James C. Ascough

The WEPP Watershed Model : I. Hydrology and Erosion

The Water Erosion Prediction Project (WEPP) watershed scale model is a continuous simulation tool that extends the capability of the WEPP hillslope model to provide erosion prediction technology for small cropland and rangeland watersheds. The model is based on fundamentals of erosion theory, soil and plant science, channel flow hydraulics, and rainfall-runoff relationships, and contains hillslopes, channels, and impoundments as the primary components. The hillslope and channel components can be further divided into hydrology and erosion components. Channel infiltration is calculated by a Green-Ampt Mein-Larson infiltration equation. A continuous channel water balance is maintained, including calculation of evapotranspiration, soil water percolation, canopy rainfall interception, and surface depressional storage. The channel peak runoff rate is calculated using either a modified Rational equation or the equation used in the CREAMS model. Flow depth and hydraulic shear stress along the channel are computed by regression equations based on a numerical solution of the steady state spatially varied flow equations. Detachment, transport, and deposition within constructed channels or concentrated flow gullies are calculated by a steady state solution to the sediment continuity equation. The impoundment component routes runoff and sediment through several types of impoundment structures, including farm ponds, culverts, filter fences, and check dams. The purpose of this article is to provide an overview of the model conceptual framework and structure. In addition, detailed mathematical representations of the processes simulated by the channel hydrology and erosion components are presented. The processes simulated by the impoundment component are not described in this article, but it does include impoundment effects on watershed model channel peak discharge and time of concentration calculations.

A software engineering perspective on environmental modeling framework design: The Object Modeling System

The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to address this problem, but much work remains before EMFs are adopted as mainstream modeling tools. Environmental model development requires both scientific understanding of environmental phenomena and software developer proficiency. EMFs support the modeling process through streamlining model code development, allowing seamless access to data, and supporting data analysis and visualization. EMFs also support aggregation of model components into functional units, component interaction and communication, temporal-spatial stepping, scaling of spatial data, multi-threading/multi-processor support, and cross-language interoperability. Some EMFs additionally focus on high-performance computing and are tailored for particular modeling domains such as ecosystem, socio-economic, or climate change research. The Object Modeling System Version 3 (OMS3) EMF employs new advances in software framework design to better support the environmental model development process. This paper discusses key EMF design goals/constraints and addresses software engineering aspects that have made OMS3 framework development efficacious and its application practical, as demonstrated by leveraging software engineering efforts outside of the modeling community and lessons learned from over a decade of EMF development. Software engineering approaches employed in OMS3 are highlighted including a non-invasive lightweight framework design supporting component-based model development, use of implicit parallelism in system design, use of domain specific language design patterns, and cloud-based support for computational scalability. The key advancements in EMF design presented herein may be applicable and beneficial for other EMF developers seeking to better support environmental model development through improved framework design.

Integrating system modeling with field research in agriculture: applications of the root zone water quality model (RZWQM)

Publisher Summary Models provide a ready means of translating research to other locations and thus minimize duplication of field research. They also provide a ready means to transfer the integrated knowledge and technology to farmers and other users. This chapter focuses on root zone water quality model (RZWQM), an agricultural system model that integrates the state-of-the-science knowledge of agricultural systems into a tool for agricultural research and management, environmental assessment, and technology transfer. RZWQM has been tested for different aspects of water movement, several pesticides, crop growth, nitrogen dynamics, and several agricultural management practices. The model has been used nationally and internationally and the degree of success in RZWQM application depends on the agricultural system simulated, data availability and quality, processes of interest, and, to some extent, the modeling experience of users. Both the successes and failures of the model have provided information to improve the model and data collection. During these numerous calibration and validation exercises, many new ideas have been developed on model application. Moreover, RZWQM applications have furthered understanding of agricultural systems and promoted the integration of models with field research.

The Water Erosion Prediction Project (WEPP) Model

Soil erosion by water continues to be a serious problem throughout the world, and models play an increasingly critical role in conservation and assessment efforts. Improved soil erosion prediction technology is needed to provide land managers, conservationists and others with tools to examine the impact of different land management decisions on on-site soil loss and off-site sediment yield and determining optimal land use. Additionally, soil erosion prediction technology allows policymakers to assess the current status of land resources and the potential need for enhanced or new policies to protect soil and water resources.

Root Zone Water Quality Model sensitivity analysis using Monte Carlo simulation.

Performing a sensitivity analysis for a mathematical simulation model is helpful in identifying key model parameters and simulation errors resulting from parameter uncertainty. The Root Zone Water Quality Model (RZWQM) has been evaluated for many years, however, detailed sensitivity analyses of the model to various agricultural management systems and their representative input parameters are lacking. This study presents results of RZWQM output response sensitivity to selected model input parameters. Baseline values for the parameters were measured for an experiment on a manured corn field in eastern Colorado. Four groups of model input parameters (saturated hydraulic conductivity, organic matter/nitrogen cycling, plant growth, and irrigation water/manure application rates) were selected and three model output responses (plant nitrogen uptake, silage yield, and nitrate leaching) were used to quantify RZWQM sensitivity to selected model input parameters. A modified Monte Carlo sampling method (Latin Hypercube Sampling) was used to obtain parameter sets for model realizations. The model parameter sets were then analyzed separately using linear regression analysis. In general, RZWQM output responses were most sensitive to plant growth input parameters and manure application rates. The plant nitrogen uptake and silage yield model output responses were less sensitive to nitrogen cycling and irrigation rate input parameters than those observed in previous field experiments. This finding may warrant further study on the effects of water and nitrogen stresses on crop growth. Finally, the results showed that model output responses were more sensitive to the average saturated hydraulic conductivity of the entire soil profile than to the saturated hydraulic conductivity of individual soil layers.

