.74 12.58 12.45
Table 6 Observed and corrected mean annual precipitations and annual mean temperatures from 1992 to 2005 by bias-corrected ANN data, derived from CSIRO-Mk3-6-0 in Zarrineh River basin
Fig. 4 The observed (points) and corrected (box plots) mean precipitation (a) and mean temperature (b) from 1992 to 2005 by bias-corrected ANN data for daily data, derived from CSIRO-Mk3-6-0
Fig. 5 Assessment of bias corrected ANN simulations of monthly mean precipitation (a) and temperature (b) derived from CSIRO-Mk3-6-0 in the validation period (2006-2015)
Fig. 6 Annual mean stream?ow of the Zarrineh River in the HBV-Light (hydrologiska byrans vattenavdelning) models simulated by bias-corrected ANN data, derived from CSIRO-Mk3-6-0, operated under RCP2.6 and RCP8.5 from 2015 to 2050
R2 Sum of monthly mean streamflow (m3/s) Monthly mean streamflow (m3/s)
RCP2.6 RCP8.5 Observed RCP2.6 RCP8.5 Observed RCP2.6 RCP8.5
0.63 0.63 713.84 703.58 714.34 59.49 58.63 59.53
Table 7 Performance of the HBV-Light (hydrologiska byrans vattenavdelning) models simulated by bias-corrected ANN data for daily bias-corrected precipitation and temperature, derived from CSIRO-Mk3-6-0, operated under RCP2.6 and RCP8.5 in 2015
Month Monthly mean streamflow (m3/s)
Observation Simulation
RCP2.6 RCP8.5
Jan 42.74 32.40 34.70
Feb 211.78 38.10 37.75
Mar 202.61 219.21 219.81
Apr 89.31 75.21 75.01
May 45.76 54.20 53.72
Jun 40.69 45.50 45.42
Jul 27.30 42.89 42.89
Aug 16.68 40.82 40.83
Sep 9.89 39.18 39.36
Oct 7.83 37.22 38.74
Nov 8.19 33.77 36.23
Dec 11.06 45.08 49.88
Table 8 Monthly mean stream?ow of the HBV-Light models simulated by bias-corrected ANN data for daily bias-corrected precipitation and temperature, derived from CSIRO-Mk3-6-0, operated under RCP2.6 and RCP8.5 in 2015
[1]   Abbaspour M, Nazaridoust A. 2007. Determination of environmental water requirements of Lake Urmia, Iran: an ecological approach. International Journal of Environmental Studies, 64(2):161-169.
doi: 10.1080/00207230701238416
[2]   Ahmadaali J, Barani G, Qaderi K, et al. 2018. Analysis of the effects of water management strategies and climate change on the environmental and agricultural sustainability of Urmia Lake basin, Iran. Water, 10(2):1-21.
doi: 10.3390/w10020001
[3]   Ahmadi A, Abbaspour M, Arjmandi R, et al. 2016. Resilient approach toward urban development in lake catchments, case of Lake Urmia. Scientia Iranica, 23(4):1627-1632
doi: 10.24200/sci.2016.2233
[4]   Ahmadzadeh K T, Pejman A, Mahin A E, et al. 2011. Evaluation of salt effects on some thermodynamic properties of Urmia Lake water. International Journal of Environmental Research, 5(2):343-348.
[5]   Alborzi A, Mirchi A, Moftakhari H, et al. 2018. Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts. Environmental Research letters, 13(8):084010, doi: 10.1088/1748-9326/aad246.
doi: 10.1088/1748-9326/aad246
[6]   Anderson J W P, Storniolo R E, Rice J S. 2011. Bank thermal storage as a sink of temperature surges in urbanized streams. Journal of Hydrology, 409(1-2):525-537.
doi: 10.1016/j.jhydrol.2011.08.059
[7]   Bozkurt D, Rojas M, Boisier J P, et al. 2018. Projected hydroclimate changes over Andean basins in Central Chile from downsca led CMIP5 models under the low and high emission scenarios. Climatic Change, 150:131-147.
