DRAINMOD-P: A Model for Simulating Phosphorus Dynamics and Transport in Drained Agricultural Lands: I. Model Development

2021 ◽  
Vol 64 (6) ◽  
pp. 1835-1848
Author(s):  
Manal H. Askar ◽  
Mohamed A. Youssef ◽  
Peter A. Vadas ◽  
Dean L. Hesterberg ◽  
Aziz Amoozegar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Erosion ◽  
Best Management Practices ◽  
Management Practices ◽  
Subsurface Drainage ◽  
Water Quality Modeling ◽  
List Type ◽  
Macropore Flow ◽  
Drainage Systems ◽  
P Cycling

HighlightsDRAINMOD-P has been developed to simulate phosphorus (P) dynamics in drained croplands.Key hydrological and biochemical processes affecting P cycling are represented in the model.The model predicts surface and subsurface P losses as affected by weather, soil, and management factors.Abstract. High phosphorus (P) loads to streams and lakes can promote harmful algae blooms and cause water quality deterioration. Recent research has identified subsurface drainage as an important pathway for the transport of dissolved P from drained croplands to receiving surface water bodies, particularly when macropore flow contributes a considerable portion of the subsurface drainage outflow. Currently, a few models are capable of simulating P dynamics in poorly drained soils with artificial drainage systems. The objective of this study was to develop DRAINMOD-P, a field-scale, process-based model that simulates P cycling and transport in drained croplands. Processes represented in the model include atmospheric deposition, organic and inorganic fertilizer applications, plant uptake, sediment-bound and dissolved P losses in both surface runoff and subsurface drainage, tillage practices, and P mineralization and immobilization. The model predicts P losses under different management practices, climatic conditions, drainage systems, and crop rotations. The model is an extension to the nitrogen model DRAINMOD-NII, with full integration of the nitrogen and P model components. DRAINMOD-P uses the recently modified hydrology component that simulates macropore flow. A soil erosion component, based on the RUSLE approach, has been incorporated into the model to estimate sediment loss and associated particulate P loss. Sediment deposition in tile drains is considered to quantify particulate P settling in the drainage system. In this article, we review the approaches used in DRAINMOD-P for simulating P-related processes. Model testing against field-measured data from a subsurface-drained field in northwest Ohio is presented in a companion article. Keywords: Best management practices, Phosphorus model, Phosphorus processes, Soil erosion, Water quality modeling.

Water quality modeling to evaluate BMPs in rice paddies

2006 ◽  
Vol 53 (2) ◽  
pp. 253-261 ◽  
Author(s):  
J.H. Jeon ◽  
C.G. Yoon ◽  
H.S. Hwang ◽  
K.W. Jung
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Nutrient Concentration ◽  
Management Practices ◽  
Nutrient Loading ◽  
Water Quality Modeling ◽  
Rice Paddies ◽  
Quality Model ◽  
Dirac Delta ◽  
Surface Drainage

A water quality model applicable to rice paddies was developed using field data from 1999–2002. Use of the Dirac delta function efficiently explained the nutrient-concentration characteristics of ponded water. The model results agreed reasonably well with the observed data. The ponded-water quality was influenced primarily by fertilization; nutrient concentration was especially high during early cultivation periods. Reducing surface drainage during the fertilization period may substantially reduce nonpoint source loading from paddies. Increased weir heights and shallow irrigation methods were evaluated by the model as practical methods for reducing nutrient loading from paddies. These methods were effective in reducing surface drainage and are suggested as “best management practices” (BMPs) if applied based on site-specific paddy conditions.

scholarly journals Comparison of the presence and partitioning behavior of indicator organisms and Salmonella spp. in an urban watershed

2009 ◽  
Vol 8 (1) ◽  
pp. 44-59 ◽  
Author(s):  
Leigh-Anne H. Krometis ◽  
Gregory W. Characklis ◽  
Patricia N. Drummey ◽  
Mark D. Sobsey
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Management Practices ◽  
Water Quality Modeling ◽  
Fecal Coliforms ◽  
Urban Watershed ◽  
Indicator Organisms ◽  
Salmonella Spp ◽  
Transport Behavior ◽  
Partitioning Behavior

