Philip L. Barnes

Soil loss predictions with three erosion simulation models

Abstract Quantification of soil loss is one of the greatest challenges in natural resources and environmental planning. Computer simulation models are becoming increasingly popular in predicting soil loss for various land use and management practices. In this study, three soil erosion prediction models — the Water Erosion Prediction Project (WEPP), the Erosion Productivity Impact Calculator (EPIC), and the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS) were used for simulating soil loss and testing the capability of the models in predicting soil losses for three different tillage systems (ridge-till, chisel-plow, and no-till). For each model, the most sensitive model parameters were calibrated using measured soil erosion data. After calibration, models were run and predicted soil loss values were compared with the measured soil loss values. The measured soil erosion data were collected from an erosion experiment field of Kansas State University at Ottawa (Kansas), USA. Field experiments were conducted from 1995 to 1997 on small plots to measure runoff and soil losses under all three tillage systems. All three models were evaluated on the basis of individual event, total yearly, and mean event-based soil loss predictions. Results showed that all the three models performed reasonably well and the predicted soil looses were within the range of measured values. For ridge-till and chisel-plow systems, WEPP and ANSWERS gave better predictions than those by EPIC model. For no-till system, WEPP and EPIC predictions were better than those by ANSWERS. The overall results indicate that WEPP predictions were better than those by the other two models in most of the cases, and it can be used with reasonable degree of confidence for soil loss quantification for all the three tillage systems.

Source specific fecal bacteria modeling using soil and water assessment tool model

Fecal bacteria can contaminate water and result in illness or death. It is often difficult to accurately determine sources of fecal bacteria contamination, but bacteria source tracking can help identify non-point sources of fecal bacteria such as livestock, humans and wildlife. The Soil and Water Assessment Tool (SWAT) microbial sub-model 2005 was used to evaluate source-specific fecal bacteria using three years (2004-2006) of observed modified deterministic probability of bacteria source tracking data, as well as measure hydrologic and water quality data. This study modeled source-specific bacteria using a model previously calibrated for flow, sediment and total fecal coliform bacteria (FCB) concentration. The SWAT model was calibrated at the Rock Creek sub-watershed, validated at the Deer Creek sub-watershed, and verified at the Auburn sub-watershed and then at the entire Upper Wakarusa watershed for predicting daily flow, sediment, nutrients, total fecal bacteria, and source-specific fecal bacteria. Watershed characteristics for livestock, humans, and wildlife fecal bacterial sources were first modeled together then with three separate sources and combinations of source-specific FCB concentration: livestock and human, livestock and wildlife and human and wildlife. Model results indicated both coefficient of determination (R(2)) and Nash-Sutcliffe Efficiency Index (E) parameters ranging from 0.52 to 0.84 for daily flow and 0.50-0.87 for sediment (good to very good agreement); 0.14-0.85 for total phosphorus (poor to very good agreement); -3.55 to 0.79 for total nitrogen (unsatisfactory to very good agreement) and -2.2 to 0.52 for total fecal bacteria (unsatisfactory to good agreement). Model results generally determined decreased agreement for each single source of bacteria (R(2) and E range from -5.03 to 0.39), potentially due to bacteria source tracking (BST) uncertainty and spatial variability. This study contributes to new knowledge in bacteria modeling and will help further understanding of uncertainty that exists in source-specific bacteria modeling.

Field-Level Targeting Using SWAT: Mapping Output from HRUs to Fields and Assessing Limitations of GIS Input Data

Soil erosion from agricultural fields is a fundamental water quality and quantity concern throughout the U.S. Watershed models can help target general areas where soil conservation measures are needed, but they have been less effective at making field-level recommendations. The objectives of this study were to demonstrate a method of field-scale targeting using ArcSWAT and to assess the impact of topography, soil, land use, and land management source data on field-scale targeting results. The study was implemented in Black Kettle Creek watershed (7,818 ha) in south-central Kansas. An ArcGIS toolbar was developed to post-process SWAT hydrologic response unit (HRU) output to generate sediment yields for individual fields. The relative impact of each input data source on field-level targeting was assessed by comparing ranked lists of fields on the basis of modeled sediment-yield density (Mg ha-1) from each data-source scenario. Baseline data of field-reconnaissance land use and management were compared to NASS and NLCD data, 10 m DEM topography were compared to 30 m, and SSURGO soil data were compared to STATSGO. Misclassification of cropland as pasture by NASS and aggregation of all cropland types to a single category by NLCD led to as much as 75% and 82% disagreement, respectively, in fields identified as having the greatest sediment-yield densities. Neither NASS nor NLCD data include land management data (such as terraces, contour farming, or no-till), but such inclusion changed targeted fields by as much as 71%. Impacts of 10 m versus 30 m DEM topographic data and STATSGO versus SSURGO soil data altered the fields targeted as having the highest sediment-yield densities to a lesser extent (about 10% to 25%). SWAT results post-processed to field boundaries were demonstrated to be useful for field-scale targeting. However, use of incorrect source data directly translated into incorrect field-level sediment-yield ranking, and thus incorrect field targeting. Sensitivity was greatest for land use data source, followed closely by inclusion of land management practices, with less sensitivity to topographic and soil data sources.

