برآورد تغییرات مکانی و زمانی رطوبت خاک در حوضه آبخیز مرغاب با استفاده از SWAT

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تهران، کرج، ایران.

2 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تهران، کرج، ایران

3 پژوهشگر ارشد، موسسه مدیریت منابع inter 3، برلین آلمان

4 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه گیلان

چکیده

تهیه نقشه یکپارچه رطوبت خاک با وضوح مکانی بالا و کیفیت مناسب اهمیت زیادی در مدیریت اراضی دارد. با توجه به کمبود ایستگاه‌های پایش در حوزه‌های آبخیز به‌ویژه در مناطق کوهستانی، مطالعات میدانی پایش رطوبت خاک فرآیندی زمان‌بر، پرهزینه و با خطا است. جهت دستیابی به روشی مناسب برای شبیه‌سازی مکانی و زمانی رطوبت‌ خاک در حوضه‌ آبریز مرغاب استان خوزستان با مساحت 690 کیلومترمربع، از مدل SWAT استفاده شد. از داده‌های هواشناسی روزانه ایستگاه باران‌سنجی بارانگرد و سینوپتیک ایذه و نقشه‌های خاک، کاربری اراضی و رقومی ارتفاع به‌عنوان ورودی مدل استفاده شد. جهت تحلیل حساسیت، واسنجی، عدم قطعیت و اعتبار‌سنجی مدل از برنامهSUFI-2  و آمار رواناب ایستگاه هیدرومتری جلوگیر- مرغاب استفاده گردید. سال‌های 2019 -2003 میلادی برای واسنجی و سال‌های 2002-1995 میلادی برای اعتبار‌سنجی مدل با سه سال دست‌گرمی 1994-1992 به کار گرفته شد. برای تعیین نکویی برازش مدل در شبیه‌سازی رواناب از ضرایب نش‌ساتکلیف (NSE) و ضریب تبیین (R2)، برای تعیین درجه عدم قطعیت از شاخص‌های P-Factor  وR-Factor  استفاده شد. با توجه به هیدروگراف‌های شبیه‌سازی‌شده و مشاهده‌ای رواناب ماهانه و معیارهای آماری، مدل SWAT در هر دو دوره واسنجی و اعتبارسنجی دارای نتایج خوب در شبیه‌سازی رواناب بود. مقادیر ضرایب NSE, R2،P-Factor  وR-Factor در دوره واسنجی به ترتیب 76/0، 73/0، 68/0 و62/0و در دوره اعتبار سنجی به ترتیب 73/0، 71/0، 60/0 و 65/0 بود. پس از واسنجی و اعتبار سنجی مدل، نقشه‌های رطوبت خاک در سری زمانی 2019-1995 استخراج گردید. نتایج نشان داد، مدل‌سازی SWAT ابزاری امیدوارکننده‌ای جهت شبیه‌سازی رطوبت خاک در حوضه آبریز با توزیع مکانی )مقیاس زیر حوضه و واحدهای پاسخ هیدرولوژی) و نیز زمانی )مقیاس ماهیانه و سالیانه) مناسب است.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of spatial and temporal variability of soil moisture in marghab watershed using swat

نویسندگان [English]

  • Padideh Javadi 1
  • Hossein Asadi 2
  • Aliasghar Besalatpour 3
  • Majid Vazifehdoust 4
1 Department of Soil Sciences and Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Department of Soil Sciences and Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Senior Researcher, inter 3 - Institut für Ressourcenmanagement, Berlin, Germany
4 Associate professor, Department of Water Engineering, Faculty of Agricultural Science, University of Guilan,
چکیده [English]

