Agricultural Non-Point Source Pollution Model Research Articles

Impact of Riparian Buffer Zone Design on Surface Water Quality at the Watershed Scale, a Case Study in the Jinghe Watershed in China

This study was conducted to evaluate the impact of riparian buffer zones on water quality in the Jinghe watershed, China. To evaluate the effectiveness of riparian buffers in reducing sediments and nutrients in surface runoff, we employed two validated models: the agricultural non-point source pollution model (AnnAGNPS) and the riparian ecosystem management model (REMM). The AnnAGNPS was used to divide the catchment into homogeneous drainage areas and generate upland loadings for the REMM. The REMM model was then utilized to assess the impact of different riparian buffer designs on sediments and nutrient reduction in surface runoff. We tested five designs, including the recommended standard design by the United States Department of Agriculture (USDA). This design with 20 m herbaceous perennials next to the field (Zone 3), followed by a 20 m wide harvestable deciduous forest in the middle (Zone 2), and a 10 m wide non-harvestable deciduous forest adjacent to the river (Zone 1). We also evaluated alternative designs, such as removing Zone 3, removing Zone 2, and reducing the widths of the buffer zones further. For the entire Jinghe watershed, we calculated, compared, and analyzed the annual totals of water inflow, sediment yields, and dissolved nitrogen in surface runoff into and out of Zone 1, 2, and 3 for all the designs. The analysis indicated that the removal efficiency of sediments ranged from 85.7% to 90.8%, and the removal efficiency of dissolved nitrogen in surface runoff ranged from 85.4% to 91.9% for all the designs. It is also indicated that riparian buffer zones are highly effective in reducing sediments and nutrients in agricultural runoff, even with reduced buffer widths. This finding underscores the importance of implementing riparian buffer zones as a valuable approach in the agricultural intensive watershed with constraints for allocating for the creation of standard riparian buffers.

Assessing Soil Vulnerability Index classification with respect to rainfall characteristics

The Soil Vulnerability Index (SVI) uses widely available inputs from the SSURGO database to classify cropland into four levels of vulnerability to sediment and nutrient losses: Low, Moderate, Moderately High, and High. Previous work has identified inconsistencies in SVI assessments across the United States, possibly because neither precipitation amount nor intensity were included in the development of SVI. This study aimed to determine if rainfall characteristics influence the SVI classification and which ones are most critical. The objectives were to (1) evaluate the impact of precipitation characteristics on land vulnerability to sediment loss, and (2) evaluate if rainfall characteristics alter the degree of agreement between the simulated sediment yield and SVI classification. The study focused on four Conservation Effects Assessment Project (CEAP) watersheds in Ohio, Missouri, Mississippi, and Pennsylvania for which sediment yields were simulated using previously calibrated models. The models were run with input precipitation data from these four watersheds. In addition, in order to examine a wider range of precipitation characteristics, model runs were made for the same four watersheds utilizing precipitation data from two CEAP areas in Georgia and Maryland. Sediment yields for all the cropland units in four of the watersheds were simulated using the Soil and Water Assessment Tool or the Annualized Agricultural Nonpoint Source Pollution Model using 1985 to 2014 precipitation data from all six areas as inputs. Similarities and differences between precipitation characteristics such as precipitation amount, intensity, and rainfall erosivity R-factors were compared with the similarities and differences in simulated sediment loss. Results confirmed that SVI is a useful tool for relative ranking of cropland at risk of erosion within a region, as SVI and the model-based vulnerability classifications agreed for 55% to 100% of the watersheds’ subunits. However, model-based classification of field vulnerability could shift due to changes in precipitation characteristics. Thus, the range of soil loss for each vulnerability class can shift from one region to another. The results suggest that precipitation intensity or annual R-factor may help improve the correspondence between vulnerability and the range of expected soil loss.

