Effects of Timber Harvesting and Related Management Practices on Water Quality in Forested Watersheds

The opening or removal of forest canopies during harvesting or land clearing results in a predictable sequence of responses, the descriptions of which appear remarkably similar around the world. Such activities are now widely acknowledged to have adverse impacts upon water quality and in-stream ecology. Sediment delivery, therefore, encapsulates the dominant process by which water resources are impacted and the process that can be best managed to limit off-site impacts. This paper is a review of current processes, and perceptions, of sediment delivery in managed forests. We outline the major components of sediment and runoff delivery as they relate specifically to timber harvesting activities. Whilst much existing research has focused upon soil loss as the major component of timber harvesting impacts, this review highlights both the need for, and benefits from, a conceptual advance in our thinking of sediment delivery. We advocate that by managing runoff delivery pathways and the resultant pattern of hydrological connectivity, we can limit the potential adverse effects of forest harvesting on in-stream water quality. Specific attention is given here to the interaction of the forest road and track network with both sediment and runoff delivery. The result is a comprehensive account of how best to manage timber harvesting for both on-site sustainability and off-site water resource protection.Key words: timber harvesting, sediment delivery, road network, connectivity, best management practices (BMPs).

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Postharvest Assessment of Vermont's Acceptable Silvicultural Management Practices and Water Quality Impacts

Northern Journal of Applied Forestry ◽

10.1093/njaf/8.4.140 ◽

1991 ◽

Vol 8 (4) ◽

pp. 140-144 ◽

Cited By ~ 2

Author(s):

David J. Brynn ◽

John C. Clausen

Keyword(s):

Water Quality ◽

Soil Erosion ◽

Management Practices ◽

Management Practice ◽

Woody Debris ◽

Transportation Network ◽

Erosion Control ◽

Timber Harvesting ◽

Silvicultural Management ◽

Soil Erosion Control

Abstract Seventy-eight recently completed timber harvesting operations in Vermont were evaluated for Acceptable Management Practice (AMP) compliance, soil erosion extent, and water quality impacts using a systematic, one-day examination of each site. Evaluations of water quality impacts and soil erosion were conducted on the portions of the transportation network and streams that could be most heavily affected by the timber harvesting operation. Increased stream sedimentation was observed on 46% of the operations with streams. Woody debris impacts occurred on 65% of the operations with streams. AMP compliance was over 90% for protective strip maintenance and stream avoidance, but soil erosion control practices on truck roads and skid trails commonly failed to meet AMP recommendations. Soil erosion was very limited on truck roads, skid trails, and log landings. Although the Vermont operations often only partially complied with the AMPs, minimal soil erosion and water quality impacts were observed. North. J. Appl. For. 8(4):140-144.

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A Survey of Nursery Water Quality Best Management Practices

HortScience ◽

10.21273/hortsci.33.3.501e ◽

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.

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Applying the Soil Water Assessment Tool to 5th Canadian Division Support Base Gagetown

Water Quality Research Journal ◽

10.2166/wqrjc.2014.012 ◽

2014 ◽

Vol 49 (4) ◽

pp. 372-385

Author(s):

Shawn Burdett ◽

Michael Hulley ◽

Andy Smith

Keyword(s):

Water Quality ◽

Soil Water ◽

Land Management ◽

Management Practices ◽

Assessment Tool ◽

Anthropogenic Activities ◽

Quality Model ◽

Sediment Yields ◽

Soil Water Assessment Tool ◽

Water Assessment

A hydrologic and water quality model is sought to establish an approach to land management decisions for a Canadian Army training base. Training areas are subjected to high levels of persistent activity creating unique land cover and land-use disturbances. Deforestation, complex road networks, off-road manoeuvres, and vehicle stream crossings are among major anthropogenic activities observed to affect these landscapes. Expanding, preserving and improving the quality of these areas to host training activities for future generations is critical to maintain operational effectiveness. Inclusive to this objective is minimizing resultant environmental degradation, principally in the form of hydrologic fluctuations, excess erosion, and sedimentation of aquatic environments. Application of the Soil Water Assessment Tool (SWAT) was assessed for its ability to simulate hydrologic and water quality conditions observed in military landscapes at 5th Canadian Division Support Base (5 CDSB) Gagetown, New Brunswick. Despite some limitations, this model adequately simulated three partial years of daily watershed outflow (NSE = 0.47–0.79, R2 = 0.50–0.88) and adequately predicted suspended sediment yields during the observation periods (%d = 6–47%) for one highly disturbed sub-watershed in Gagetown. Further development of this model may help guide decisions to develop or decommission training areas, guide land management practices and prioritize select landscape mitigation efforts.

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Pesticide Contamination of Groundwater in Virginia: BMP Impact Assessment

Water Science & Technology ◽

10.2166/wst.1993.0440 ◽

1993 ◽

Vol 28 (3-5) ◽

pp. 379-387 ◽

Cited By ~ 8

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.

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A storm water retrofit plan for the mimico creek watershed

Water Science & Technology ◽

10.2166/wst.1999.0539 ◽

1999 ◽

Vol 39 (12) ◽

pp. 133-140

Author(s):

J. Y. Li ◽

D. Banting

Keyword(s):

Water Quality ◽

Quality Management ◽

Great Lakes ◽

Management Practices ◽

Water Quality Management ◽

Storm Water ◽

Planning Tool ◽

Creek Watershed ◽

Urbanized Areas ◽

Goals And Objectives

Storm water quality management in urbanized areas remains a challenge to Canadian municipalities as the funding and planning mechanisms are not well defined. In order to provide assistance to urbanized municipalities in the Great Lakes areas, the Great Lakes 2000 Cleanup Fund and the Ontario Ministry of the Environment commissioned the authors to develop a Geographic Information System planning tool for storm water quality management in urbanized areas. The planning tool comprises five steps: (1) definition of storm water retrofit goals and objectives; (2) identification of appropriate retrofit storm water management practices; (3) formulation of storm water retrofit strategies; (4) evaluation of strategies with respect to retrofit goals and objectives; and (5) selection of storm water retrofit strategies. A case study of the fully urbanized Mimico Creek wateshed in the City of Toronto is used to demonstrate the application of the planning tool.

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

Water ◽

10.3390/w13111547 ◽

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.

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