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The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized AGricultural NonPoint Source) pollution model, in calculating runoff, sediment loading and nutrient loadings for Funiu Mountain area. Most of the model input parameters were sourced from Luanchuan Forest Ecology Station (LFES) in Funiu Mountain area. The data on 23 storms in 2018 was used to calibrate the model and the data on 33 storms in 2019 for validation. The whole evaluation consisted of determining the coefficient of determination (R²), Nash-Sutcliffe coefficient of efficiency (E), and the percentage volume error (VE). Results showed that the runoff volumes were underpredicted by 5.0% with R² of 0.93 (P<0.05) during calibration and underpredicted by 5.3% with R² of 0.90 (P < 0.05) during validation. But sediment loading was able to produce a moderate result. The model underpredicted the daily sediment loading by 15.1% with R² of 0.63 (P < 0.05) during calibration and 13.5% with R² of 0.66 (P < 0.05) during validation. Nitrogen loading was overpredicted by 20.3% with R² = 0.68 (P < 0.05), and phosphorus loading performance was slightly poor with R2 = 0.65 ( P < 0.05) during validation. In general, the model performed well in simulating runoff compared to sediment loading and nutrient loadings. [ABSTRACT FROM AUTHOR]

Based on GIS technology and AnnAGNPS model, the outputs of runoff and soil erosion in different periods of seven typical agroforestry watersheds in Funiu Mountains were studied. The results showed that after the implementation of the project of returning farmland to forests and ecological industrialization in the study watersheds in the Funiu Mountains, the function of soil and water conservation has been continuously enhanced. Compared with 2000, in 2020, the average surface runoff of the seven watersheds decreased by 40.7 mm, and the soil erosion amount decreased by 5.41 t/hm²; the watershed runoff had a significant positive correlation with slope (P < 0.05), a significant negative correlation with forest land area ratio (P < 0.05), but no significant correlation with agricultural land ratio; soil erosion was negatively correlated with forest land (P < 0.05), but positively correlated with farmland (P < 0.05); and the average spatial variation coefficient of soil erosion was 160%, which was 4.35 times that of runoff, and the uncertainty of factors affecting soil erosion was much higher than that of runoff. [ABSTRACT FROM AUTHOR]

This study aims to compare inundation in two potholes using Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) with three Digital Elevation Models (DEMs): a 1 m DEM prepared from the LiDAR data which is readily available for the state of Iowa, USGS 1/9 arc-second DEM (∼3 m) which covers about 25% of the conterminous U.S. and USGS 1/3 arc-second DEM (∼10 m) which covers the entire USA. In this study, we found that the variations in water depth and presence/absence of ponding in the potholes of size greater than 1 ha can be predicted using USGS DEMs. The estimates of average water depths using USGS 3 m DEM was found to be 6% and 2% lower than the 1 m LiDAR DEM and the estimates of average water depths using USGS 10 m DEM was found to be 7% and 12% higher than the 1 m LiDAR DEM for the Walnut and Bunny potholes, respectively. [ABSTRACT FROM AUTHOR]

The aim of this study is to model potential changes in land use and evaluate their effects on sediment load in a Mediterranean watershed, the Carapelle, in Southern Italy. For this purpose, a set of Landsat Thematic Mapper (TM) images were processed to generate three different land use maps for 1987, 2002, and 2011. The images were corrected for geometric distortion and atmospheric interference before performing an unsupervised classification and decision expert system post classification. The land use maps for 1987 and 2002, derived from the Landsat TM processing, were analyzed using a Land Change Modeler (LCM) module to identify transitions from the first land cover type to the second. The transitions were modeled using a multi-layer perceptron (MLP) neural network to create transition potential maps, which provide the controls for subsequent dynamic land use change predictions. The model produced a predicted land use map for 2011 using Markov Chain analysis, which was validated with the actual 2011 land use map. Consequently, a land use scenario (S1) for 2035 and 2050 was predicted, taking into account the current constraints and management options. LCM was further used to define two additional scenarios (S2 and S3) both for 2035 and 2050 based on different land management options. Finally, the Annual Agricultural Non-Point Source Pollution Model (AnnAGNPS) was used to estimate the effect of the predicted land use changes on sediment load after model calibration, using a five-year dataset registered at the Ordona monitoring station. The land use change analysis revealed low transformations from 1987 to 2011. Equally, land use changes were low for the base scenario (S1) so moderate variations in sediment load were estimated. The changes in land use were more significant for the additional scenarios (S2 and S3) and consequently the model estimations underwent major variations, with a significant reduction of soil erosion. The associated utilization of land use change analysis and AnnAGNPS modeling demonstrates how land use management options can be adopted to reduce potential watershed sediment load. [ABSTRACT FROM AUTHOR]

The Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model is a distributed-parameter continuous-simulation watershed-scale model used to simulate runoff, sediment, nutrient, and pesticide loads exiting from agricultural areas through drainage streams. The aim of this study was to evaluate the performance and suitability of the AnnAGNPS model in predicting runoff and nitrogen and phosphorus loading in a typical small drinking water source watershed located in the region of Taihu Lake (China) using estimated yearly runoff data and monthly observed nutrient data. The estimated runoff data for the years 2005–2009 and 2010–2013 were used to calibrate and validate the annual runoff. The data for the monthly observed nutrients from July 2008 to September 2009 and December 2012 to December 2013 were used to calibrate and validate the monthly nutrient load. The results showed that the model provided satisfactory simulations of annual surface runoff, and for the calibration and validation, the coefficient of determination (R 2 ) values were 0.96 and 0.97, respectively, and Nash–Sutcliffe efficiency coefficient (Ens) values were 0.96 and 0.97, respectively. The model was also capable of simulating the monthly nitrogen and phosphorus load, with the nitrogen load simulation yielding moderate calibration and validation results of R 2 = 0.86 and 0.88, respectively, and Ens = 0.82 and 0.87, respectively, and the phosphorus load yielding slightly poorer calibration and validation results of R 2 = 0.60 and 0.83, respectively, and Ens = 0.61 and − 3.86, respectively. The results obtained from applying the AnnAGNPS to this typical small watershed in the Taihu Lake basin demonstrate that the model has considerable potential as a research and management tool for comparative assessments and runoff and nutrient yield estimations in similar watersheds. [ABSTRACT FROM AUTHOR]

• Boosted regression tree model is robust in charactering nonlinear non-point source pollution generation and transport processes. • The nonlinear responses of critical source areas to influencing factors was evaluated quantitatively. • The thresholds for influencing factors can provide supportive information for watershed management. • Land use and fertilizer application have higher importance in determining the occurrence of critical source areas. • Machine learning techniques have great potential for predicting critical source areas under climate change. Critical Source Areas (CSAs) are areas that contribute disproportionate high levels of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of the complex interaction between the factors related to the sources and transport processes of NPS pollution. A systematic understanding of how these influencing factors affect CSAs is essential for successful watershed management. In this study, we applied a statistical data mining technique boosted regression tree model to quantify the contribution of eight influencing factors (soil type, slope, elevation, RUSLE LS factor, RUSLE K factor, runoff, fertilizer application rate and land use) on two types of CSAs (TN-CSAs and TP-CSAs), as well as the marginal effects and potential thresholds of influencing factors on the occurrence of CSAs. Results show that land use (37.35%, 25.03%), fertilizer application (36.93%, 57.83%) and soil type (17.59%, 13.70%) have higher importance in determining the occurrence of TN-CSAs and TP-CSAs; and the incidence of TN-CSAs is positively correlated with most factors before the threshold for each influencing factor, after which the marginal effect largely leveled off or dropped slightly; TP-CSAs have essentially the same characteristics as TN-CSAs, but TP-CSAs are more likely to occur in areas with an annual runoff of around 244.92 mm. In addition, this study discussed the application of machine learning techniques in predicting CSAs under climate change without physical-based models, as well as a preliminary watershed management planning for NPS pollution control in the study watershed. These results provided important information for nutrient management regulations. [ABSTRACT FROM AUTHOR]

In order to evaluate prediction models of runoff and sediment yield in a Mediterranean environment, the distributed parameter, physically based, continuous simulation, daily time step AnnAGNPS model was applied to an experimental watershed of mainly pasture in Sicily. Results from AnnAGNPS simulations were evaluated using 7-year data monitored at this watershed. The model showed satisfactory capability in simulating surface runoff at event, monthly, and annual scales after calibration. Peak flow predictions were generally good for low flow events and poorer for higher flow rates. A high model efficiency was achieved for the 24 suspended sediment yield events recorded during the entire period of observation after reducing the roughness coefficients for both rangeland and cropland areas. The overall results confirmed the applicability of the AnnAGNPS model to the experimental conditions. [ABSTRACT FROM AUTHOR]

The modelling package Annualized Agricultural Nonpoint Source Model (AnnAGNPS) was applied to the prediction of export of nitrogen and phosphorus from Currency Creek, a small experimental catchment within the Hawkesbury–Nepean drainage basin of the Sydney Region. The catchment is 255 ha in area and has experienced extensive soil erosion and losses of nutrients from intensive vegetable cultivation, irrigated dairy pasture and poultry farms. Simulations of nitrogen and phosphorus loads in the Currency Creek catchment were performed at various temporal scales and the degree of calibration was quantified by comparing the simulated data with the monitoring results. In addition, the model independent, nonlinear parameter estimation code PEST, was applied for sensitivity testing to determine and assess the relative importance of the key parameters of the model. Event flows were simulated satisfactorily with AnnAGNPS but only moderate accuracy was achieved for prediction of event-based nitrogen and phosphorus exports. The biggest deviations from the measured data were observed for daily simulations but trends in the generated nutrients matched observed data. Despite achieving good resemblance between measured and predicted phosphorus loads the model showed high level of sensitivity to assigned pH values for topsoil. Increase in pH by one unit resulted in up to 34% increase in model generated particulate phosphorus load. [Copyright &y& Elsevier]