An improved minimum cumulative resistance model for risk assessment of agricultural non-point source pollution in the coastal zone

Agricultural non-point source pollution (AGNPSP) is an important risk factor affecting the water environment. Among the areas where cropland NPSP occurs, the coastal zone should be of greater concern. Typhoons, heavy precipitation, and abundant rivers and ponds accelerate the transport process of AGNPSP to the offshore waters. It is urgent to construct a simple and accurate model to assess the risk of AGNPSP in the coastal zones. Thus, this study takes the nitrogen pollution from agricultural cultivation in the coastal zone of the Yellow River Delta as an example. A new minimum cumulative resistance (MCR) to agricultural non-point source pollution (AGNPSP-MCR) model is first proposed to simulate the transport process of cropland NPSP into the sea based on the “source-sink” theory in landscape ecology. Finally, the risk is assessed for AGNPSP transport into the sea. The results show the following. (1) The environmental factors of vegetation cover, rainfall erosivity, and soil erodibility are the three most important factors in pollution transportation, weighted 0.3433, 0.2608, and 0.2219, respectively, while the least influential factor is slope, with a weight of only 0.0053. (2) The minimum cumulative resistance of AGNPSP transportation shows a significant positive correlation with the distance to the river and sea, and is higher on the west sides away from the ocean, and smaller in the eastern coastal area near the sea. (3) Similarly, the regions facing serious AGNPSP risk are concentrated in the eastern coastal area, and the cropland area above medium risk was 252.72 km2, accounting for 47.57% of the total cultivated land area. (4) Compared with the traditional MCR model, the AGNPSP-MCR model takes into account the quantitative differences of the transport process characteristics of AGNPSP, and it is constrained by the topographical parameter, so the results of AGNPSP risk are more reliable. This study provides a new reference for risk assessments of AGNPSP in the coastal zones.

Soil erosion assessment in Ethiopia: A review

Soil erosion is a critical problem affecting rural livelihoods in Ethiopia. Large numbers of studies have been undertaken to identify critical areas of soil loss and prioritize conservation options. With the advancement of geospatial analysis techniques, the use of spatially distributed soil erosion assessment options has become increasingly common. The lack of database and documentation related to soil erosion assessment undermines coordination leading to duplication of efforts and in some instances generating contradictory results. The purpose of this study is to (1) review existing knowledge related to the extent and spatial distribution of soil erosion, (2) document the associated methods employed to assess soil erosion, and (3) assess the spatio-temporal dynamics of soil erosion and the determinant factors in Ethiopia. The review shows that there are about 170 peer-reviewed papers published in scientific journals related to soil erosion in Ethiopia. In those scientific articles, 15 different approaches were used to assess soil loss and sediment yield at different scales. Considering the data set, soil loss rate in Ethiopia varies between 0 and 220 t ha^–1 y^–1, and sediment yield ranges between 2 and 70 t ha^–1 y^–1. Based on the database, the national average gross soil erosion rate is estimated to be 38 t ha^–1 y^–1, while the net sediment yield is about 26 t ha^–1 y^–1. Generally, the observed gross soil loss is slightly lower than the mean value from cultivated lands reported in previous estimates (42 t ha^–1 y^–1) by Hurni (1993). The estimate made using the Revised Universal Soil Loss Equation gives the highest soil loss (51 t ha^–1 y^–1), while that based on field-survey approaches gives the lowest (20 t ha^–1 y^–1). The highest average net soil loss rate (40 t ha^–1 y^–1) is obtained using plot-level measurements, while the lowest (18 t ha^–1 y^–1) is obtained using the AGricultural Non-Point Source pollution model. The highest average soil erosion rate is observed in the moist agro-ecological zone (57 ± 7.8 t ha^–1 y^–1), while the lowest is obtained in the submoist (23.6 ± 2.7 t ha^–1 y^–1) following the arid zone (28.8 ± 6.5 t ha^–1 y^–1). The wide range of soil erosion estimates imply spatio-temporal dynamics of soil erosion in the country, which is mainly a reflection of heterogeneity of the various sites, mainly associated with different values of cover and management factors. Such knowledge can enable making informed conservation decisions by focusing on critical hotspots.

