Evaluation Theerosion and Sediment in Different land Uses of Tamer Watershed, Golestan Province Using SWAT Model

آب و خاک

Article 5, Volume 31, Issue 5 - Serial Number 55, November 2018, Pages 1383-1395   

Document Type: Research Article

DOI: 10.22067/jsw.v31i5.62746

Authors

farshad kiani^{* 1}; Behroz Behtari nejad²; Ali Najafi nejad²; Abdolreza Kaboli4³

¹Gorgan University of Agricultural Sciences and Natural Resources

²Gorgan University of Agricultural Sciences and Natural Resources.

³Gorgan Regional water authority

Abstract

Introduction: population growth, urbanization and land use changes cause negative effects in natural ecosystems and water resources. Soil erosion is one of the most important problems in agriculture and natural resources of Golestan province. Using low cost and accurate methods for planning and proper management of land and water resources are essential for estimating consequences of soil erosion and providing appropriate solutions to reduce soil losses.
Materials and Methods: The study area is located in eastern part of Golestan province with an area of 1524 square Kilometers. The average annual precipitation of the region is 496 millimeters. In this watershed, rainfall decreases from south and south west to north and north east (due to the remoteness from the Caspian Sea), while evapotranspiration, temperature and the number of dry months increase in the same direction. Also the average annual temperature of the watershed and its relative humidity and evaporation are 17.8°C, 68.5 % and 1398.34 millimeters, respectively. Tamer watershed was divided into 15 sub-watersheds by adding an outlet in the site of Tamar gauging station. In this study, the SWAT model was used to simulate erosion and sedimentation. To compare the measured and simulated data and evaluation of the SWAT performance in terms of simulating flow and sediments, daily flow (cubic meters per second) and sediment (tons per day) data at the Tamar gauging station located in Tamar’s watershed outlet was collected from the studies of water resources organization (Tamab). Simulated values were generally consistent with the data observed during calibration and validation period. At this stage of calibration, the SUFI-2 model was used to optimize the parameter values. In this study, daily rainfall and temperature data recorded during an 8-year period by the stations within the watershed were imported into the model. The daily discharge data and daily sediment data of Tamar station recorded during 1999- 2006 were selected. Then model was run using runoff and sediment parameters, and ranges of parameters were adjusted at each iterations, and therefore SWAT model was calibrated using SUFI-2 model. After calibration, model must be validated and its ability to predict future events must be determined. Validation was performed using the runoff and sediment data recorded in Tamar gauging station from 2007 to 2010.
Results and Discussion: NS, R2, R-factor and P-factor were estimated for runoff calibration about 0.76, 0.77, 0.06 and 69 and for runoff evaluation 0.72, 0.75, 0.05 and 69 respectively. The same parameters were also measured for sediment calibration about 0.54, 0.62, 0.15, and 16 and sediment evaluation 0.55, 0.61, 0.35, and 12 respectively. The results showed that irrigated agriculture 24.95 and 15.56 t ha -1y-1 respectively, with average erosion and sediment ha of agriculture by an average of 20.23 and 12.33 t ha -1y-1 respectively erosion and sediment erosion and deposition are tons per hectare maximum value. Results also showed that the soil loss caused by erosion in this watershed is average 6.49 t ha -1y-1 in sediment and 10.28 t ha -1y-1 in erosion.
Conclusion: The assessment factors showed that model has successfully simulated the daily runoff discharge during calibration and validation phases with a Nash-Sutcliffe coefficient of 0.76 and 0.72. A Nash-Sutcliffe coefficient above 0.5 could be acceptable for sediment simulation. However, sediment load simulated for rainy seasons has been lower than actual value while this value has been higher than actual value during dry seasons. In most months of the year, model results are higher than measured values and this issue is more pronounced in the peak runoffs. This issue is due to limitations in spatial distribution of rainfall, so when a small area in watershed experience a severe rainfall, model considers the impact for the entire watershed and therefore overestimates the total runoff. The results showed that SWAT model can be a useful tool for the simulation of flow and sediment basins in the loess land.
Simulation results showed that land use changes have resulted in corresponding increases in surface runoff and sediment. Rates were highly variable both spatially and temporally, and the agricultural lands were most significantly affected. These land use changes have negative implications for the ecological health of the river system as and local communities.