FARM COMPUTER ADOPTION IN THE GREAT PLAINS

Computers change rapidly, yet the last survey on computer use in agriculture was in 1991. We surveyed Great Plains producers in 1995 and used logit analysis to characterize adopters and non-adopters. About 37% of these producers use computers which is consistent with the general population. We confirmed previous surveys emphasizing the importance of education, age/experience, and other farm characteristics on adoption. However, we also found that education and experience may no longer be a significant influence. Future research and education could focus on when and where computers are most needed, and therefore when adoption is most appropriate.

THE WEPP WATERSHED MODEL: II. SENSITIVITY ANALYSIS AND DISCRETIZATION ON SMALL WATERSHEDS

The Water Erosion Prediction Project (WEPP) watershed scale model was developed by the USDA for purposes of erosion assessment and conservation planning. The purpose of this study was to verify that the watershed model behaves rationally and consistently over a range of discretization structures and channel parameter inputs for applications to small watersheds. Effects of watershed discretization were evaluated for selected events within a one-year continuous simulation by comparing results for two watersheds under various discretization schemes. Impacts of channel input parameters were assessed by comparing the value of a linear sensitivity coefficient for user-specified parameters. Hillslope length, Manning’s coefficients, and channel slope were found to be key parameters in the prediction of watershed sediment yields. Erodibility and critical shear stress were found to be important for events where channel scour was active, and the results were sensitive to the hydraulic conductivity for events with small runoff and small sediment contributions from hillslopes. Improvements in the WEPP model are suggested where limitations were observed.

Simulated rainfall study for transport of veterinary antibiotics-mass balance analysis.

Occurrence of human and veterinary antibiotics has been reported in various environmental compartments. Yet, there is a lack of information verifying the transport mechanisms from source to environment, particularly the transport of veterinary antibiotics as a non-point source pollutant. A rainfall simulation study was conducted to address surface runoff as a possible transport mechanism of veterinary antibiotics introduced in the field and mass balance was calculated with supplementary surface and depth soil measurement. Seven veterinary antibiotics that are the most abundantly used in agriculture for therapeutic and non-therapeutic (growth-promotion) purposes were examined in this study, including tetracycline (TC), chlortetracycline (CTC), sulfathiazole (STZ), sulfamethazine (SMZ), erythromycin (ETM), tylosin (TYL), and monensin (MNS). Runoff in aqueous and sediment phases was collected every 5 min for 1h with varied rainfall intensity and additional surface (0-2 cm) and depth (2-30 cm) soil samples were collected after rainfall simulation for mass balance analysis. Quantification of antibiotic concentration in all collected samples was based on solid phase extraction (SPE) followed by measurement with high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS). MNS showed the highest concentration in runoff aqueous samples (0.22 mg plot(-1)), while ETM showed the highest concentration in runoff sediment samples (0.08 mg plot(-1)). The highest concentration of each applied antibiotic in surface soil samples occurred at different locations. This result might indicate the mobility of these compounds in surface soil varies due to different physicochemical properties among the antibiotics. Further, the analysis results showed that all of the subject antibiotics had penetrated into the subsurface; yet, no residuals were found for STZ, suggesting this compound might have penetrated even deeper into the soil. These results indicate that aqueous or sediment erosion control might reduce the transport of veterinary antibiotics in the environment.

Computer use in agriculture: an analysis of Great Plains producers☆

Computers have changed a great deal in the past decade, yet the last survey of computer use in agriculture was performed in 1991. Furthermore, previous computer use surveys are not very extensive in coverage. In the summer and fall of 1996, we conducted a random survey of Great Plains producers. The purpose of the survey was to examine three questions: (1) who adopts computers and what are they and their farms like; (2) what are the characteristics of non-adopters; and (3) what tasks do producers want computers to perform? Our results confirmed that most of the variables earlier studies identified as influential on computer adoption still had an impact. These included farm size (acres and sales), ownership of livestock, farm tenure, and off-farm employment exposure to computer use. We found some question as to whether age or experience is a better predictor of computer adoption. Moreover, there also appears to be reason to question whether education has a significant impact on adoption.

THE WEPP WATERSHED MODEL: III. COMPARISONS TO MEASURED DATA FROM SMALL WATERSHEDS

The Water Erosion Prediction Project (WEPP) watershed scale model was developed by the USDA for purposes of erosion assessment and conservation planning. The purpose of this study was to evaluate the WEPP watershed model applicability and prediction accuracy for small watersheds (0.34-5.14 ha) under different climate, topography, soil, and management regimes. No calibration was conducted to obtain the results. Only default model parameters were used. Data from 15 watersheds in six U.S. locations were compared to runoff and sediment yield estimates using WEPP95. The r2 values between measured and predicted total runoff and sediment yield for the 15 watersheds were 0.86 and 0.91, respectively. The r2 between measured and predicted event data for individual watersheds ranged from 0.01 to 0.85 for runoff and from 0.02 to 0.90 for sediment. Cumulative frequency distributions for predicted values of event runoff and sediment matched those for measured values with some exceptions. Improvements in the WEPP model are suggested where limitations were observed.