The appropriateness of indicator organisms as surrogates for human pathogens in water quality modeling is dependent on similarities in both presence and transport behavior; however, very little data relating indicator and bacterial pathogen transport behavior in receiving waters is available. In this study observations of presence, partitioning behavior (i.e. association with settleable particles) and removal by upland detention basins were used to assess the suitability of six indicator organisms as surrogates for Salmonella spp. bacteria in an urban watershed. The fecal indicator bacteria (fecal coliforms, E. coli and enterococci) were most closely correlated with Salmonella in terms of presence and partitioning behavior (25–35% associated with settleable particles on average); however, further resolution of the variability associated with Salmonella partitioning is required. Higher removal of particle-associated microbes relative to the total microbial concentration by the detention ponds suggests that sedimentation may be an important removal mechanism. However, large fluctuations in pond performance between storm events and occasional net microbial exports in effluents indicate that these best management practices (BMPs), as currently implemented, will be unlikely to achieve water quality objectives.

Modeling Water Quality and the Effects of Agricultural Best Management Practices in the Iowa River Basin

1985 ◽  
Vol 17 (6-7) ◽  
pp. 1141-1153 ◽  
Author(s):  
B. R. Bicknell ◽  
Anthony S. Donigian ◽  
T. A. Barnwell
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Best Management Practices ◽  
Management Practices ◽  
Agricultural Runoff ◽  
Large River ◽  
Water Quality Modeling ◽  
Hydrological Simulation ◽  
Best Management ◽  
Creek Watershed

This paper describes a demonstration application of comprehensive hydrology and water quality modeling on a large river basin to evaluate the effects of agricultural nonpoint pollution and proposed best management practices (BMP). The model application combines detailed simulation of agricultural runoff and soil processes, including calculation of surface and subsurface pollutant transport to receiving water, with subsequent simulation of instream transport and transformation. The result is a comprehensive simulation of river basin water quality. The investigation of the Iowa River Basin described in this paper was part of a large study which included application and evaluation of the Hydrological Simulation Program - FORTRAN (HSPF) to both the data-intensive Four Mile Creek watershed and the Iowa River above Coralville Reservoir. In this study, the methodology developed on Four Mile Creek was extrapolated to the Iowa River Basin to demonstrate its applicability and functionality on a large river basin. Many model parameter values from Four Mile Creek were applied directly to the study area without adjustment while other parameters were modified based on available information and calibration. This study allowed the exploration of problems associated with modeling hydrology, sediment, and chemical fate and transport in a large river basin with varying meteorologic conditions, soils, and agricultural practices.

A Survey of Nursery Water Quality Best Management Practices

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 501e-502
Author(s):  
Cody J. White ◽  
Michael A. Schnelle ◽  
Gerrit W. Cuperus
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Management Practices ◽  
Cultural Practices ◽  
Plant Materials ◽  
Best Management ◽  
Employee Safety ◽  
Current Water ◽  
Management Approaches ◽  
Environmentally Sound

A survey was designed to assess high-risk areas with respect to environmental contamination, specifically how it relates to water quality. Oklahoma growers of all economic levels, retail and/or wholesale, were queried at their place of business for their current state of implementing best management practices (BMPs) and other strategic actions that could potentially affect current and future water quality standards. Specific areas such as the physical environment of the nursery, primary pesticides and fertilizers used, Integrated Pest Management (IPM) practices, and employee safety training were covered as well as other aspects germane to preserving and protecting current water quality and related environmental issues. More than 75 nurseries were surveyed and given the opportunity to participate in future training at Oklahoma State Univ. Results indicated that nurseries have not fully implemented many BMPs, but have adopted fundamental IPM approaches. The stage is set for the implementation of the next phase of expansion and refinement into ecologically based programs such as propagation and sale of low pesticide input plant materials, improved cultural practices, and the integration of environmentally sound management approaches. As an example, many growers are in the process of phasing out calendar-based pesticide application programs in favor of aesthetic and/or economic threshold-driven pesticide spray programs.