doi: 10.1007/s10584-018-2246-7
[8]   Cai L, Alexeev V A, Walsh J E, et al. 2018. Patterns, impacts, and future projections of summer variability in the Arctic from CMIP5 models. Journal of Climate, 31(24):9815-9833.
doi: 10.1175/JCLI-D-18-0119.1
[9]   Campozano L, Tenelanda D, Sanchez E, et al. 2015. Comparison of statistical downscaling methods for monthly total precipitation: Case study for the paute river basin in southern Ecuador. Advances in Meteorology, Special Issue: 6526341, doi: 10.1155/2016/6526341.
doi: 10.1155/2016/6526341
[10]   Chadwick R, Coppola E, Giorgi F. 2011. An artificial neural network technique for downscaling GCM outputs to RCM spatial scale. Nonlinear Processes in Geophysics, 18:1013-1028.
doi: 10.5194/npg-18-1013-2011
[11]   Coulibaly P, Dibike Y B, Anctil F. 2005. Downscaling precipitation and temperature with temporal neural networks. Journal of Hydrometeorology, 6(4):483-496.
doi: 10.1175/JHM409.1
[12]   Cramer H. 1999. Mathematical Methods of Statistics (9th ed.). Princeton: Princeton University Press, 208-232, 310- 316, 557-559.
[13]   Delju A, Ceylan A, Piguet E, et al. 2013. Observed climate variability and change in Urmia Lake Basin, Iran. Theoretical and Applied Climatology, 111:285-296.
doi: 10.1007/s00704-012-0651-9
[14]   Do Hoai N, Udo K, Mano A. 2011. Downscaling global weather forecast outputs using ANN for flood prediction. Journal of Applied Mathematics, Special Issue: 246286, doi: 10.1155/2011/246286.
doi: 10.1155/2011/246286
[15]   Fang G, Yang J H, Chen Y N, et al. 2015. Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19:2547-2559.
doi: 10.5194/hess-19-2547-2015
[16]   Fowler H J, Ekström M, Blenkinsop S, et al. 2007. Estimating change in extreme European precipitation using a multimodel ensemble. Journal of Geophysical Research, 112(D18):104, doi: 10.1029/2007JD008619.
doi: 10.1029/2007JD008619
[17]   García-Cascales M S, Lamata T M. 2012. On rank reversal and TOPSIS method. Journal of Mathematical and Computer Modelling, 56(5-6):123-132.
doi: 10.1016/j.mcm.2011.12.022
[18]   Ghale Y, Altunkaynak A, Unal A. 2018. Investigation anthropogenic impacts and climate factors on drying up of Urmia Lake using water budget and drought analysis. Water Resources Management, 32:325-337.
doi: 10.1007/s11269-017-1812-5
[19]   Gholampour A, Nabizadeh R, Hassanvand M S, et al. 2015. Characterization of saline dust emission resulted from Urmia Lake drying. Journal of Environmental Health Science and Engineering, 13:82, doi: 10.1186/s40201-015-0238-3.
doi: 10.1186/s40201-015-0238-3 pmid: 26617986
[20]   Gondim R, Silveira C, Souza F, et al. 2018. Climate change impacts on water demand and availability using CMIP5 models in the Jaguaribe basin, semi-arid Brazil. Environmental Earth Sciences, 77:550, doi: 10.1007/s12665-018-7723-9.
doi: 10.1007/s12665-018-7723-9
[21]   Hung N Q, Babel M S, Weesakul S, et al. 2009. An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, 13(8):1413-1425.
doi: 10.5194/hess-13-1413-2009
[22]   IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013:The physical science basis, contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press, 75, 109,113.
[23]   Jena P, Azad S, Nair Rajeevan M. 2015. Statistical selection of the optimum models in the cmip5 dataset for climate change projections of indian monsoon rainfall. Climate, 3(4):858-875.
doi: 10.3390/cli3040858
[24]   Khalili M, Nguyen V T, Gachon P. 2013. A statistical approach to multi-site multivariate downscaling of daily extreme temperature series. International Journal of Climatology, 33(1):15-32.
doi: 10.1002/joc.3402
[25]   Khan N, Shahid S, Ahmed K, et al. 2018. Performance assessment of general circulation model in simulating daily precipitation and temperature using multiple gridded datasets. Water, 10(12):1793, doi: 10.3390/w10121793.
doi: 10.3390/w10121793
[26]   Lippmann R P. 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4(2):4-22.