Evaluating Hydrological Model Performances on Stream Flow Simulation with Agricultural Management

Two comprehensive agricultural watershed-scale hydrological models: Soil and Water Assessment Tool (SWAT), and Annualized Agricultural Non-Point Source Pollution Model (AnnAGNPS), have been widely used for evaluating agricultural Best Management Practice (BMP). In this study, models were developed for the Big Sunflower River Watershed (BSRW) in Mississippi, USA. Both hydrological models were successfully calibrated and validated for predicting monthly stream flow discharges from three USGS gages (Merigold, Sunflower, and Leland) within the BSRW. The model performances were reasonable with R 2 and NSE values range from 0.45 to 0.84 for SWAT, and from 0.34 to 0.66 for AnnAGNPS. Both models predicted lower stream flow discharge with conservation tillage practice as compare to conventional tillage practice BMP evaluated in this study. Overall, results showed that both SWAT and AnnAGNPS models successfully simulated hydrological conditions of the watershed with agricultural BMP, and these models can be useful for making hydrological decisions related to agricultural watershed managements. Keywords: SWAT, AnnAGNPS, tillage, streamflow, hydrology, BMP DOI: 10.7176/JEES/11-1-03 Publication date: January 31 st 2021

Evaluating Hydrological Model Performances on Stream Flow Simulation with Agricultural Management

Two comprehensive agricultural watershed-scale hydrological models: Soil and Water Assessment Tool (SWAT), and Annualized Agricultural Non-Point Source Pollution Model (AnnAGNPS), have been widely used for evaluating agricultural Best Management Practice (BMP). In this study, models were developed for the Big Sunflower River Watershed (BSRW) in Mississippi, USA. Both hydrological models were successfully calibrated and validated for predicting monthly stream flow discharges from three USGS gages (Merigold, Sunflower, and Leland) within the BSRW. The model performances were reasonable with R 2 and NSE values range from 0.45 to 0.84 for SWAT, and from 0.34 to 0.66 for AnnAGNPS. Both models predicted lower stream flow discharge with conservation tillage practice as compare to conventional tillage practice BMP evaluated in this study. Overall, results showed that both SWAT and AnnAGNPS models successfully simulated hydrological conditions of the watershed with agricultural BMP, and these models can be useful for making hydrological decisions related to agricultural watershed managements. Keywords: SWAT, AnnAGNPS, tillage, streamflow, hydrology, BMP DOI: 10.7176/JEP/12-3-03 Publication date: January 31 st 2021

Simulation of Runoff and Sediment Yield Using AnnAGNPS Model at Dawe Watershed, Eastern Hararghe, Ethiopia

The high erosion rates are mainly affecting the developing countries due to intensive cultivation, deforestation; extreme climate hazard, sediment transport and loss of agricultural nutrients were caused by unwise land use practices, intensive cultivation and improper management. Soil conservation is the only known way to protect the productive land. In this study a physically based watershed model, Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model was applied to the Dawe River watershed for simulation of the runoff and sediment yield. The objectives were to estimate potential runoff and sediment yield and to recommend and design appropriate soil and water conservation measures on a sub watershed basis in Dawe watershed of east hararghe zone. Sensitivity analysis, model calibration and validation were also performed. Four highly sensitive parameters were identified and of which CN was the most sensitive one. For model calibration, model efficiencies of 0.742, -231.081 and 0.828 were observed for surface runoff, peak runoff rate, and sediment yield, respectively. The corresponding determination of coefficients was 0.825, 0.1669 and 0.848, respectively. Runoff and sediment yield were well predicted but, peak runoff rate was over predicted. Validation results produced model efficiencies NSE of 0.769, -73.801 and 0.718 for surface runoff, peak runoff rate and sediment yield, respectively. With coefficient of determination (R²) of 0.9215, 0.235 and 0.764 for runoff, peak runoff rate and sediment yield, respectively. Surface runoff and sediment yield simulation were well in the validation stage and peak runoff rate shows the same trend as calibration. Dawe watershed was divided in thirteen sub-watersheds. Runoff and sediment yield for each sub-watershed were quantified. Average annual watershed runoff, average annual soil loss and total annual sediment outflow from Dawe watershed was 194.48mm, 22.467 tons/ha/yr and 354215 tons/yr, respectively. In Dawe watershed, gully, rill and inter-rill erosions were identified as major problems. Thus, check dam and bench terrace designed and vegetative waterway are recommended for intensively cultivated crop land of Dawe watershed.