The integrated maps of soil moisture having high spatial resolution and appropriate quality are of great importance in land management. Due to the lack of monitoring stations in watersheds, especially in mountainous areas, field monitoring of soil moisture is a time-consuming, costly and error-prone process. SWAT model was used to obtain a suitable method for spatial and temporal simulation of soil moisture in the Marghab watershed of Khuzestan province with an area of 690 km2. The daily meteorological data of Barangard and Izeh synoptic stations, soil and land use maps, and digital elevation model were used as inputs to the model. The SUFI-2 program was used for calibration, sensitivity and uncertainty analysis, and validation of the model using the runoff data of Jologir-Marghab hydrometric station. The model was run from 2003 to 2019 for calibration and from 1995 to 2002 for validation, with a three-year warm-up from 1992-1994. Nash-Sutcliffe efficiency (NSE) and determination coefficient (R2) were used to determine the goodness of fit of the model, and P-Factor and R-Factor indices were used to determine the degree of uncertainty. Based on the simulated and observed monthly runoff hydrographs as well as the statistical criteria, the SWAT performance in simulating monthly runoff was acceptable both in the calibration and validation periods. The NSE, R2, P-Factor, and R-Factor were 0.76, 0.73, 0.68, and 0.62, respectively in the calibration period, and 0.73-0.71-0.60 and 0.65, respectively in the validation period. After model calibration and validation, soil moisture maps were obtained for the 1995-2019 period. The results indicated that SWAT model is a promising tool for simulating soil moisture in the catchment area with appropriate spatial (sub-basin scale and hydrological response units) and temporal (monthly and annual scale) distributions.

کلیدواژه‌ها [English]

  • runoff
  • model sensitivity analysis
  • uncertainty
  • land use
  • digital elevation model