[27]   Liu T, Willems P, Pan X L, et al. 2011. Climate change impact on water resource extremes in a headwater region of the Tarim basin in China, Hydrology and Earth System Sciences, 15:3511-3527.
doi: 10.5194/hess-15-3511-2011
[28]   Liu Z, Xu Z, Huang J, et al. 2010. Impacts of climate change on hydrological processes in the headwater catchment of the Tarim River basin, China. Hydrological Processes, 24(2):196-208.
[29]   Maraun D, Wetterhall F, Ireson A M, et al. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48(3): RG3003, doi: 10.1029/2009RG000314.
doi: 10.1029/2009RG000314
[30]   McSweeney C F, Jones R G, Lee R W, et al. 2015. Selecting CMIP5 GCMs for downscaling over multiple regions. Climate Dynamics, 44(11-12):3237-3260.
doi: 10.1007/s00382-014-2418-8
[31]   Mendes D, Marengo J A. 2010. Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios. Theoretical and Applied Climatology, 100(3):413-421.
doi: 10.1007/s00704-009-0193-y
[32]   Nikbakht J, Tabari H, Talaee P H. 2013. Streamflow drought severity analysis by percent of normal index (PNI) in Northwest Iran. Theoretical and Applied Climatology, 112:565-573.
doi: 10.1007/s00704-012-0750-7
[33]   Patil N S, Laddimath R, Pooja S, et al. 2015. Downscaling of precipitation data from GCM outputs using artificial neural network for Bhima basin. International Journal of Applied Environmental, 10(4):1493-1508.
[34]   Piani C, Haerter J, Coppola E. 2010. Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99:187-192.
doi: 10.1007/s00704-009-0134-9
[35]   Skamarock W C, Klemp J B, Dudhia J. 2008. A Description of the Advanced Research WRF Version 3. In: NCAR Technical Note TN-475+STR. Colorado, USA.
[36]   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.
[37]   Themebl M J, Gobiet A, Heinrich G. 2012. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change, 112:449-468.
doi: 10.1007/s10584-011-0224-4
[38]   Thom H C. 1958. A note on the gamma distribution. Monthly Weather Review, 86(4):117-122.
doi: 10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2
[39]   Torabi Haghighi A, Klove B. 2017. Design of environmental flow regimes to maintain lakes and wetlands in regions with high seasonal irrigation demand. Ecological Engineering, 100:120-129.
doi: 10.1016/j.ecoleng.2016.12.015
[40]   Tran Anh Q, Taniguchi K. 2018. Coupling dynamical and statistical downscaling for high-resolution rainfall forecasting: case study of the Red River Delta, Vietnam. Progress in Earth and Planetary Science, 5:28, doi: 10.1186/s40645-018-0185-6.
doi: 10.1186/s40645-018-0185-6
[41]   Tue Vu M, Aribarg T, Supratid S, et al. 2016. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok? Theoretical and Applied Climatology, 126(3-4):453-467.
doi: 10.1007/s00704-015-1580-1
[42]   Wilby R L, Charles S P, Zorita E, et al. 2014. Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. Supporting Material of the Intergovernmental Panel on Climate Change. http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf.
[43]   Yazdandoost F, Moradian S, Izadi A. 2020. Evaluation of water sustainability under a changing climate in Zarrineh River Basin, Iran. Water Resources Management, 34(15):4831-4846.
doi: 10.1007/s11269-020-02693-3
[44]   Yazdandoost F, Moradian S, Izadi A, et al. 2021. Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Journal of Atmospheric Research, 250:105369, doi: 10.1016/j.atmosres.2020.105369.
doi: 10.1016/j.atmosres.2020.105369
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