Paying for sediment: Field-scale conservation practice targeting, funding, and assessment using the Soil and Water Assessment Tool

Watershed models have been widely used to estimate soil erosion and evaluate the effectiveness of conservation practices at different temporal and spatial scales; however, little progress has been made in applying these theoretical model results to the practical challenge of allocating conservation practice funding to meet specific soil loss objectives. Black Kettle Creek subwatershed (7,809 ha [19,295 ac]) of Little Arkansas River Watershed (360,000 ha [889,579 ac]) in south central Kansas was the focus of an innovative project to target conservation practice funding and pay directly for modeled sediment reduction. Detailed data (10 m [33 ft] digital elevation model topography, Soil Survey Geographic database soils, and a manually developed land use/land cover layer) were input into the Soil and Water Assessment Tool model, and the calibrated model was used to quantify soil erosion for each field. Effectiveness of locally relevant best management practices (BMPs) was simulated for each field. The simulated field-scale effectiveness for implemented BMPs ranged from 9% to 83% for single BMPs and 67% to 100% for selected combinations of BMPs. An in-field signup sheet was developed with field-specific sediment loss–based payments calculated for each BMP option. BMP implementation was 16.7% of cropland area prior (preinstalled BMPs) to the project, and 30.6% of cropland area (postinstalled BMPs) was added due to project-funded implementation. Postinstalled BMP implementation (47.3% of cropland) resulted in 35.8% sediment yield reduction compared to the no-BMPs scenario and 21.9% reduction compared to preinstalled BMP conditions, which was better than initially projected for this project. Inclusion of nontargeted fields and less-than-optimal BMPs had no influence on achieving soil loss objectives because payments were based on implemented soil loss rather than implemented area. Targeting of conservation practices based on payments scaled directly by project outcome (in this case, dollars per ton of sediment reduction) using a modeling approach allowed flexibility for both adopters (farmers) and funders (project staff) while assuring the project objective (i.e., sediment reduction) was met.

Atrazine, S-metolachlor, and isoxaflutole loss in runoff as affected by rainfall and management

Abstract Research was conducted to determine the effects of management practices and precipitation on herbicide loss in surface water runoff. A field runoff experiment was conducted in 1999 and 2000 in Manhattan, KS. Some plots received only natural precipitation, whereas others received natural precipitation plus additional precipitation from a rainfall simulator. Atrazine was applied at 0.9 and 1.8 kg ha−1, S-metolachlor at 0.7 and 1.4 kg ha−1, and isoxaflutole at 0.05 and 0.11 kg ha−1 to field corn grown under conventional tillage and no-till. Runoff volumes and herbicide concentrations were determined for each runoff event. Across all precipitation, tillage, and placement variables, atrazine, S-metolachlor, and isoxaflutole and diketonitrile (DKN) (soil metabolite of isoxaflutole), hereafter referred to as isoxaflutole/DKN, losses were similar at 5.0, 4.1, and 4.1% of applied, respectively. Additional precipitation increased runoff 2.5-, 2.2-, and 3.4-fold for atrazine, S-metolachlor, and isoxaflutole/DKN, respectively. Preplant soil incorporation reduced atrazine, S-metolachlor, and isoxaflutole/DKN losses in runoff by 67, 69, and 57%, respectively, compared with soil surface applications. Lower preplant rainfall in 2000 resulted in sharply reduced runoff losses despite postplant precipitation similar to that in 1999. These findings suggest that the best management practices for atrazine can be used to manage S-metolachlor and isoxaflutole/DKN loss in surface water runoff. Nomenclature: Atrazine; S-metolachlor; isoxaflutole; diketonitrile metabolite of isoxaflutole (DKN); 2-cyano-3-cyclopropyl-1-(2-methylsulfonyl-4-trifluoromethylphenyl)propan-1,3-dione; corn, Zea mays L.