Keywords

Digital Elevation Map; Modeling; SWAT-CUP; Uncertainty

References

1- Abbaspour K. C., Sonnleitner C., and Schulin R. 1999. Uncertainty in Estimation of Soil Hydraulic Parameters by Inverse Modeling: Example Lysimeter Experiments.Journal of soil science socity of American, 63: 501-509.

2- Abbaspour K. C., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., Zobrist J., and Srinivasan R. 2007. Modelling hydrology and water quality in thepre-alpine/alpine Thur watershedusing SWAT. Journal of Hydrology, 333: 413– 430.

3- Abbaspour K.c. 2008. SWAT Calibration and Uncertainty Programs - A User Manual. Department of Systems Analysis, Integrated Assessment and Modelling (SIAM), Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, 2008, 95pp.

4- Akhavan S., Abedi-Koupai J., Mousavi S.F., Afyuni M., Eslamian S.S., and Abbaspour K. 2010. Application of SWAT model to investigate nitrate leaching in Hamadan-Bahar watershed, Iran. Journal of Ecosystem Environment, 139(4):675-688.

5- Alansi A.W., M.S.M. Amin G., Abdul Halim H.Z.M., Shafri and Aimrun W. 2009. Validation of SWAT model for stream flow simulation and forecasting in Upper Bernam humid tropical river basin, Malaysia.Journal of Hydrology and Earth System Sciences, 6: 7581–7609.

6- Arnold J.G., Srinivasan R., Muttiah R.S., and Williams J.R. 1998. Hydrologic modeling and assessment part I: model development. Journal of Water Resources Association, 34(1):73-89.

7- Bakker M.M., Govers G., Doorn A.V., Quetier F., Chouvards D., and Rounsevel M. 2008. The response of soil erosion and sediment export to land-use change in four area of Europe: The importance of landscape pattern. Journal of Geomorphology. 98: 213-226.

8- Borah D. K., and Bera M. 2003. Watershed-scale hydrologic and nonpoint-sourcc pollution models: Review of mathematical bases. Trans. ASAE, 46(6), 1553-1566.

9- Bosch D.D., Sheridan J.M., Batten H. L., and Arnold J. G. 2004. Evaluation of the SWAT Model on coastal Plain Agricultural Watershed. Trans. ASAE, 47(5), 1493-1506.

10- Bouraoui F., Grizzetti B., Granlund K., Rekolainen S., and Bidoglio G. 2004. Impact of climate change on the water cycled and nutrient losses in a Finnish catchment. Clim. Change 66(1-2): 109-126.

11- Chen X., Bing C., and Hongjing W. 2014. Parameter Uncertainty Analysis of Surface Flow and Sediment Yield in the Huolin Basin, China, Journal of Hydrology Engineering, 19:6, 1224-1236.

12- Demir Y.M., Atasoy M., Bayrak T., and Biyik C. 2008. Evaluating Sustainable Land Use for the De Irmendere Valley: A Case Study From NortheasternTurkey.14:626-633.

13- Duan Q. 2003. Global Optimization for Watershed Model Calibration, in Calibration of Watershed Models, edited by Q. Duan, H. V. Gupta, S. Sorooshian, A. N. Rousseau, and R. Turcotte, pp. 89-104, AGU, Washington, DC.

14- Faramarzi M., Abbaspour K., Schulin R., and Yang H. 2009. Blue and green water resources in Iran. Journal of Hydrology. Process. 23: 486–501.

