Modeling Catchment-Scale Nitrogen Losses Across a Land-Use Gradient in the Subtropics

Hajati, Mithra‐Christin; White, Shane; Moosdorf, Nils; Santos, Isaac R.

doi:10.3389/feart.2020.00347

Cited by 9 publications

(8 citation statements)

References 87 publications

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“…Moreover, the selected period for calibration and validation process was due to lack of in-situ data spanning the model duration. During calibration (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003), four iterations each with no less than 500 simulations were executed to refine the model parameters (Abbaspour, 2015;Hajati et al, 2020). With each iteration performed, values of each parameter range become smaller approaching the best solution and achieving better models than previous iterations.…”

Section: Calibration Validation and Uncertainty Analysismentioning

confidence: 99%

“…Where Q is the variable under observation or simulation, that is discharge, and are discharge data, which have been measured and simulated respectively, while n refers to the number of total records and ̅ denotes the average measured data while i is the ith measured or simulated data. Similar to the P-factor and R-factor, there are no specific numbers to achieve, however, the recommended values for watershed scale (Gupta et al, 2009;Hajati et al, 2020;Moriasi et al, 2007Moriasi et al, , 2015 are as follows;…”

Section: ∑ (mentioning

confidence: 99%

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Evaluating SWAT model for streamflow estimation in the semi-arid Okavango-Omatako catchment, Namibia

Negussie¹,

Wyss²,

Knox³

et al. 2022

Afr. J. Environ. Sci. Technol.

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A semi-distributed hydrological model was used for runoff estimation in the Okavango-Omatako catchment in Namibia. The model was configured for a 31-year period from 1985 to 2015. Subsequently, calibration and validation processes followed using the SUFI-2 algorithm. For evaluating catchment simulation, two methods were used: i. model prediction uncertainty measured by P-factor and R-factor and ii. Model performance indicators, that is, Nash-Sutcliffe Efficiency (NSE), Coefficient of determination (R 2 ), Percent bias (PBIAS), and Residual variation (RSR). The P-factor achieved 0.77 and 0.68 while R-factor attained 1.31 and 1.82 for calibration and validation, respectively. The following indicators were used to evaluate the model performance through calibration and validation results respectively; NSE with 0.82 and 0.80, R 2 with 0.84 and 0.89, PBIAS achieving -20≤PBIAS≤-1.1 and RSR performing 0.42 and 0.44. All performance indices achieved very good ratings apart from PBIAS validation which rated as satisfactory. The semi-arid characteristics together with relatively flat terrain features justified the need for the evaluation of model performance using discharge data in our study region. SWAT demonstrated reasonable results in modelling semi-arid streamflow with high and low flows adequately captured. Consequently, this evaluation was necessary for further investigations into impacts of climate change on scarce water resources highlighting the challenges of SWAT model applications in our study area climatic regime and other similar regions globally for further model improvements.

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Section: Calibration Validation and Uncertainty Analysismentioning

confidence: 99%

Section: ∑ (mentioning

confidence: 99%

Evaluating SWAT model for streamflow estimation in the semi-arid Okavango-Omatako catchment, Namibia

Negussie¹,

Wyss²,

Knox³

et al. 2022

Afr. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

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“…Agricultural fertilization and urban wastewater discharge directly expose Nr to the environment 26 ; climate and topography contribute to Nr loss by driving Nr transport 11 ; changes in land use composition and structure can also affect Nr loss by altering surface nutrient content and nutrient transport 27 . Research has shown that the Nr loss has increased 3.5-fold due to agricultural intensification 28 , and increased impervious surfaces have also exacerbated Nr losses 29 . Moreover, the shift from forest to cleared land increases the denitrification capacity 28 ; and increasing the patch edges of grassland can reduce Nr pollution 30 .…”

Section: Introductionmentioning

confidence: 99%

“…Research has shown that the Nr loss has increased 3.5-fold due to agricultural intensification 28 , and increased impervious surfaces have also exacerbated Nr losses 29 . Moreover, the shift from forest to cleared land increases the denitrification capacity 28 ; and increasing the patch edges of grassland can reduce Nr pollution 30 . However, traditional research tools and methods such as multiple linear regression, correlation analysis, ordinary least squares models, spatial lag models, and spatial error models are challenging to deal with drivers' spatial variability.…”