Non-point source pollution and long-term effects of best management measures simulated in the Qifeng River Basin in the karst area of Southwest China

Abstract Non-point source (NPS) pollution has caused serious threats to water quality on a global scale. However, the investigation using a single measure with multi-scenarios for the long-term simulation in karst agricultural watershed is still lacking. In this study, the Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model was developed to verify the applicability in a karst agricultural watershed. Then, the model was used to determine the best management measures (BMPs) and the reduction rate characteristics under long-term effects (about 10 years) in the watershed. The AnnAGNPS model perform well in simulating in NPS pollution with R2 (0.95 for runoff, 0.93 for TN, and 0.93 for TP, respectively) and NSE (0.95 for runoff, 0.53 for TN, and 0.57 for TP, respectively). The output of total nitrogen (TN) and total phosphorus (TP) primarily occurred in the rainy season (up to 80%). The loss of mass of TN and TP were mainly observed in orchards and woodlands in the upstream of each sub-basin. The results from AnnAGNPS model demonstrated that different BMPs had significant impacts on the reduction of NPS pollution. Furthermore, a same BMPs measure showed it was closely related to land use in the watershed. In the Qifeng River watershed, stubble tillage (ST) showed to be useful with relatively good reduction rates (16.64% for sediment, 17.85% for TN, and 17.80% for TP, respectively). The simulation results indicated that AnnAGNPS was a valuable tool after validation for the planning and management of the watershed in karst areas.

Mapping runoff generating areas using AGNPS-VSA model

ABSTRACT In humid regions, surface runoff is often generated by saturation-excess runoff mechanisms from relatively small variable source areas (VSAs). However, the majority of the current hydrologic models are based on infiltration-excess mechanisms. In this study, the AGricultural Non-Point Source Pollution (AGNPS) model was used to integrate the VSA concept using topographic wetness index (TWI). Both the original and AGNPS-VSA models were evaluated for a small agricultural field in Ontario, Canada. The results indicate that the AGNPS-VSA model performed better than original model. The AGNPS-VSA model predicted that only the saturated portion of the field with higher TWI values produced runoff, whereas the original AGNPS model showed uniform hydrologic response from the entire field. The results of this study are important for accurately mapping the locations of VSAs. This new model could be a powerful tool in identifying critical source areas for applying targeted best management practices to minimize pollutant loads to receiving waters.

Assessment of the Soil Vulnerability Index and comparison with AnnAGNPS in two Lower Mississippi River Basin watersheds

There is an increasing need to quickly and accurately identify areas where agricultural conservation practices can provide the greatest reduction in nutrient and sediment runoff. Geographic information systems (GIS)-based tools and indices are promising for identifying critical areas that are significant contributors of nonpoint source pollution loads with limited data. One such tool, the Soil Vulnerability Index (SVI), is tested here in Beasley Lake and Goodwin Creek watersheds in Mississippi. The SVI runoff component results are compared against aerial images and long-term land use histories in the watershed to determine if a higher SVI score is related to visibly degraded land or land removed from cultivation. SVI results are also compared to sediment yield estimates generated with the Annualized Agricultural Non-Point Source pollution model (AnnAGNPS) to determine the degree of agreement. The SVI runoff score demonstrated agreement with imagery and land use histories in both watersheds. The SVI categories and corresponding AnnAGNPS-predicted sediment yield also had moderate agreement, with 45% and 68% of watershed area in agreement in Beasley Lake and Goodwin Creek watersheds, respectively. In general, the tool is a quick way to assess spatial areas potentially contributing to nonpoint source pollution, which can then be combined with field-based knowledge and/or imagery to provide valuable insight for placement of conservation practices.

Assessing effects of “source-sink” landscape on non-point source pollution based on cell units of a small agricultural catchment

Ascertaining the relationship between “source-sink” landscape and non-point source (NPS) pollution is crucial for reducing NPS pollution, however, it is not easy to realize this target on cell unit scale. To reveal the relationships between “source-sink” landscape and NPS pollution based on cell units of a small catchment in the Three Gorges Reservoir Region (TGRR), the runoff and nutrient yields were simulated first by rainfall events on a cell unit scale based on the Annualized AGricultural Non-Point Source Pollution Model (AnnAGNPS). Landscape structure and pattern were quantified with “source-sink” landscape indicators based on cell units including landscape area indices and location-weighted landscape indices. The results showed that: the study case of small Wangjiagou catchment highlighted a good prediction capability of runoff and nutrient export by the AnnAGNPS model. Throughout the catchment, the spatial distribution trends of four location-weighted landscape indices were similar to the trends of simulated total nitrogen (TN) and total phosphorus (TP), which highlighted the importance of spatial arrangement of “source” and “sink” landscape types in a catchment when estimating pollutant loads. Results by Pearson correlation analysis indicated that the location-weighted landscape index provided a more comprehensive account of multiple factors, and can better reflect NPS-related nutrient loss than other landscape indices applied in single-factor analysis. This study provides new findings for applying the “source-sink” landscape indices based on cell units in small catchments to explain the effect of “source-sink” landscape on nutrient export based on cell unit, and helps improve the understanding of the correlation between “source-sink” landscape and NPS pollution.

