Hanane Mebarki; Noureddine Maref; Mohammed El-Amine Dris

doi:10.26599/JGSE.2024.9280013

doi: 10.26599/JGSE.2024.9280013

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出版历程
- 收稿日期: 2023-12-16
- 录用日期: 2024-04-17
- 网络出版日期: 2024-06-10
- 刊出日期: 2024-06-30

Modelling the monthly hydrological balance using Soil and Water Assessment Tool (SWAT) model: A case study of the Wadi Mina upstream watershed

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Civil Engineering and Environmental Laboratory (LGCE), University of Djillali Liabès, Faculty of Technology, Hydraulics department, Ben M'Hidi district, P.O.Box 89, 22000, Sidi Bel Abbès, Algeria

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Corresponding author: m.noure@yahoo.fr

摘要

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Abstract: Modelling the hydrological balance in semi-arid zones is essential for effective water resource management, encompassing both surface water and groundwater. This study aims to model the monthly hydrological water cycle in the Wadi Mina upstream watershed (northwest Algeria) by applying the Soil and Water Assessment Tool (SWAT) hydrological model. SWAT modelling integrates spatial data such as the Digital Elevation Model (DEM), land use, soil types and various meteorological parameters including precipitation, maximum and minimum temperatures, relative humidity, solar radiation and wind speed. The SWAT model was calibrated and validated using data from January 2012 to December 2014, with a calibration period from January 2012 to August 2013 and a validation period from September 2013 to December 2014. Sensitivity and parameter calibration were conducted using the SWAT-SA program, and model performance evaluation relied on comparing the observed discharge at the outlet of the basin with model-simulated discharge, assessed through statistical coefficients including Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R²) and Percent Bias (PBAIS). Calibration results indicated favourable objective function values (NSE=0.79, R²=0.93, PBAIS= −8.53%), although a slight decrease was observed during validation (NSE=0.69, R²=0.86, and PBAIS= −11.41%). The application of the SWAT model to the Wadi Mina upstream watershed highlighted its utility in simulating the spatial distribution of different components of the hydrological balance in this basin. The SWAT model revealed that approximately 71% of the precipitation in the basin evaporates, while only 29% contributes to surface runoff or infiltration into the soil.
- SWAT model /
- Performance /
- Parameters /
- Runoff /
- Groundwater /
- Wadi

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Figure 1. Position map and DEM of the Wadi Mina upstream Basin

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Figure 2. Average monthly variation of rainfall and runoff in the Wadi Mina upstream

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Figure 3. Average monthly variation of rainfall and humidity in the Wadi Mina upstream basin

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Figure 4. Land use map of the Wadi Mina upstream

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Figure 5. Soil map of the Wadi Mina upstream

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Figure 6. Subdivision of sub-basins and HRUs in the Wadi Mina upstream basin

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Figure 7. Simulation steps using the SWAT model

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Figure 8. Comparison between observed and simulated monthly discharge for the calibration (01/2012 to 08/2013) and validation (09/2013 to 12/2014) periods

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Figure 9. Spatial distribution of monthly rainfall in the Wadi Mina upstream

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Figure 10. Spatial distribution of monthly evapotranspiration in the Wadi Mina upstream

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Figure 11. Spatial distribution of monthly runoff in the Wadi Mina upstream

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Figure 12. Spatial distribution of monthly aquifer recharge in the Wadi Mina upstream

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Figure 13. Schematization of the annual hydrological balance in the Wadi Mina upstream basin

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Table 1. Input data for the SWAT model

Data	Sources	Description
Digital Elevation Model (DEM)	USGS website (https://earthexplorer.usgs.gov/) in Shuttle Radar Topography Mission(SRTM)	30 m×30 m resolution
Land use map	https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2	Land use classification (1/400,000 scale)
Soil map	FAO–UNESCO Soil Map of the Word (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/en/)	Soil classification and physical properties (1/300,000 scale)
Weather data	Hydro-meteorological database of the National Water Resources Agency (ANRH) and National Metrology Office (ONM)	Daily data of precipitation, temperatures (max and min), humidity, solar radiation and wind speed for the period 2012 to 2014
Hydrometric data	National Water Resources Agency	Monthly flows observed during the period 2012 to 2014

