Flow regime changes of Gamasiab river under climate change scenarios

Document Type : Research Paper


1 Watershed management department, University of Hormozgan

2 Faculty of Agricultural Engineering and Technology, Tehran University

3 watershed management department, university of hormozgan.


The first effects of climate change are visible on temperature and precipitation; changing these variables will disrupt the current order of the hydrological cycle. The new state of the hydrological cycle causes a change in the flow regime. Natural flow regime plays a major role in sustaining native biodiversity and ecosystem integrity in rivers (Poff et al., 1997). Stream flow regime alteration may also affect aquatic organisms, sediment movement and flood plain interactions (Gibson et al., 2005). Characterization of flow regime has been examined by Mohammad et al. 2015 via metrics that describe the magnitude, frequency, duration, timing and rate of change for stream flow. Their results in the Champlain lake basin in the United States indicated 30% increase in 7 day maximum flow, an increase in flood days, and a threefold increase of the base flow index.
Material and Methods
Study area
The Gamasiab River watershed is located between Hamedan, Kermanshah and Lorestan provinces. The watershed area is about 11690 square kilometers, with 515816 hectares of agricultural land, 619583 hectares of pasture, 4938 hectares of urban land, and 28663 hectares of others lands.
In this study, the SWAT model was used to simulate flow discharge. The SWAT model needs three maps to simulate discharge including digital elevation map (DEM), soil and land use map. This model divides sub-basins into a number of hydrologic response units (HRUs), each HRU is the main simulation unit in the SWAT model (30). Daily precipitation (Pcp), minimum and maximum air temperatures (Tmin and Tmax) for the period from 1977 to 2005 were obtained. The daily discharge in the Polchehr hydrometric station during the years 1977 to 2005 were used for calibration of the model as well as the comparison of changes in flow regime under climate change conditions.
Optimization of parameters and uncertainty analysis of the SWAT model were performed by using SWAT-CUP software by the SUFI2 algorithm (Sequential Uncertainty Fitting Ver. 2).
In order to simulate and predict the effects of climate change in the future, general circulation models (GCM) were used. The main problem in using general circulation models in regional research is their large scale. There are various methods for producing regional climate scenarios from these models, which called downscaling (38). In this research the Change Factor Mean-Based Method was used to downscaling CMIP5 models.
The flow regime and its changes were studied under conditions of climate change for high flow disturbance and low flow disturbance distribution. The distribution of high flow was investigated by using three indexes including 7-day maximum flows (7QMAX), a high discharge distribution (Q1.67) and flood duration (FLDDUR). A seven-day minimum flow (7QMIN) parameter was used to investigate the distribution of low flows. The Daily flow Coefficient of variation was also used to show the overall changes of the flow regardless of the time series. 7-day maximum flows are the average of maximum daily discharge of seven days per year. For this purpose, the moving average of the daily discharge in seven-day is calculated for each year and the biggest one selected as 7-day maximum flows of the year (9). The Q1.67 index is defined as Flow of magnitude exceeding a return interval of 1.67 years based on a log-normal distribution (13). Flood duration (FLDDUR) is also the average number of days per year when flow equals or exceeds Q1.67 (27). Seven-day minimum flows are the average of minimum daily discharge of seven days per year (27). The Kernel probability density graph was used to show the flood duration for observational data and scenarios
The results of future flow simulation in the Gamasiab basin show that the mean of the discharge based RCP2.6 scenario will be close to 36.6 m3/s in the near future, which is slightly more than the mean of the discharge in observation period (33.1m3/s). Continuing this scenario would increase the average of discharge by 17.8% and reach 40.4m3/s in the future. The average discharge under RCP8.5 scenario will be reduced to 30.6m3/s in the near future, and the continuation of the RCP8.5 scenario in the far future will cause a very sharp decrease in average of discharge and reach 19.1 m3/s.
The 7QMAX changes under RCP2.6 scenario in the near future show the same trend by comparing observation period. The average of 7QMAX in the observation period is 209m3/s. Under the RCP2.6 scenario, the average of 7QMAX in the near future will reach 154.4m3/s, and in the far future it will reach 183.7m3/s. The 7QMAX under the RCP8.5 scenario will be reduced in the near and far future. The average 7QMAX in the near future will be close to 146/6 m3/s, and in the far future it will reach 96.8m3/s.
The 7QMIN in the near and far future will be a little change compared to the observation period. The average of 7QMIN in the observation period is 2m3/s, and this average under RCP2.6 scenario for the near and far future will be 1.2 and 1.6 m3/s respectively. Under the RCP8.5 scenario, 7QMIN will be significantly reduced, with an average of 0.9 m3/s in the near future and 0.48 m3/s in the far future. With the fitting of the log-normal distribution, the maximum instantaneous velocity of the discharge was calculated with a return period of 1.67 years, thus, the value of Q1.67 was calculated 211.29m3/s.To calculate the flood duration in each year, the number of days which flow was equal to or greater than Q1.67 was counted. According to the kernel density diagram, during the observation period and the selected scenarios, flood events with a maximum of 5 days duration are most likely to occur. It is also observed that under RCP2.6, in the near and far future, the probability of occurrence of floods with longer duration is expectable.
The study shows that the Gamasiab River watershed is flashy. Under the scenario RCP2.6, which is a favorable scenario with minimal greenhouse gas emissions, the coefficient of variation will be reduced significantly. It can be concluded that in addition to increasing the average of the runoff under RCP2.6, the flash floods of the river will reduce. In this regard, Wu et al. (2015) achieved similar results. Under the scenario RCP8.5, more floods in the Gamasiab River watershed occurs. Alkama et al. (2013) and Dirmeyer et al. (2014) also predict more flooding event under the RCP8.5 scenario.
The 7QMAX and 7QMIN in all scenarios will decrease compared to the observation period. In both indexes, the lowest decreases are under RCP2.6 in far future, and the largest decline is under RCP8.5 in the far future. The results of Papadimitriou et al. (2016) show a decrease in minimum flow, but the results of Mohammed et al. (2015) indicated 7QMAX increase and a minimum discharge increase as well.
The kernel chart in observation period shows that the duration of floods occurrence most likely is maximum 5 days. Under RCP2.6, in the near future floods with a maximum duration of 5 days are the most likely to occur but are less than the observation period, instead of under RCP2.6 10 to 15 days of floods duration are more than the observation period. Base on this chart it can be concluded that under the scenario RCP2.6, the duration of the floods will be increase compared to the observation period, and it will be longer in the end of this century, In this regard, Mohammed et al. (2015) predicted an increase in flood days. The results under the RCP8.5 scenario indicate that the flood duration in the near future will be dramatically reduced and at the end of the current century, the flood frequency with a discharge equal to or greater than Q1.67 will be sharply reduced.


Main Subjects