Determining the depth of mixing layer in which soil solute releasing from soil to surface runoff on the unsaturated loess slope under artificial rainfall condition

The mixing zone depth is a critical parameter in many popular agricultural non-point source pollution (NPS) models. Its variation for some special soil conditions and easily erosive region was limitedly studied. This study dealt with the extent of Br− releasing from soil to surface runoff, the actual mixing depth and the calculated one on the unsaturated loess slope, and the influences of soil initial moistures. Br− was placed as a tracer at different soil depths in the loess slope with three soil initial moistures. Br− concentration in surface runoff and soil profiles was monitored under simulated rainfall condition. The actual mixing layer depth was determined by whether Br− in that layer could be detected in surface runoff during the whole rainfall process. The average effective depth of interaction (EDI) was calculated by the confirmation method reported before. The shallower the application depth, the higher the Br− concentration and loss risk in surface runoff. The net loss rate had an exponential decreasing correlation with the solute-applied depth. The depth of Br− peak content in soil profile after the rainfall ended increased with the application depth. Br− concentration in the surface soil was more than that in the runoff at the end of rainfall, which indicated an incomplete mixing between runoff water and soil water in the mixing layer. Under this experimental condition, the actual mixing layer depth for 3.5% soil moisture slope was about 3 cm, that for 10% soil moisture was 6 cm or so, and that for 20% soil moisture was more than 7 cm. The average EDI calculated for 3.5, 10, and 20% soil moisture was 2.31, 3.76 and 4.95 cm, respectively, which were less than the actual mixing layer depth and more than some former studies. The traditional 1.0-cm mixing depth in many agricultural NPS models might not be so exact for some special region.

Assessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa

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.

Modeling Tropical Cyclone Induced Stream Flow in Tar Pamlico River of North Carolina

ABSTRACT In order to better understand and simulate large coastal watershed hydrology and hydro-meteorological processes associated with tropical cyclone (TC) induced inland flooding, the Annualized Agricultural Nonpoint source pollution Model (A nn AGNPS) with Muskingum channel routing has been applied to the Tar-pamlico river basin in North Carolina, USA. The study focuses on three major hydro-meteorological processes: 1) large-scale atmospheric environment, 2) watershed hydrological processes and 3) groundwater response. The modeling results indicate that although the AnnAGNPS model can perform well in predicting the total amount of watershed runoff, channel routing is needed to improve the hydrographs of flow discharge during hurricane events. Sensitivity analysis of soil saturated hydrological conductivity ( K s ) indicates that both base flow and event total runoff are sensitive to K s . Base flow increases as K s increases when 0 K s ≤ 15 m/day, but decreases slightly when K s > 15 m/day. Peak runoff exponentially decreases as K s increases. The results also show that without the preceding Hurricane Dennis, the outlet discharge as a result of Hurricane Floyd would have been as much as 37% lower than that caused by the combined Dennis-Floyd effect.

Characterizing ponds in a watershed simulation and evaluating their influence on streamflow in a Mississippi watershed

ABSTRACTSmall water bodies, such as ponds and wetlands, are common landscape features, but often are not simulated within a watershed modeling framework. The wetland modeling tool, AgWET, uses a GIS framework to characterize the features of ponds and wetlands so that they can be incorporated into watershed simulations using the Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model. AgWET was used to characterize farm ponds on the Goodwin Creek Experimental Watershed in northwest Mississippi and AnnAGNPS simulated watershed hydrology. Monthly streamflow was validated at four watershed gauges with Nash-Sutcliffe efficiency values between 0.91 and 0.94. Ponds influenced watershed hydrology at various scales, with a decrease in average streamflow by 4% at the watershed outlet, 8% at the sub-watershed scale, and an average of 56% immediately downstream of the ponds. AgWET can be used to simulate ponds in watershed assessments for improved results and evaluation of future scenarios.

