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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (12): 1633-1647    DOI: 10.1007/s40333-024-0069-0    
Research article     
Impact of climate change on water resources in the Yarmouk River Basin of Jordan
Abdelaziz Q BASHABSHEH*(), Kamel K ALZBOON
Environmental Engineering Department, Al-Huson University College, Al-Balqa' Applied University, Irbid 21510, Jordan
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Abstract  

Understanding the impact of climate change on water resources is important for developing regional adaptive water management strategies. This study investigated the impact of climate change on water resources in the Yarmouk River Basin (YRB) of Jordan by analyzing the historical trends and future projections of temperature, precipitation, and streamflow. Simple linear regression was used to analyze temperature and precipitation trends from 1989 to 2017 at Irbid, Mafraq, and Samar stations. The Statistical Downscaling Model (SDSM) was applied to predict changes in temperature and precipitation from 2018 to 2100 under three Representative Concentration Pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5), and the Soil and Water Assessment Tool (SWAT) was utilized to estimate their potential impact on streamflow at Addasiyia station. Analysis of data from 1989 to 2017 revealed that mean maximum and minimum temperatures increased at all stations, with average rises of 1.62°C and 1.39°C, respectively. The precipitation trends varied across all stations, showing a significant increase at Mafraq station, an insignificant increase at Irbid station, and an insignificant decrease at Samar station. Historical analysis of streamflow data revealed a decreasing trend with a slope of -0.168. Significant increases in both mean minimum and mean maximum temperatures across all stations suggested that evaporation is the dominant process within the basin, leading to reduced streamflow. Under the RCP scenarios, projections indicated that mean maximum temperatures will increase by 0.32°C to 1.52°C, while precipitation will decrease by 8.5% to 43.0% throughout the 21st century. Future streamflow projections indicated reductions in streamflow ranging from 8.7% to 84.8% over the same period. The mathematical model results showed a 39.4% reduction in streamflow by 2050, nearly double the SWAT model's estimate under RCP8.5 scenario. This research provides novel insights into the regional impact of climate change on water resources, emphasizing the urgent need to address these environmental challenges to ensure a sustainable water supply in Jordan.

Key words： streamflow      climate change      Soil and Water Assessment Tool (SWAT)      Statistical Downscaling Model (SDSM)      Yarmouk River Basin      Jordan     
Received: 05 May 2024      Published: 31 December 2024
Corresponding Authors: *Abdelaziz Q BASHABSHEH (E-mail: abdelazizbashabsheh@gmail.com)
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Abdelaziz Q BASHABSHEH
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Abdelaziz Q BASHABSHEH, Kamel K ALZBOON. Impact of climate change on water resources in the Yarmouk River Basin of Jordan. Journal of Arid Land, 2024, 16(12): 1633-1647.

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http://jal.xjegi.com/10.1007/s40333-024-0069-0     OR     http://jal.xjegi.com/Y2024/V16/I12/1633

Fig. 1 Overview of the Yarmouk River Basin (YRB) and the locations of meteorological stations, rainfall stations, and streamflow gauge station
Station name Pettitt's test SNHT Von Neumann's test
α P-value Check α P-value Check α P-value Check
Irbid 0.05 0.944 OK 0.05 0.874 OK 0.05 0.341 OK
Mafraq 0.05 0.066 OK 0.05 0.138 OK 0.05 0.032 NOT OK
Samar 0.05 0.033 NOT OK 0.05 0.739 OK 0.05 0.743 OK
Table 1 Homogeneity test results for annual precipitation data series
Station Name Pettitt's test SNHT Von Neumann's test
α P-value Check α P-value Check α P-value Check
Irbid 0.05 0.069 OK 0.05 0.176 OK 0.05 0.415 OK
Mafraq 0.05 0.099 OK 0.05 0.261 OK 0.05 0.723 OK
Samar 0.05 0.006 NOT OK 0.05 0.115 OK 0.05 0.022 NOT OK
Table 2 Homogeneity test results for annual mean temperature data series
Station name Pettitt's test SNHT Von Neumann's test
α P-value Check α P-value Check α P-value Check
Addasiyia 0.05 0.227 OK 0.05 0.699 OK 0.05 0.16 OK
Table 3 Homogeneity test results for streamflow data series
Fig. 2 Trend of mean maximum temperature at Irbid, Mafraq, and Samar stations during 1989-2017
Fig. 3 Trend of mean minimum temperature at Irbid, Mafraq, and Samar stations during 1989-2017
Fig. 4 Trend of annual precipitation at Irbid, Mafraq, and Samar stations during 1989-2017
Period RCP2.6 RCP4.5 RCP8.5
Temperature change (°C) Rate of change in precipitation (%) Temperature change (°C) Rate of change in precipitation (%) Temperature change (°C) Rate of change in precipitation (%)
2018-2050 0.32 -8.5 0.37 -10.3 0.44 -12.3
2051-2079 0.42 -10.7 0.61 -18.7 0.10 -29.6
2080-2100 0.42 -11.4 0.73 -21.6 1.52 -43.2
Table 4 Projected changes in temperature and precipitation under RCP2.6, RCP4.5, and RCP8.5 scenarios for different future periods
Parameter Definition Unit Estimated value Calibrated value Range
CN2 Curve number - 83 76 0-100
Sol_Awc Available water capacity of the soil layer mmH2O/mm soil 0.10 0.13 -
Gw_Delay Groundwater delay time d 310 450 -
ESCO Soil evaporation compensation factor - 0.95 0.75 0.00-1.00
Sol_K Saturated hydraulic conductivity mm/h 3.0 11.5 0.0-100.0
Sol_Z Depth from soil surface to bottom of layer mm 300 1100 0-2000
Table 5 Parameters used in the SWAT model calibration
Objective function Calibration (1992-2008) Validation (2009-2017)
R2 0.964 0.863
NSE 0.936 0.834
PBIAS (%) 9.7 12.6
Simulated mean (m3/s) 3.242 2.271
Observed mean (m3/s) 3.594 2.023
Observed Std Dev (m3/s) 11.022 4.524
Simulated Std Dev (m3/s) 9.111 3.494
Table 6 Summary of the objective functions in streamflow simulation during the calibration and validation periods
Fig. 5 Observed monthly streamflow versus simulated streamflow during 1992-2017
Fig. 6 Temporal variations in observed streamflow during 1989-2017
Fig. 7 Predicted changes of the simulated streamflow under RCP2.6, RCP4.5, and RCP8.5 scenarios for different future periods
Number Observed streamflow (m3/s) Simulated streamflow (m3/s) Error (m3/s)
1 1.021 1.036 -0.015
2 1.036 0.987 0.049
3 1.020 1.008 0.012
4 1.003 1.235 -0.232
5 0.927 1.174 -0.247
6 1.001 1.089 -0.088
7 0.995 0.967 0.028
8 1.007 1.080 -0.073
9 1.000 0.983 0.017
10 1.000 0.980 0.020
11 0.968 1.316 -0.348
12 1.000 1.071 -0.071
13 0.979 0.984 -0.005
14 0.970 0.997 -0.027
15 0.968 1.062 -0.094
16 0.980 1.258 -0.278
17 1.001 1.089 -0.088
18 3.013 2.367 0.646
19 0.983 1.040 -0.057
20 0.999 1.026 -0.027
21 0.968 1.059 -0.091
Table 7 Performance evaluation of the mathematical model for monthly streamflow predictions during 1989-2017
Fig. 8 Predicted changes of the streamflow using the mathematical model for different future periods
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