Pesticide Contamination of Groundwater in Virginia: BMP Impact Assessment

1993 ◽  
Vol 28 (3-5) ◽  
pp. 379-387 ◽  
Author(s):  
S. Mostaghimi ◽  
P. W. McClellan ◽  
R. A. Cooke
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Best Management Practices ◽  
Water Samples ◽  
Management Practices ◽  
Water Quality Monitoring ◽  
Pesticide Contamination ◽  
Cropping Patterns ◽  
Creek Watershed ◽  
Surface And Groundwater

The Nomini Creek Watershed/Water Quality monitoring project was initiated in 1985, as part of the Chesapeake Bay Agreement of 1983, to quantify the impacts of agricultural best management practices (BMPs) on improving water quality. The watershed monitoring system was designed to provide a comprehensive assessment of the quality of surface and groundwater as influenced by changes in land use, agronomic, and cultural practices in the watershed over the duration of the project. The primary chemical characteristics monitored include both soluble and sediment-bound nutrients and pesticides in surface and groundwater. Water samples from 8 monitoring wells located in agricultural areas in the watershed were analyzed for 22 pesticides. A total of 20 pesticides have been detected in water samples collected. Atrazine is the most frequently detected pesticide. Detected concentrations of atrazine ranged from 0.03 - 25.56 ppb and occurred in about 26 percent of the samples. Other pesticides were detected at frequencies ranging from 1.6 to 14.2 percent of all samples collected and concentrations between 0.01 and 41.89 ppb. The observed concentrations and spatial distributions of pesticide contamination of groundwater are compared to land use and cropping patterns. Results indicate that BMPs are quite effective in reducing pesticide concentrations in groundwater.

scholarly journals Prioritization of Sub-Watersheds to Sediment Yield and Evaluation of Best Management Practices in Highland Ethiopia, Finchaa Catchment

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 650
Author(s):  
Wakjira Takala Dibaba ◽  
Tamene Adugna Demissie ◽  
Konrad Miegel
Keyword(s):  
Soil Erosion ◽  
Best Management Practices ◽  
Sediment Yield ◽  
Soil Loss ◽  
Crop Production ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Sediment Yields ◽  
Soil And Water

Excessive soil loss and sediment yield in the highlands of Ethiopia are the primary factors that accelerate the decline of land productivity, water resources, operation and function of existing water infrastructure, as well as soil and water management practices. This study was conducted at Finchaa catchment in the Upper Blue Nile basin of Ethiopia to estimate the rate of soil erosion and sediment loss and prioritize the most sensitive sub-watersheds using the Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and validated using the observed streamflow and sediment data. The average annual sediment yield (SY) in Finchaa catchment for the period 1990–2015 was 36.47 ton ha−1 yr−1 with the annual yield varying from negligible to about 107.2 ton ha−1 yr−1. Five sub-basins which account for about 24.83% of the area were predicted to suffer severely from soil erosion risks, with SY in excess of 50 ton ha−1 yr−1. Only 15.05% of the area within the tolerable rate of loss (below 11 ton ha−1yr−1) was considered as the least prioritized areas for maintenance of crop production. Despite the reasonable reduction of sediment yields by the management scenarios, the reduction by contour farming, slope terracing, zero free grazing and reforestation were still above the tolerable soil loss. Vegetative contour strips and soil bund were significant in reducing SY below the tolerable soil loss, which is equivalent to 63.9% and 64.8% reduction, respectively. In general, effective and sustainable soil erosion management requires not only prioritizations of the erosion hotspots but also prioritizations of the most effective management practices. We believe that the results provided new and updated insights that enable a proactive approach to preserve the soil and reduce land degradation risks that could allow resource regeneration.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

Water Quality Benefits of "Shallow" Subsurface Drainage Systems

2006 ◽  
Author(s):  
Gary R. Sands ◽  
Inhong Song ◽  
Lowell M. Busman ◽  
Bradley Hansen
Keyword(s):  
Water Quality ◽  
Subsurface Drainage ◽  
Drainage Systems ◽  
Shallow Subsurface
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