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DOI:10.22059/jwim.2022.333974.945
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Projection of the Effects of Climate Change on the Inflow to Maroon Dam
Mostafa Mirmehdi1, Mojtaba Shourian2, Ahmad Sharafati3, Saeed Lotfi4
1. Ph.D. Student, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2. Assistant Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
3. Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4. Bureau of Water and Wastewater Macro-Planning, Ministry of Energy, Tehran, Iran.
Received: November 15, 2021 Accepted: March 27, 2022
Abstract
The purpose of this study is to investigate the effects of climate change on the inflow of Maroon Dam using the SWAT model under two climatic scenarios RCP4.5 and RCP8.5 in the next three twenty-year periods. Flow data measured at Idnak and Tang-e-Takab stations were used to calibrate and validate the model. The Nash-Sutcliffe index of Idnak station was equal to 0.69 and 0.65 and Tang-e-Takab station of Behbahan was equal to 0.67 and 0.59 in the calibration and validation stages. The highest temperature increase will be in the final period and under the RCP8.5 climate scenario.
To simulate the flow in future periods, precipitation and air temperature under the two scenarios were micro-scaled using the LARS-WG model and by entering the data into the SWAT model, the inflow to the dam was simulated for the next three periods. The results of forecasting the inflow to the dam showed that although the amount of rainfall in the area has increased, but increasing the temperature in this basin will have a greater effect and efficiency in reducing the amount of flow. The highest decrease in the average inflow to the Maroon Dam in the near future is in the middle scenario of RCP4.5 with 21.91 and 26 percent and in the pessimistic scenario of RCP8.5 with 19 and 22.36 percent in February and March respectively. As a result, the maximum reduction of the inflow to the Maroon Dam compared to the baseline conditions is in the RCP4.5 release scenarios.
Keywords: Climate change, Reservoir inflow, Runoff estimation, Watershed modeling.
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Table 1. The character of meteorological stations in the Maroon catchment area
Rank Weather Station Station Type Longitude latitude Elevation Years
1 Idenak Climatological 50° 25' 39" 30° 56' 57" 612 1981-2018
2 Idenak Hydrometric 50° 25' 39" 30° 56' 57" 612 1981-2018
3 Tang-e-Takab Hydrometric 50° 00' 13" 30° 00' 40" 308 1981-2018
Figure 2. a) Land Use, b) Soil map and c) DEM of the Maroon catchment area
Table 2. The attribute table of land use
Class Name Area (km2) Percentage Type
CRDY 340.38 8.9 Dryland Cropland
CRIR 29.88 0.78 Irrigated Cropland
CRGR 37.15 0.97 Cropland/ Grassland mosaic
CRWO 401.08 10.53 Cropland/Woodland mosaic
GRAS 1246.87 32.74 Grassland
SHRB 1752.62 46.02 Shrubland
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Table 3. The character of soils in soil map
Rank Soil Type to SWAT Soil Type Hydrologic Type
1 Rc33-3bc-3254 LOAM D
2 Xh7-2-3ab-3297 CLAY-LOAM D
3 I-Rc-YK-c-3508 LOAM D
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Table 4. The character of GCM models
Model Resolution Developer
EC-EARTH 1.125°×1.125° EC-Earth consortium
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Table 5. Model sensitivity analysis results
Rank Parameter Minimum Maximum Fitted Description
1 r__CN2.mgt -0.864 -0.284 -0.503 SCS runoffcurve number 2 v__SMTMP.bsn -1.854 3.445 -1.276 Snow melt base temperature
3 v__SMFMN.bsn 3.822 5.303 4.146 Minimum melt rate for snow during the year (occurs onwinter solstice) 4 v__REVAPMN.gw 196.877 294.428 215.509 Threshold depth of water in the shallow aquifer for"revap"to occur (mm) 5 v__RCHRG_DP.gw 0.699 0.9 0.858 Deep aquifer percolation fraction
6 v__SLSUBBSN.hru 13.101 40.857 20.845 Average slope length 7 v__GW_DELAY.gw 5.646 42.167 28.69 Groundwater delay (days)
8 v__CH_K2.rte 29.616 105.522 66.43 Effective hydraulic conductivity in main channelalluvium 9 v__ESCO.hru 0.228 0.683 0.409 Soil evaporation compensation factor
10 r__HRU_SLP.hru 0.324 0.714 0.499 Average slope steepness 11 v__ALPHA_BF.gw 0.635 0.878 0.642 Baseflow alpha factor (days) 12 r__SOL_BD().sol 0.442 0.884 0.505 Moist bulkdensity
13 r__SOL_AWC().sol -0.446 0.083 -0.185 Available water capacity of the soil layer 14 r__SOL_K().sol -0.050 0.601 0.442 Saturated hydraulic conductivity 15 v__SFTMP.bsn 3.279 6.009 4.122 Snowfall temperature
16 v__ALPHA_BNK.rte 0 0.182 0.044 Baseflow alpha factor for bank storage 17 v__LAT_TTIME.hru 0 37.56 18.066 Lateral flow travel time
18 v__PLAPS.sub 90.983 142.258 132.567 Precipitation lapse rate 19 v__TLAPS.sub -5.931 -0.840 -4.561 Temperature lapse rate
20 r__PCPMM().wgn 0.111 0.491 0.34 Average amount of precipitation falling in month(mm/dd)
Table 6. Values of model performance evaluation indicators in runoff simulation in implemented models
Idenak station Tange-takab station
calibration validation calibration validation
NS 0.69 .65 0.67 0.59
R2 0.7 0.73 0.68 0.61
r-factor 0.79 0.72 0.99 0.79
p-factor 0.81 0.78 1.3 0.89
Figure 3. Results of calibration and validation of Idenak station using SUFI-2 algorithm
0 50 100 150 200 250 300 350 400 450 500
Discharge (m3/s)
Time
95 PPU Observation Best Simulation
Calibration Validation
Figure 4.Calibration and validation results of Tang-e-Takab Behbahan station using SUFI-2 algorithm
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climatic scenarios 0
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Percipitation (mm)
Time
2021-2040 Obs RCP4.5 RCP8.5
0 20 40 60 80 100 120 140 160 180
jan feb mar apr may jun jul aug sep oct nov dec
Percipitation (mm)
Time 2041-2060
Obs RCP4.5 RCP8.5
0 20 40 60 80 100 120 140 160 180
jan feb mar apr may jun jul aug sep oct nov dec
Percipitation (mm)
Time
2061-2080 Obs RCP4.5 RCP8.5
Figure 6. Changes in the average monthly temperature of Idenak station in future periods under RCP4.5 and RCP8.5
climatic scenarios
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1. Intergovernmental Panel on Climate Change 2. Soil & Water Assessment Tool
3. Global Circulation Model
4. Food and Agriculture Organization of the United Nations
5. Global Land Cover Characterization 6. Hydrological Response Unit
7. Coupled Model Intercomparison Project
?@ A 6
† ' 6 [ " cI l d" 6 .
?
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