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Spatially distributed model DHSVM for analyzing soil and vegetation attributes impacts on hydrological responses, case study: Chehelgazi Watershed of Sanandaj | ||
| مهندسی و مدیریت آبخیز | ||
| Article 21, Volume 11, Issue 4, January 2020, Pages 1086-1100 PDF (780.01 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwmse.2018.120730.1446 | ||
| Authors | ||
| Salahudin Zahedi* 1; Kaka Shahedi2; Mahmod Habibnejad Rawshan3; Karim Solimani4; Kourosh Dadkhah5 | ||
| 1Assistant Professor, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran | ||
| 2Associate Professor, Faculty of Watershed Management, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran | ||
| 3Professor, Faculty of Watershed Management, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran | ||
| 4Professor, Kurdistan agricultural and natural Resources research and Education Center, AREEO, Sanandaj, Iran | ||
| 5Assistant Professor, Faculty of Sciences, University of Kurdistan, Sanandaj, Iran | ||
| Abstract | ||
| This study was conducted to evaluate the capability of the DHSVM model to simulate hydrological processes in a mountainous watershed with a minimum agricultural land use. The required climatic parameters were set for the daily time step. The inverse distance method was used to interpolate the climatic variables from the stations to the network cells. Time inputs to the model were prepared for the years 2008 to 2013. The land cover map was prepared using the supervised classification method of Landsat TM data. The stream network map was generated using the DEM map in ArcGIS software. The soil texture map was prepared using field sampling and in the laboratory. Primary tests to the determine sensitivity of input parameters showed that this model is sensitive to lateral hydraulic conductivity, exponential coefficient of hydraulic conductivity, porosity, field capacity, and minimum stomatal resistance. In this study, except for the lateral hydraulic conductivity and exponential decrease coefficient, all other parameters (soil and vegetation) were determined based on previous studies and field measurements in a similar way for both categories of data. The time series of the data were divided into three, warm- up or preparation periods (2008-2009), calibration (2009-2011) and validation (2011-2013). The model was calibrated using streamflow data from 2008 to 2010. Different efficiency criteria were calculated between simulated and observed flows. NSE value for calibrating was 0.59 and for validation was 0.606. In general, the results of this model implementation in the studied basin with the quality and quantity of input data to the model are satisfactory. | ||
| Keywords | ||
| DEM; Inverse distance method; Precipitation; Runoff simulation; Vegetation cover | ||
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