The Interactive Impact of Land Cover and DEM Resolution on the Accuracy of Computed Streamflow Using the SWAT Model

Al-Khafaji, Mahmoud Saleh; Saeed, Fouad H.; Al‐Ansari, Nadhir

doi:10.1007/s11270-020-04770-0

Cited by 18 publications

(7 citation statements)

References 26 publications

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“…SWAT is a semi-distributed, physically based hydrological model operated in QGIS through the QSWAT extension. SWAT is a globally employed and efficient tool for quantifying various hydrological components, such as surface runoff, sediments, and pollution (Al-Khafaji et al 2020). The flowchart in Fig.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

Senent-Aparicio,

Jimeno-Sáez,

Martínez-España

et al. 2023

Water Resour Manage

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Streamflow prediction in ungauged basins (PUB) is necessary for effective water resource management, flood assessment, and hydraulic engineering design. Spain is one of the countries in Europe expected to suffer the most from the consequences of climate change, notably an increase in flooding. The authors selected the Miño River basin in the northwest of Spain, which covers an area of 2,168 km 2 , to develop a novel approach for predicting streamflow in ungauged basins. This study presents a regionalisation of the soil and water assessment tool (SWAT), a semi-distributed, physically based hydrological model. The regionalisation approach transfers SWAT model parameters based on hydrological similarities between gauged and ungauged subbasins. The authors used k-means and expectation−maximisation (EM) machine learning clustering techniques to group 30 subbasins (9 gauged subbasins) into homogeneous, physical, similarity-based clusters. Furthermore, the regionalisation featured physiographic attributes (basin area, elevation, and channel length and slope) and climatic information (precipitation and temperature) for each subbasin. For each homogeneous group, the SWAT model was calibrated and validated for the gauged basins (donor basins), and the calibrated parameters were transferred to the pseudo-ungauged basins (receptor basins) for streamflow prediction. The results of the streamflow prediction in the pseudo-ungauged basins demonstrate satisfactory performance in most of the cases, with average NSE, R 2 , RSR, and RMSE values of 0.78, 0.91, 0.42, and 5.10 m 3 /s, respectively. The results contribute to water planning and management and flood estimation in the studied region and similar areas.

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Section: Hydrological Modellingmentioning

confidence: 99%

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

Senent-Aparicio,

Jimeno-Sáez,

Martínez-España

et al. 2023

Water Resour Manage

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“…The ARB has an area of 11600 km 2 , one of Tigris River's tributaries. The ARB Figure 1 is located between the longitudes 43° 41′ 9″ to 45° 27′ 31″ E and latitudes 35° 42′ 24″ to 34° 33′ 8″ N in northern Iraq [33]. The river originates from highlands of 1800 meters above sea level (m.a.s.l.)…”

Section: Study Areamentioning

confidence: 99%

“…The Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs) of 90 m spatial resolution were used in SWAT for watershed delineation and determination of stream locations (downloaded from; http://glcf.umd.edu/data/srtm/, access date 30/4/2021). The Moderate Resolution Imaging Spectroradiometer (MODIS) land cover/use of 500 m spatial resolution (downloaded from; http://gdex.cr.usgs.gov/gdex/, access date 10/4/2021) and Food and Agriculture Organization (FAO) soil data of 1:500000 spatial scale (downloaded from; https://www.fao.org, access date 22/4/2021) were used in defining Hydrologic Response Units in SWAT [33]. Figure 2 shows the spatial data used in the SWAT model.…”

Section: Datamentioning

confidence: 99%

Potential Impact of Global Warming on Climate and Streamflow of Adhaim River Basin, Iraq

Saeed¹,

Al-Khafaji²,

Al-Faraj³

2022

ETJ

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 Connection between LARS-WG and SWAT models presented successful simulation for current and future climate and hydrological systems in ARB.  ARB tend to become hotter and drier by 2080 due to global warming.  Adaptation strategies should be applied in water resources management of ARB.Global warming induces to increase of greenhouse gases in the atmosphere and plays a crucial role in determining the future trend in climatology and hydrology of a watershed. This paper aims to investigate the implications of global warming on future climate and its consequents on streamflow of the Adhaim River Basin (ARB). For this purpose, the Long Ashton Research Station-Weather Generator (LARS-WG) and Soil and Water Assessment Tool (SWAT)-Based models were implemented. The climate and hydrologic records for the period 1990-2019 were used as a Reference Period (RP) and projected to 2080 under Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and five Global Climate Models (GCMs). The results show that the region of ARB tends to become hotter and drier with an increase in mean temperature by 1.2, 2.9, and 4.6 °C under the considered RCPs, respectively. However, precipitation tends to decrease from 366 mm/y in RP to 320.2, 302, and 300.5 mm/y by 2080 under the considered RCPs. Consequently, the streamflow will decrease to about 28, 26, and 24 m3/s by 2080 under the considered RCPs, respectively, compared with 28.96 m3/s in RP. Therefore, adaptation strategies are highly recommended to alleviate the negative impacts of climate change, and the implications of climate change on groundwater, water demand, and adaptation plans should be investigated in future studies.

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“…In recent years, the sensitivity of the SWAT model to spatial input data has attracted the attention of researchers [6,8,[11][12][13]. The previous studies show somewhat contradicting results, and there is no clear pattern indicating that high-resolution and local data outperform the low-resolution data.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, Geza and McCray [14] evaluated the performance of SWAT with two U.S. soils (i.e., high-resolution SSURGO and low-resolution STATSGO soils) and reported better performance of the model with SSURGO soil. Al-Khafaji et al [12] investigated the effect of DEM and land use data quality on the accuracy of SWAT model predictions and reported that high-resolution datasets did not provide better predictive reliability. Similar results were obtained by Asante et al [11], who evaluated the impact of land use data quality on the predictive capacity SWAT model and indicated slightly better performance of low-resolution land use data.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Spatial Input Data Quality on the Performance of the SWAT Model

Kmoch

Moges

Sepehrar

et al. 2022

Water

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Soil and land use information are important inputs for physically-based hydrological modeling such as SWAT. Although fine resolution local or regional data are often preferred for modeling, it is not always reliable that these data can lead to better model performance. In this study, we investigate the effect of input data on the sensitivity and uncertainty of the SWAT model in the Porijõgi catchment in Estonia. We created four model setups using global/regional level data (HWSD soil and CORINE) and local high-resolution spatial data, including the Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). We employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. The results illustrated that models with high-resolution local soil data performed lower than models with global soil data, but in contrast, in the case of land use datasets, the local high-resolution ETAK dataset improved performance over the CORINE data.

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The Interactive Impact of Land Cover and DEM Resolution on the Accuracy of Computed Streamflow Using the SWAT Model

Cited by 18 publications

References 26 publications

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

Potential Impact of Global Warming on Climate and Streamflow of Adhaim River Basin, Iraq

The Effect of Spatial Input Data Quality on the Performance of the SWAT Model

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