Evaluation of spatial variability on hydrology and nutrient source loads at watershed scale using a modeling approach

Parajuli, Prem B.

doi:10.2166/nh.2012.013

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…For the statistical analysis of the model performances, this study utilized two commonly used methods (Parajuli, 2012;Kim and Parajuli, 2014): the regression coefficient (R ), and Nash Sutcliffe Efficiency index (NSE). The Root Mean Square Error (RMSE) was also used to evaluate crop yield prediction (Kim et al, 2013).…”

Section: Climate Change Model Calibration and Validationmentioning

confidence: 99%

Assessing the impacts of climate change and tillage practices on stream flow, crop and sediment yields from the Mississippi River Basin

Parajuli

Jayakody

Sassenrath

et al. 2016

Agricultural Water Management

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This study evaluated climate change impacts on stream flow, crop and sediment yields from three different tillage systems (conventional, reduced 1close to conservation, and reduced 2close to no-till), in the Big Sunflower River Watershed (BSRW) in Mississippi. The Soil and Water Assessment Tool (SWAT) model was applied to the BSRW using observed stream flow and crop yields data. The model was calibrated and validated successfully using monthly stream flow data (2001 to 2011). The model performances showed the regression coefficient (R 2) from 0.72 to 0.82 and Nash Sutcliffe Efficiency Index (NSE) from 0.70 to 0.81 for streamflow; R 2 from 0.40 to 0.50 and NSE from 0.72 to 0.86 for corn yields; and R 2 from 0.43 to 0.59 and NSE from 0.48 to 0.57 for soybeans yields. The Long Ashton Research Station Weather Generator (LARS-WG), was used to generate future climate scenarios. The SRES (Special Report on Emissions Scenarios) A1B, A2, and B1 climate change scenarios of the Intergovernmental Panel on Climate Change (IPCC) were simulated for the mid (2046-2065) and late (2080-2099) century. Model outputs showed slight differences among tillage practices for corn and soybean yields. However, model simulated sediment yield results indicated a large difference among the tillage practices from the corn and soybean crop fields. The simulated future average maximum temperature showed as high as 4.8°C increase in the BSRW. Monthly precipitation patterns will remain unchanged based on simulated future climate scenarios except for an increase in the frequency of extreme rainfall events occurring in the watershed. On average, the effect of climate change and tillage practice together did not show notable changes to the future crop yields. The reduced tillage 2

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Section: Climate Change Model Calibration and Validationmentioning

confidence: 99%

Assessing the impacts of climate change and tillage practices on stream flow, crop and sediment yields from the Mississippi River Basin

Parajuli

Jayakody

Sassenrath

et al. 2016

Agricultural Water Management

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“…To this end, the Soil and Water Assessment Tool (SWAT) model is an efficient tool developed to predict different nutrient loadings delivered to water resources as a function of multiple management practices, and LULC change scenarios over long periods of time (Arnold et al 1998). SWAT is appropriate as it has been applied for the simulation of different pollutant parameters under different climatic areas, geographic locations, and management conditions (Gassman et al 2007;Parajuli 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale

Zena,

Demissie,

Feyessa

2024

Water Science &Amp; Technology

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Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

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Combined impacts of precipitation and temperature on diffuse phosphorus pollution loading and critical source area identification in a freeze-thaw area

Peng

Ouyang

Hao

et al. 2016

Science of The Total Environment

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Evaluation of spatial variability on hydrology and nutrient source loads at watershed scale using a modeling approach

Cited by 4 publications

References 32 publications

Assessing the impacts of climate change and tillage practices on stream flow, crop and sediment yields from the Mississippi River Basin

Assessing the impacts of climate change and tillage practices on stream flow, crop and sediment yields from the Mississippi River Basin

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale

Combined impacts of precipitation and temperature on diffuse phosphorus pollution loading and critical source area identification in a freeze-thaw area

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