Section: Introductionmentioning

confidence: 99%

Attribution and driving force of nitrogen losses from the Taihu Lake Basin by the InVEST and GeoDetector models

Tian

Zhang

et al. 2023

Sci Rep

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Quantifying temporal and spatial changes in reactive nitrogen (Nr) losses from a watershed and exploring its main drivers are the key to watershed water quality improvements. Huge Nr losses continue to threaten the safety of the water environment in the Taihu Lake Basin (TLB). Here, the InVEST and GeoDetector models were combined to estimate Nr losses in the TLB from 1990 to 2020 and explore driving forces. Different scenarios for Nr losses were compared, showing that Nr loss peaked at 181.66 × 103 t in 2000. The key factors affecting Nr loss are land use, followed by elevation, soil, and slope factors, and their mean q-values were 0.82, 0.52, 0.51, and 0.48, respectively. The scenario analysis revealed that Nr losses increased under the business-as-usual and economic development scenarios, while ecological conservation, increased nutrient use efficiency, and reduced nutrient application all contribute to a reduction in Nr losses. The findings provide a scientific reference for Nr loss control and future planning in the TLB.

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“…In addition to its performance and functionality described in the preceding paragraphs, the SWAT model was used mainly because it has a multidimensional methodology to assess the catchment water yield, baseflow, and surface runoff [40]. To evaluate the LULC effect on hydrological flow, the SWAT model has better performance during a simple water balance approach [41,42]. In semi-arid climate areas, the curve number (CN) simulating method of the SWAT model performed well in estimating surface runoff at high precipitation intensity, although the model underestimated surface runoff [43].…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Change Modulates Hydrological Flows and Water Supply to Gaborone Dam Catchment, Botswana

Arsiso,

Mengistu Tsidu

2023

Water

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Identifying the mechanism through which changes in land use and land cover (LULC) modulate hydrological flows is vital for water resource planning and management. To examine the impact of LULC change on the hydrology of the Gaborone Dam catchment within the upper Limpopo basin, where Notwane river is the major river within the catchment, three LULC maps for the years 1997, 2008, and 2017 were established based on a mosaic of Landsat 5 for 1997 and 2008 and Landsat 8 for 2017. The 10 m-resolution Version 200 ESA World Land Cover Map for 2021 is used as a ground truth to train the random forest (RF) classifier to identify land cover classes from Landsat 8 imageries of 2021 using the Google Earth Engine (GEE) Python API. The overall accuracy/kappa coefficient of the RF classifier is 0.99/0.99 for the training and 0.73/0.68 for the validation data sets, which indicate excellent and substantial agreements with the ground truth, respectively. With this confidence in the LULC classification, the impact of LULC change on the hydrological flow within the catchment was estimated by employing the Soil and Water Assessment Tool (SWAT) and indicator of hydrological alteration (IHA). The SWAT model calibration and validation were first performed, and the ability of the model to capture the observed stream flow was found to be good. The LULC maps from Landsat images during the 1997–2017 period show a decrease in forests and shrubland in contrast to an increase in pasture land. The expansion of pasture and cropland and the reduction in forests and shrubland led to a decline in the amount of evapotranspiration and groundwater recharge. Furthermore, the LULC change also caused a reduction in low flow during dry periods and an increase in high flow during the rainy season. The findings clearly demonstrate that LULC changes can affect the water table by altering soil water recharge capacity. The study highlighted the importance of LULC for catchment water resource management through land use planning to regulate the water level in the Gaborone Dam against the impact of climate change and growing water demands by the city of Gaborone due to population growth.

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Modeling Catchment-Scale Nitrogen Losses Across a Land-Use Gradient in the Subtropics

Cited by 9 publications

References 87 publications

Evaluating SWAT model for streamflow estimation in the semi-arid Okavango-Omatako catchment, Namibia

Evaluating SWAT model for streamflow estimation in the semi-arid Okavango-Omatako catchment, Namibia

Attribution and driving force of nitrogen losses from the Taihu Lake Basin by the InVEST and GeoDetector models

Land Use and Land Cover Change Modulates Hydrological Flows and Water Supply to Gaborone Dam Catchment, Botswana

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