Modeling land use changes and their impact on sediment load in a Mediterranean watershed

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.

Modelling triazines in the valley of the River Cauca, Colombia, using the annualized agricultural non-point source pollution model

The annualized agricultural non-point source pollution model (AnnAGNPS) was applied to simulate losses of triazine herbicides to the River Cauca following application to sugarcane, maize and sorghum in the Cauca Valley of Colombia. Surface runoff was found to be the main driver of triazine losses to surface water in the catchment. Satisfactory simulation and validation of the hydrology was achieved after little calibration (Nash-Sutcliffe model efficiency=0.70 and r2=0.73). A fairly good simulation of pesticides was generally achieved, but some patterns in the measured data could not be simulated. Uncertainty analyses of sensitive input parameters were carried out which explained most of the concentrations that were not captured by the initial simulation; however, evidence of point source pollution was observed for some large concentrations measured upstream. Replacing triazine herbicides with mesotrione was predicted to result in an 87% reduction in pesticide losses expressed as a proportion of the total pesticide applied.

Evaluating ephemeral gully erosion impact on Zea mays L. yield and economics using AnnAGNPS

Ephemeral gully erosion causes serious water quality and economic problems in the Midwest United States. A critical barrier to soil conservation practice adoption is often the implementation cost, although it is recognized that erosion reduces farm income. Yet few, if any, understand the relationship between cost of conservation practice implementation and potential economic benefit gained from erosion control practices, especially as related to ephemeral gully erosion. The objectives of this research are to: (1) evaluate the soil loss and corresponding topsoil depth reduction due to annually ephemeral gully filling; and (2) estimate the economic loss associated with the crop production reduction attributed to topsoil thinning. Surface runoff and watershed sediment yield were flume measured at the approximately 1-ha drainage scale in Iowa. Sediment yield of the developing ephemeral gully was partitioned from measured total watershed sediment loss, by modeling ephemeral gully development with the Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS), and subtracting this soil loss value from the flume measured watershed total. Topsoil thinning in the adjacent area used to fill the ephemeral gully was calculated based on the corresponding ephemeral gully sediment yield. The effect of A horizon thickness on corn yield obtained from published literature was used to calculate the corn (Zea mays L.) yield reduction due to topsoil thinning. Ephemeral gully erosion negatively impacts farm economics in the long term and implies that soil conservation measures should be carefully designed and well maintained. However, this evidence suggests that costs associated with establishment and use of structures such as grass waterways to minimize or eliminate ephemeral gully formation will not be recuperated in the short term through yield potential maintenance.

Assessment of the AnnAGNPS model in simulating runoff and nutrients in a typical small watershed in the Taihu Lake basin, China

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 (R2) 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 R2=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 R2=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.

A Multi-Criteria Model Selection Protocol for Practical Applications to Nutrient Transport at the Catchment Scale

Process-based models are widely used to investigate nutrient dynamics for water management purposes. Simulating nutrient transport and transformation processes from agricultural land into water bodies at the catchment scale are particularly relevant and challenging tasks for water authorities. However, few practical methods guide inexperienced modelers in the selection process of an appropriate model. In particular, data availability is a key aspect in a model selection protocol, since a large number of models contain the functionalities to predict nutrient fate and transport, yet a smaller number is applicable to specific datasets. In our work, we aim at providing a model selection protocol fit for practical application with particular emphasis on data availability, cost-benefit analysis and user’s objectives. We select for illustrative purposes five process-based models with different complexity as “candidates” models: SWAT (Soil and Water Assessment Tool), SWIM (Soil and Water Integrated Model), GWLF (Generalized Watershed Loading Function), AnnAGNPS (Annualized Agricultural Non-Point Source Pollution model) and HSPF (Hydrological simulation program-FORTRAN). The models are described in terms of hydrological and chemical output and input requirements. The model selection protocol considers data availability, model characteristics and user’s objectives and it is applied to hypothetical scenarios. This selection method is particularly formulated to choose process-based models for nutrient modeling, but it can be generalized for other applications which are characterized by a similar degree of complexity.