مراجع

Abbaspour, K. C. (2008). SWAT-CUP2: 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.
Abbaspour, K. C. (2022). The fallacy in the use of the “best-fit” solution in hydrologic modeling. Science of the Total Environment, 802, 149713.
Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S. R. I. N. I. V. A. S. A. N. B., Srinivasan, R., Yang, H., & Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733-752.
Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., & Klöve, B. (2015). Modelling hydrology and water quality of the European Continent at a subbasin scale: calibration of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733-752.
Abbaspour, K. C., Johnson, C. A., & Van Genuchten, M. T. (2004). Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3(4), 1340-1352.
Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., & Srinivasan, R. (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333(2-4), 413-430.
Al Masmoudi, Y., Bouslihim, Y., Doumali, K., El Aissaoui, A., & Namr, K. I. (2021). Application of the random forest model to predict the plasticity state of vertisols. Journal of Ecological Engineering, 22(2).
Ansari, M. R., Gorji, M., Sayad, G. A., Shorafa, M., & Hemadi, K. (2016). Simulation of Runoff in Rood Zard Basin using Arc Swat Model. Irrigation Sciences and Engineering, 38(4), 97-107 (In Persian).
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: model development 1. JAWRA Journal of the American Water Resources Association, 34(1), 73-89.
Brevik, E. C. (2015, April). Teaching about the Links between Soils and Climate: An International Year of Soil Outreach by the Soil Science Society of America. In EGU General Assembly Conference Abstracts (p. 15066).
Byun, K., Liaqat, U. W., & Choi, M. (2014). Dual-model approaches for evapotranspiration analyses over homo-and heterogeneous land surface conditions. Agricultural and Forest Meteorology, 197, 169-187.
Fan, K., Zhang, Q., Singh, V. P., Sun, P., Song, C., Zhu, X., & Shen, Z. (2019). Spatiotemporal impact of soil moisture on air temperature across the Tibet Plateau. Science of the Total Environment, 649, 1338-1348.
Faramarzi, M., Abbaspour, K. C., Vaghefi, S. A., Farzaneh, M. R., Zehnder, A. J., Srinivasan, R., & Yang, H. (2013). Modeling impacts of climate change on freshwater availability in Africa. Journal of Hydrology, 480, 85-101.
Faramarzi, M., Yang, H., Schulin, R., & Abbaspour, K. C. (2010). Modeling wheat yield and crop water productivity in Iran: Implications of agricultural water management for wheat production. Agricultural Water Management, 97(11), 1861-1875.
Fathololoumi, S., Vaezi, A. R., Alavipanah, S. K., Ghorbani, A., Saurette, D., & Biswas, A. (2021). Effect of multi-temporal satellite images on soil moisture prediction using a digital soil mapping approach. Geoderma, 385, 114901.
Fathololoumi, S., Vaezi, A. R., Alavipanah, S. K., Ghorbani, A., & Biswas, A. (2020). Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area. Science of the Total Environment, 724, 138319.
Forkuor, G., Hounkpatin, O. K., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS one, 12(1), e0170478.
Gholami, A., Habibnejad Roshan, M., Shahedi, K., Vafakhah, M., & Solaymani, K. (2016). Hydrological stream flow modeling in the Talar catchment (central section of the Alborz Mountains, north of Iran): Parameterization and uncertainty analysis using SWAT-CUP. Journal of Water and Land Development. (In Persian).
Hosseini, S., Memarian, H., & Memarian, H. (2019). Using SWAT and SWAT-CUP for hydrological simulation and uncertainty analysis in arid and semi-arid watersheds (Case study: Zoshk Watershed, Shandiz, Iran). Iranian Journal of Rainwater Catchment Systems, 7(2), 35-44. (In Persian)
Javadi, P., Asadi, H., & Vazifehdoust, M. (2022). Prediction of Spatial Variations of Soil Moisture Using Random Forest Method and Environmental Features derived from Satellite Images in Marghab Basin of Khuzestan. Iranian Journal of Soil and Water Research, 52(11), 2859-2874. (In Persian)
Jha, M. K., Gassman, P. W., & Arnold, J. G. (2007). Water quality modeling for the Raccoon River watershed using SWAT. Transactions of the ASABE, 50(2), 479-493.
Keshavarz, M. R., Vazifedoust, M., & Alizadeh, A. (2014). Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data. Agricultural Water Management, 132, 37-45.
Lehnert, M. (2014). Factors affecting soil temperature as limits of spatial interpretation and simulation of soil temperature. Acta Universitatis Palackianae Olomucensis–Geographica, 45(1), 5-21.
Li, J., Wang, S., Gunn, G., Joosse, P., & Russell, H. A. (2018). A model for downscaling SMOS soil moisture using Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 72, 109-121.
Li, H., Wolter, M., Wang, X., & Sodoudi, S. (2018). Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM. Theoretical and Applied Climatology, 134(1), 67-81.
Luo, W., Xu, X., Liu, W., Liu, M., Li, Z., Peng, T., & Zhang, R. (2019). UAV based soil moisture remote sensing in a karst mountainous catchment. Catena, 174, 478-489.
Maleki, K. H., Vaezi, A. R., & Sarmadian, F. (2019). Validation of satellite-based soil moisture retrievals from SMAP with in situ observation in the Simineh-Zarrineh (Bokan) Catchment, NW of Iran. Eurasian Journal of Soil Science, 8(4), 340-350.
Mehravar, S., Amani, M., Moghimi, A., Javan, F. D., Samadzadegan, F., Ghorbanian, A., & Mirmazloumi, S. M. (2021). Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine. Advances in Space Research, 68(11), 4573-4593.
Mishra, V., & Shah, H. L. (2018). Hydroclimatological perspective of the Kerala flood of 2018. Journal of the Geological Society of India, 92(5), 645-650.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900.