Calibration of the APEX Model to Simulate Management Practice Effects on Runoff, Sediment, and Phosphorus Loss

Process-based computer models have been proposed as a tool to generate data for Phosphorus (P) Index assessment and development. Although models are commonly used to simulate P loss from agriculture using managements that are different from the calibration data, this use of models has not been fully tested. The objective of this study is to determine if the Agricultural Policy Environmental eXtender (APEX) model can accurately simulate runoff, sediment, total P, and dissolved P loss from 0.4 to 1.5 ha of agricultural fields with managements that are different from the calibration data. The APEX model was calibrated with field-scale data from eight different managements at two locations (management-specific models). The calibrated models were then validated, either with the same management used for calibration or with different managements. Location models were also developed by calibrating APEX with data from all managements. The management-specific models resulted in satisfactory performance when used to simulate runoff, total P, and dissolved P within their respective systems, with > 0.50, Nash-Sutcliffe efficiency > 0.30, and percent bias within ±35% for runoff and ±70% for total and dissolved P. When applied outside the calibration management, the management-specific models only met the minimum performance criteria in one-third of the tests. The location models had better model performance when applied across all managements compared with management-specific models. Our results suggest that models only be applied within the managements used for calibration and that data be included from multiple management systems for calibration when using models to assess management effects on P loss or evaluate P Indices.

New Methods in Modeling Sources Specific Bacteria at Watershed Scale Using SWAT

Fecal coliform bacteria contamination is one of the causes of water-quality impairments in surface waters which often result from the non-point source pollution, including grazing operations, failing septic system and wildlife. The Soil and Water Assessment Tool (SWAT) microbial sub-model 2005, was used to simulate the daily flows, total suspended solids, and fecal coliform bacteria concentrations in three grazed sub-watersheds (Auburn, Deer Creek, Rock Creek) of the Upper Wakarusa watershed in the northeast Kansas. The watershed characteristics for bacterial source, such as livestock, human, and wildlife, were modeled with four separate combinations to evaluate the source specific bacteria concentration at the outlet of the each watershed using modified deterministic probability of bacteria source tracking data.

Modeling Source-specific Fecal Coliform Bacteria using SWAT/Microbial sub-model

Water-quality impairments due to fecal bacteria in surface water often result from the non-point source pollution, including land-applied animal manure, septic tank failure, and grazing operations. Several models have been developed during the past two decades to model the movement of bacteria from agricultural land to the stream (MWASTE, COLI, SEDMOD, HSPF, and SWAT). Limited research has verified these models for simulating bacterial concentrations. All of these models simulate bacterial concentrations based on Chick’s Law; most assume steady-state conditions and are not process-based. The objective of this research is to calibrate and verify SWAT/Microbial sub-model using source specific bacterial data. The bacterial loadings for each source were calibrated separately using the fraction of animal or human contribution to bacterial samples based on source tracking analysis. The model was calibrated using coefficient of determination (R2) and Nash Sutcliffe Efficiency Index (EI) at the sub-watershed scale for each bacterial source. The results of this research was able to demonstrate the use of SWAT for modeling fecal bacteria and help refine procedures for estimating livestock and septic system input parameters.

Application timing impact on runoff losses of atrazine

Abstract Field experiments were conducted from 1996 to 2000 near Manhattan, KS, to determine the effects of application timing on atrazine loss in surface water runoff. In addition, Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) was run to compare simulated loss with actual loss in the field. Atrazine treatments were fall plus preemergence (FALL + PRE), early preplant plus PRE (EPP + PRE), PRE at a low rate (PRE-LOW), and PRE at a full (recommended) rate (PRE-FULL). Ridge-till furrows served as mini watersheds for the collection of surface water runoff. Water runoff volumes and herbicide concentrations were determined for each runoff event. Across four sampling years, mean atrazine runoff loss was 1.7, 4.3, and 1.7% of applied for FALL + PRE, EPP + PRE, and the mean of the PRE treatments, respectively. Thus, actual average losses from FALL + PRE and EPP + PRE treatments were somewhat higher than that predicted by GLEAMS. For PRE treatments, actual average losses were significantly lower than that predicted by GLEAMS, with measured losses falling below the bottom of the graph in 3 of 4 yr. These findings suggest that in certain parts of the Great Plains, FALL + PRE split applications of atrazine offer acceptably low atrazine runoff loss potential; EPP + PRE is more vulnerable to loss than FALL + PRE; and the GLEAMS model may overestimate atrazine runoff potential for PRE applications. Nomenclature: Grain sorghum, Sorghum bicolor L.; field corn, Zea mays L.

Monitoring and Estimating Ephemeral Gully Erosion using Field Measurements and GIS

Ephemeral gully (EG) erosion has been recognized as a major source of sediment in agricultural watersheds. Over the past few decades, soil erosion caused by sheet and rill erosion has been studied extensively, and field and watershed-scale models have been used to quantify contributions of sheet and rill erosion. In recent years, many studies have been conducted to understand EG formation, location and model development. The overall goal of this study was to develop a method to locate the potential EGs and further develop a simple model to estimate EG erosion at the watershed scale. To achieve this goal, several EGs were monitored, measured and overall characteristics described. Therefore, this paper focuses on monitoring and estimating sediment yields of few EGs in north eastern and south central Kansas.