Use of a calibrated SWAT model to evaluate the effects of agricultural BMPs on sediments of the Kalaya river basin (North of Morocco)

Briak, Hamza; Mrabet, Rachid; Moussadek, Rachid; Aboumaria, Khadija

doi:10.1016/j.iswcr.2019.02.002

Cited by 72 publications

(29 citation statements)

References 60 publications

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“…Combinations of BMPs achieved better results than individual BMPs (Table 6). Similar studies, using the SWAT model, in different regions of the world corroborate these results [37,[44][45][46]. Combination 1 involved testing the percent of reduction by applying the structural BMPs.…”

Section: Combination Bmpssupporting

confidence: 60%

Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain)

et al. 2019

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Best management practices (BMPs) provide a feasible solution for non-point source pollution problems. High sediment and nutrient yields without retention control result in environmental deterioration of surrounding areas. In the present study, the soil and water assessment tool (SWAT) model was developed for El Beal watershed, an anthropogenic and ungauged basin located in the southeast of Spain that drains into a coastal lagoon of high environmental value. The effectiveness of five BMPs (contour planting, filter strips, reforestation, fertilizer application and check dam restoration) was quantified, both individually and in combination, to test their impact on sediment and nutrient reduction. For calibration and validation processes, actual evapotranspiration (AET) data obtained from a remote sensing dataset called Global Land Evaporation Amsterdam Model (GLEAM) were used. The SWAT model achieved good performance in the calibration period, with statistical values of 0.78 for Kling–Gupta efficiency (KGE), 0.81 for coefficient of determination (R2), 0.58 for Nash–Sutcliffe efficiency (NSE) and 3.9% for percent bias (PBIAS), as well as in the validation period (KGE = 0.67, R2 = 0.83, NS = 0.53 and PBIAS = −25.3%). The results show that check dam restoration is the most effective BMP with a reduction of 90% in sediment yield (S), 15% in total nitrogen (TN) and 22% in total phosphorus (TP) at the watershed scale, followed by reforestation (S = 27%, TN = 16% and TP = 20%). All effectiveness values improved when BMPs were assessed in combination. The outcome of this study could provide guidance for decision makers in developing possible solutions for environmental problems in a coastal lagoon.

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Section: Combination Bmpssupporting

confidence: 60%

Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain)

et al. 2019

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“…We will use these results in the same region by integrating them into an agro-hydrological modelling system in order to estimate and predict the impact of climate scenarios on water balance, sediment yield and agricultural practices ... etc. Moreover, we will apply the same approach for the same objective of a previous research that was carried out in the Northern Morocco watersheds ( Briak et al., 2016(b) ; Briak et al., 2019 ; Ouallali et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software

Beroho

Briak

Halimi

et al. 2020

Heliyon

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For many years, the application of mixed-effects modeling has received much attention for predicting scenarios in the fields of theoretical and applied sciences. In this study, a “new” Multilevel Linear Mixed-Effects (LME) model is proposed to analyze and predict multiply-nested and hierarchical data. Temperature and rainfall observation were carried out successively between 1979-2014 and 1984–2018; and the data input was organized on monthly basis for each year. Besides, a daily observation was made for “Dar Chaoui” zone of Northern Morocco. However, we chose in the first time a simple linear regression model, but the estimation has been just for fixed effects and ignoring the random effect. On the other hand, in multilevel linear mixed effects models, once the model has been formulated, methods are needed to estimate the model parameters. In this section, we first deal with the joint estimation of the fixed effects (β), random effects (ui) and then with estimation of the variance parameters (γ, ρ and σ 2 ). The study revealed that the predicted values are very close to the real value. Besides, this model is capable of modelling the error, fixed and random parts of the sample. Moreover, in this range, the results showed that there is three standard deviations measures for fixed and random effects, also the variance measure, which demonstrate us a great prediction. In conclusion, this model gives a decisive precision of results that can be exploited in studies for forecast of water balance and/or soil erosion. These results can also be used to inhibit the risk of erosion with possible arrangements for the environment and human security.

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“…soil water erosion). More recently, several strategies have been suggested to control soil erosion in agricultural lands, such as soil mulching ( Cerdà et al., 2017 ), agri-spillways ( Rodrigo-Comino et al., 2017a ), catch crops ( Kort et al., 1998 ), and soil terracing ( Liu et al., 2013 ; Briak et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing the WEPP model performance for predicting daily runoff in three terrestrial ecosystems in western Syria

Mohammed

Hussien

Alsafadi

et al. 2021

Heliyon

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Soil erosion is one of the main threats facing the agriculture and natural resources sector all over the world, and the same is true for Syria. Several empirical and physically based tools have been proposed to assess erosion induced soil losses and runoff driving the processes, from plot to regional spatial scales. The main goal of this research is to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting runoff in comparison with field experiments in the Al-Sabahia region of Western Syria in three ecosystems: agricultural lands (AG), burned forest (BF) and forest (FO). To achieve this, field experimental plots (2∗1.65∗0.5 m) were prepared to obtain runoff observation data between September 2012 and December 2013. In addition, the input data (atmospheric forcing, soil, slope, land management) were prepared to run the WEPP model to estimate the runoff. The results indicate that the average observed runoffs in the AG, BF and FO were 12.54 ± 1.17, 4.81 ± 0.97 and 1.72 ± 0.16 mm/event, respectively, while the simulated runoffs in the AG, BF and FO were 15.15 ± 0.89, 9.23 ± 1.48 and 2.61 ± 0.47mm/event, respectively. The statistical evaluation of the model's performance showed an unsatisfactory performance of the WEPP model for predicting the run-offs in the study area. This may be caused by the structural flaws in the model, and/or the insufficient site-specific input parameters. So, to achieve good performance and reliable results of the WEPP model, more observation data is required from different ecosystems in Syria. These findings can provide guidance to planners and environmental engineers for proposing environmental protection and water resources management plans in the Coastal Region in Syria.

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Use of a calibrated SWAT model to evaluate the effects of agricultural BMPs on sediments of the Kalaya river basin (North of Morocco)

Cited by 72 publications

References 60 publications

Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain)

Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain)

Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software

Assessing the WEPP model performance for predicting daily runoff in three terrestrial ecosystems in western Syria

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