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Table 2. Parameters chosen for modeling the different hydrological processes by SWAT

Parameters	Description	Hydrological processes	Min	Max
CN2	Initial SCS runoff curve number for humidity conditions	Runoff	35	98
SURLAG	Surface runoff lag time	Runoff	0.05	24
ESCO	Soil evaporation compensation factor	Potential and effective Evapotranspiration	0	1
EPCO	Plant uptake compensation factor		0	1
CANMX	Maximum storage of plant cover (mm H₂O)		0	100
SOL_AWC	Available water capacity of the soil layer (mm H₂O/mm soil)	Soil water	0	1
SOL_K	Saturated hydraulic conductivity of the soil layer (mm/h)		0	2,000
SOL_BD	Moist bulk density ((Mg/m³ or g/cm³)
GW_REVAP	Groundwater evapotranspiration coefficient	Groundwater/Baseflow	0.02	0.2
GW_DELAY	Groundwater delaytime (days)		0	500
REVAPMN	Threshold depth of water in the shallow aquifer for evapotranspiration or percolation to the deep aquifer to occur (mm H₂O)		0	500
GWQMN	Threshold depth of water in the shallow aquifer for return flow to occur (mm H₂O)		0	5,000
ALPHA_BF	Base flow alpha factor (days)		0	1
RCHRG_DP	Deep aquifer percolation fraction
CH_K2	Effective hydraulic conductivity inmain channel alluvium (mm/h)	Conveying water in canals	−0.01	500
CH_N2	Manning's "n" value for the main channel		−0.01	0.3
ALPHA_BNK	Base flow alpha factor for bank storage (days)		0	1
SLSUBBSN	Average slope length	Concentration time	10	150
OV_N	Manning's "n" value for overland flow		0.01	30
CH_N1	Manning's "n" value for tributary channels		0.01	30
CH_K1	Effective hydraulic conductivity in alluvium of tributary channels (mm/h)	Transmission losses due to surface runoff	0	300
HRU_SLP	Average slope steepness (m/m)	Lateral flow	0	1

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Table 3. Recommended values of the three objective functions used to evaluate the performance of SWAT model

Objective functions	NSE	R²	PBAIS/%
Insufficient	NSE ≤ 0.5	R²≤0.5	PBAIS ≥ ±25
Satisfying	0.5<NSE ≤0.65	0.5 <R²≤ 0.7	±15≤PBAIS< ±25
Good	0.65 <NSE≤0.75	0.7<R²≤ 0.8	±10 ≤PBAIS< ±15
Very good	0.75 <NSE≤1	R²> 0.8	PBAIS< ±10

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Table 4. Ranking of the most sensitive parameters

Parameters	Sensitivity rank	Value obtained	t-stat	p-value
CH_K2	1	166.66	19.52	8.49E-56
SOL_BD	2	1.97	12.14	5.23E-28
REVAPMN	3	300	10.88	1.51E-23
OV_N	4	10.01	−9.76	8.35E-20
CH_K1	5	100	−9.70	1.34E-19
CH_N2	6	0.09	−6.84	4.19E-11
SOL_AWC	7	0.67	−4.95	1.19E-06
RCHRG_DP	8	0.33	4.92	1.36E-06
SURLAG	9	8.03	4.48	1.03E-05
CANMX	10	78	−4.38	1.57E-05
SLSUBBSN	11	58.97	−3.34	9.39E-04
ESCO	12	0.43	2.84	4.70E-03
GW_DELAY	13	333.33	−2.52	1.20E-02
ALPHA_BNK	14	0.6	−2.34	1.97E-02
GW_REVAP	15	0.08	−2.27	2.37E-02
ALPHA_BF	16	0.4	2.04	4.14E-02

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