15- Geza M., and McCray J.E. 2008. Effects of soil data resolution on SWAT model streamflow and water quality predictions. Journal of Enviromentn. Manager, 88: 393-406.

16- Golami SH., and Nasiri M. 2014. Simulation of Atrak River monthly discharge using SWAT model, Case study: Maraveh Tappeh watershed, Gholestan province. Journal of Watershed Engineering and Management.7 (2): 126-135.

17- Green C.H., Greensoun J.G., Williams J.R., Haney R., and Harmel R.D. 2007. Soil and water assessment tool hydrologic and water quality evaluation ofpoultry litter application to small scale sub watershed in Texas. 50, 1199-1209.

18- Hosseini M., Ghafouri M., Tabatabaei M., Nadergholi E., and Zare Garizi A. 2016. Estimation of hydrologic budget for Gharesou watershed, Iran. Journal of Ecopersia, 4(3): 1455-1469.

19- Hyung Kyung J., Jong Yoon P., Hyun Kyo J., Hyun Hin S., Hyung Joong K., and Seong Joon K. 2011. The Uncertainity Analaysis of SWAT Simulated Streamflow and Water Quaility Applied to Chungju Dam Watershed of South Korea for climate change analysisjournal of Agriculture. System, 38(3): 225–238.

20- Jafarzadeh M.S., Rouhani H., Salmani H., and Fathabadi.H. 2016. Reducing uncertainty in a semi distributed hydrological modeling within the GLUE framework. Journal of Water and Soil Conservation, 23 (1): 83-100.

21- Keppner W.G., Semmens D. J., Bassett S. D., Mouat D. A., and Goordrich D. C. 2004. Scenario analysis of the San Pedro river, analyzing hydrological consequences of a future environment. Journal of Enviromental Monitoring and Assessment, 94: 115-127.

22- Mahzari S., Kiani F., Khormali F., and Azimi M. 2016. Using SWAT model to determine runoff, sediment yield and Nitrate loss in Gorganrood watershed, Iran. Journal of Ecopersia, 4(2): 1359-1377.

23- Miyata S., Kosugi K., Gomi T., Onda Y., and Mizuyama T. 2007. Surface runoff as affected by soil water repellency in a japanes cypress forest. Journal of Hydrological processes, 21 (17): 2365-2376.

24- Mohammad A.G., and Adam M. A. 2010. The impact of vegetative cover type on runoff and erosion under different land use. Journal of Catena, 81: 97-103.

25- Najafi Q. 2005. Land and agricultural lands in Iran. Monthly Dehati Magazine, 24: 17-24.

26- Nash J.E., and Sutcliffe J.V. 1970. River flow forecasting through conceptual models, Part 1. A discussion of principles. Journal of Hydrology, 10: 282–290.

27- Neitsch S.L., Arnold J.G., Kiniry J.R., Williams J.R., and King K.W. 2002. Soil and Water Assessment Tool Theoretical Documentation-Version 2000. .

28- Refahi H.Gh. 2006. Water erosion and conservation. Tehran University Publication. 22(5), 671.

29- Salmani H., Mohseni Saravi M., and Rouhan Hand Salajeghe A. 2012. Evaluation of Land Use Change and its Impact on the Hydrological Process in the Ghazaghli Watershed, Golestan Province. Journal of Watershed Management Research, 3(6): 43-60.

30- Shahriari A., Khormali F., and Azarmdel H. 2011. Clay mineralogy of Mollisols and Mollisols-like soils as affected by physiographic unit formed on loess deposits in southern Gorgan River, Golestan province. Journal of Water and Soil Conservation, 18(4): 63-79.

31- Van Liew M. W., and J Garbrecht. 2003. Hydrologic simulation of the Little Washita River experimental watershed using SWAT. Journal of American Water Resource. Assoc, 39(2): 413-426.

32- Vigiak O., Malago A., Bouraoui F., Vanmaercke M., and J Poesen. 2015. Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large basins.journal of Science of the total environment, 538: 855-875.