Mousavi, S., Sarmadian, F., Alijani, Z., & Taati, A. (2017). Land suitability evaluation for irrigating wheat by geopedological approach and geographic information system: A case study of Qazvin plain, Iran. Eurasian Journal of Soil Science, 6(3), 275-284.
Narasimhan, B., Srinivasan, R., Arnold, J. G., & Di Luzio, M. (2005). Estimation of long-term soil moisture using a distributed parameter hydrologic model and verification using remotely sensed data. Transactions of the ASAE, 48(3), 1101-1113.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., & Williams, J. R. (2004). Soil and Water Assessment Tool Input/Output File Documentation Version 2005, Grassland. Soil and Water Research Laboratory Agriculture Research Services & Black Land Research Center Texas Agricultural Experiment Station.
Niazi, Y., Talebi, A., Mokhtari, M. H., & Vazifedoust, M. (2018). Spatio-Temporal Analysis of the Accuracy of TRMM Satellite Data to Estimate the Severity of a Drought Based on Precipitation in Central Iran. Physical Geography Research Quarterly, 50(1), 69-85. (In Persian)
Nourinezhad, S., Rajabi, M. M., & Fathi, T. (2022). Sensitivity and uncertainty analysis of SWAT model in flow, sediment and phosphorus simulation for a mountainous watershed (Case study of Karaj river catchment). Water and Soil, 36(2), 167-183. (In Persian)
Rahimzadeh-Bajgiran, P., Berg, A. A., Champagne, C., & Omasa, K. (2013). Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS Journal of Photogrammetry and Remote Sensing, 83, 94-103.
Rouholahnejad, E., Abbaspour, K. C., Vejdani, M., Srinivasan, R., Schulin, R., & Lehmann, A. (2012). A parallelization framework for calibration of hydrological models. Environmental Modelling & Software, 31, 28-36.
Saleh, A., Arnold, J. G., Gassman, P. W. A., Hauck, L. M., Rosenthal, W. D., Williams, J. R., & McFarland, A. M. S. (2000). Application of SWAT for the upper North Bosque River watershed. Transactions of the ASAE, 43(5), 1077.
Salmani, H., MOHSENI, S. M., Rouhani, H., & Salajegheh, A. (2012). Evaluation of land use change and its impact on the hydrological process in the Ghazaghli Watershed, Golestan province.
Sayão, V. M., Demattê, J. A., Bedin, L. G., Nanni, M. R., & Rizzo, R. (2018). Satellite land surface temperature and reflectance related with soil attributes. Geoderma, 325, 125-140.
Schuol, J., Abbaspour, K. C., Yang, H., Srinivasan, R., & Zehnder, A. J. (2008). Modeling blue and green water availability in Africa. Water Resources Research, 44(7).
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., & Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3-4), 125-161.
Shafiei, K., Porhemmat, J., Sedghi, H., & Hosseni, M. (2018). Investigation the effect of land use changes on the quantity of water resources using remote sensing data and SWAT model (Case study: Maroon basin-southwest of Iran). Journal of Soil and Water Resources Conservation, 7(3), 71-87.
Shen, Z. Y., Chen, L., & Chen, T. (2012). Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China. Hydrology and Earth System Sciences, 16(1), 121-132.
Shivhare, N., Dikshit, P. K. S., & Dwivedi, S. B. (2018). A comparison of SWAT model calibration techniques for hydrological modeling in the Ganga river watershed. Engineering, 4(5), 643-652.
Sun, H., & Cornish, P. S. (2005). Estimating shallow groundwater recharge in the headwaters of the Liverpool Plains using SWAT. Hydrological Processes: An International Journal, 19(3), 795-807.
Sun, L., & Schulz, K. (2015). The improvement of land cover classification by thermal remote sensing. Remote Sensing, 7(7), 8368-8390.
Sun, X., Bernard‐Jannin, L., Garneau, C., Volk, M., Arnold, J. G., Srinivasan, R., & Sánchez‐Pérez, J. M. (2016). Improved simulation of river water and groundwater exchange in an alluvial plain using the SWAT model. Hydrological Processes, 30(2), 187-202.
Tóth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., & Tóth, G. (2015). New generation of hydraulic pedotransfer functions for Europe. European Journal of Soil Science, 66(1), 226-238.
Valayamkunnath, P., Sridhar, V., Zhao, W., & Allen, R. G. (2019). A comprehensive analysis of interpersonal and interannual energy and water balance dynamics in semiarid shrub land and forest ecosystems. Science of the Total Environment, 651, 381-398
Valinejad, F., Ghorbani, K., Zakerinia, M., Dehghani, A., & Ababaee, B. (2014). Performance Assessment of SWAT Model for Estimating Soil Moisture (Case Study: Nomal Watershed). Journal of Water and Sustainable Development, 1(1), 57-64. (In Persian)
Van Liew, M. W., Garbrecht, J. D., & Arnold, J. G. (2003). Simulation of the impacts of flood retarding structures on stream flow for a watershed in southwestern Oklahoma under dry, average, and wet climatic conditions. Journal of Soil and Water Conservation, 58(6), 340-348.
Wang, J., Ding, J., Yu, D., Teng, D., He, B., Chen, X. ... & Su, F. (2020). Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI. Science of the Total Environment, 707, 136092.
Wang, X., & Melesse, A. M. (2005). Evaluation of the SWAT model’s snowmelt hydrology in a northwestern Minnesota watershed. Transactions of the ASAE, 48(4), 1359-1376.
Xie, H., & Lian, Y. (2013). Uncertainty-based evaluation and comparison of SWAT and HSPF applications to the Illinois River Basin. Journal of Hydrology, 481, 119-131.
Zinck, J. A., Metternicht, G., Bocco, G., & Del Valle, H. F. (Eds.). (2015). Geopedology: An integration of geomorphology and pedology for soil and landscape studies. Springer
 
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دوره 53، شماره 10
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صفحه 2365-2382

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