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Determination of water balance equation components in irrigated agricultural watersheds using SWAT and MODFLOW models : A case study of Samalqan plain in Iran

Nasiri Shima Ansari Hossein Ziaei Ali Naghi

Nasiri S, Ansari H, Ziaei AN. 2022. Determination of water balance equation components in irrigated agricultural watersheds using SWAT and MODFLOW models : A case study of Samalqan plain in Iran. Journal of Groundwater Science and Engineering, 10(1): 44-56 doi:  10.19637/j.cnki.2305-7068.2022.01.005
Citation: Nasiri S, Ansari H, Ziaei AN. 2022. Determination of water balance equation components in irrigated agricultural watersheds using SWAT and MODFLOW models : A case study of Samalqan plain in Iran. Journal of Groundwater Science and Engineering, 10(1): 44-56 doi:  10.19637/j.cnki.2305-7068.2022.01.005

doi: 10.19637/j.cnki.2305-7068.2022.01.005

Determination of water balance equation components in irrigated agricultural watersheds using SWAT and MODFLOW models : A case study of Samalqan plain in Iran

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  • Figure  1.  Location of the study area

    Figure  2.  Geological characteristic and soil units in Samalqan watershed

    Figure  3.  Land use map of Samalqan watershed

    Figure  4.  Location of wells in Samalqan aquifer

    Figure  5.  Flowchart of computation of combined SWAT and MODFLOW models

    Figure  6.  Plots of observed and simulated mean monthly streamflow during the Calibration (2004-2012) and validation (2013-2014) periods for a) Darband b) Shirababd and c) Darkesh hydrometric stations

    Figure  7.  Mean annual recharge estimated by SWAT

    Figure  8.  Distribution of hydraulic conductivity and specific yield in Samalqan aquifer

    Figure  9.  Groundwater level contour lines for Samalqan aquifer

    Figure  10.  Plots of observed and computed groundwater level

    Figure  11.  The relation between surface water and groundwater level in Samalqan watershed

    Figure  12.  Groundwater-surface water interaction in MODFLOW cells

    Figure  13.  Comparison of the groundwater balance in simulation period

    Table  1.   General geological characteristics and soil units in the study area

    Permeability based on geological characteristicSoil depth based on geological characteristicGeological characteristicLand useSoil textureUnit
    high high Antelopes, young conifers, alluvial plains, young alluvial river Pasture Moderate 3 001
    Moderate Moderate Conglomerate with poor consolidation Forest Moderate 3 002
    Low Low Thick layer limestone, chert limestone, clayey limestone and marl Forest Moderate 3 003
    Low Low Shale Orchard-agriculture Moderate 3 004
    Low Low Thick layer limestone, chert limestone, clayey limestone and marl Bare Ground Tundra Moderate 3 005
    high high Antelopes, young conifers, alluvial plains, young alluvial river Orchard-agriculture Moderate 3 007
    Low Low Red marl and sandstone with layers of conglomerate Pasture Moderate 3 008
    Moderate high Antelopes, old cones, alluvial plains Orchard-agriculture Moderate 3 009
    Low Low Antelopes, old cones, alluvial plains Orchard-agriculture Moderate 3 010
    Low Moderate to high Orbital insoluble limestone Pasture Moderate 3 011
    Low Moderate to high Orbital insoluble limestone Forest Moderate 3 012
    Low Moderate to high Orbital insoluble limestone Orchard-agriculture Moderate 3 013
    Low Low Clay limestone, marl, sandstone and conglomerate, coarse sandstone and conglomerate Orchard-agriculture Moderate to strong 3 014
    Moderate Moderate Conglomerate with poor consolidation Pasture Moderate 3 016
    high high Antelopes, young conifers, alluvial plains, young alluvial river Pasture Moderate 3 017
    high high Antelopes, young conifers, alluvial plains, young alluvial river Orchard-agriculture Moderate 3 018
    Low Moderate to high Orbital insoluble limestone Forest Moderate 3 019
    下载: 导出CSV

    Table  2.   Model evaluation statistics, calibration - validation periods

    Coefficients
    Station NameCalibrated period (2004-2012)Validation Period (2013-2014)
    P-factor R-factor R2 NSE PBIAS PSR P-factor R-factor R2 NSE PSR PBIAS
    Darband
    Shirabad
    Darkesh
    0.82
    0.75
    0.72
    0.90
    0.78
    0.76
    0.92
    0.85
    0.82
    0.85
    0.80
    0.75
    −3.0
    2.5
    3.8
    0.58
    0.50
    0.48
    0.80
    0.78
    0.75
    0.87
    0.75
    0.70
    0.85
    0.80
    0.76
    0.80
    0.78
    0.72
    0.55
    0.52
    0.46
    −2.8
    1.5
    2.6
    下载: 导出CSV

    Table  3.   Average annual surface water balance components calculated by the SWAT model

    Surface water balance component(mm)Calibrated period(2004-2012)Validation Period(2013-2014)
    Precipitation; Precip
    Potential evapotranspiration; PET
    486.5
    1 359.0
    468.3
    1 377.8
    Actual evapotranspiration; ET
    Water yield; WYLD
    Surface runoff; Sur_Q
    Soil water; SW
    Lateral flow; Lat_Q
    Contribution of groundwater to stream flow; Gw_Q
    Percolation out of soil
    420.5
    43.7
    1.2
    61.5
    20.2
    18.5
    45.0
    429.0
    46.5
    2.5
    45.5
    14.4
    15.8
    26.5
    下载: 导出CSV

    Table  4.   Groundwater balance components

    ComponentsIn-flow (Mm3/a)Out-flow (Mm3/a)
    Inflow boundaries 20.5
    Infiltration of river bed and sewage well 5.8
    Infiltration of Surface water(Precipitation, irrigation return flow) 11.64
    Outflow boundaries 0.68
    Discharge and extraction (well, spring) 46.4
    Total 37.94 47.08
    Storage −9.14
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-05
  • 录用日期:  2021-10-18
  • 网络出版日期:  2022-03-24
  • 刊出日期:  2022-03-15

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