February 2010, Vol. 22, No. 2
Inefficiencies of Smart Irrigation Systems Revealed
Smart irrigation controllers are not as efficient as many think. The controllers, which use weather data or evapotranspiration (ET) data to automatically adjust the amount of water applied for irrigation, have been studied at a smart-controller testing facility established by the Irrigation Technology Center at Texas A&M University (College Station).
According to the report, Evaluation of Smart Irrigation Controllers, the research was conducted because some cities in Texas are now mandating the use of smart controllers for more-efficient irrigation, a university news release says. In 2008, the Irrigation Technology Center began a program to test smart controllers, beginning with six controllers donated by different manufacturers that market their products in the state. The controllers were programmed with local landscape information and used for 8 weeks.
Researchers found that the amount of water used by the controllers varied significantly, even for the same zones, and all of the controllers exceeded the TexasET Network irrigation recommendations, according to the release. The network, a project of the Irrigation Technology Center and administered by Texas A&M and the U.S. Department of Agriculture, provides weather information, current and average ET data, and watering recommendations for the state.
The smart controllers tested applied approximately 0.3 to 2.5 times more water than recommended, according to Charles Swanson, an associate in the Texas A&M Department of Biological and Agricultural Engineering.
Possible causes for over-irrigation include improper ET values and insufficient accounting for rainfall from inaccurate source information or calculations, the report says. There are several methods to calculate ET, and those that factor in solar radiation are more accurate, the news release says.
Onsite sensor controllers use weather data, such as temperature, rainfall, solar radiation, wind speed, and relative humidity to determine landscape water requirements, while ET controllers use ET data acquired by the Internet, telephone, or pager to estimate landscape water requirements.
While all of the controllers tested produced excessive irrigation, the results indicate that sensor controllers produced less excess irrigation than the ET controllers. The news releases reports that onsite sensor controllers applied, on average, 70% less water than the ET controllers and saved water, compared to most manual applications.
While the smart sensors are promising, the technology should be upgraded, Swanson said in the news release. Newer versions of two of the controllers have been placed on the market since the beginning of the research, requiring ongoing testing and evaluation. The report calls for additional testing to verify the initial results and describes a plan for working with manufacturers to design a testing protocol and evaluate the values used to define site parameters. Future analysis will include evaluation of day-to-day root-zone soil–water balance and multicycling performed to prevent runoff, the report says.
Forest Harvesting Model Protects Waterways
Forest harvesting often hurts the quality of local waterways, but a new model for planning and timing of forest harvests minimizes those negative effects. The researchers who developed this model tested it on a watershed in northern Sweden.
The modellooked at the downstream dissolved organic carbon (DOC) concentrations produced from cumulative forest clear-cuts over time, according to a news release from Allen Press Inc. (Lawrence, Kan.).A report on this study is featured in the November 2009 issue ofAMBIO: A Journal of the Human Environment, published by the Royal Swedish Academy of Sciences (Stockholm).
The report says the hypothesis was “that by optimizing the distribution and timing of forest harvesting activities over a rotation period in a large catchment, the combined downstream effect in sensitive stream locations could be minimized.”
The model was designed to maximize the value of timber from future harvesting and factored-in forest constraints, runoff, and DOC levels. Users can specify a maximum DOC concentration increase for any location along the stream. Other water quality parameters also could be incorporated, the report says.
The model was tested on the Krycklan watershed, which covers 6780 ha (16,754 ac) of productive forest land in northern Sweden. According to the report, results indicate that DOC concentrations could cause “considerable negative effects on streams, making them unsuitable habitats for many species” if not considered in planning models in the future.
Many factors affect the water quality in rivers, including forestry activities, areas of wetland, climate change, and forest species composition. Therefore, assessing the effects of DOC requires an understanding of the biological, hydrological, and chemical processes at work in a watershed, the report says.
The main weakness of the model, according to the report, is that it may not completely simulate the forest hydrological process because of the variety of factors that come into play.
The model enables users to assign different values in different parts of the watershed to capture changes in parameters throughout the watershed, but the impact of forestry on different land types has not been quantified yet. “The major focus of this study was to formulate a first conceptual model incorporating consideration of DOC concentration over time and space, which could be included in a traditional forest planning model,” the report says.
Potential uses for the model include locating suitable harvesting areas, validating existing information, and determining future effects to the landscape under a certain management plan. The model could be used as an alternative to legislative harvesting restrictions and could be used for long-term forest management, according to the report.
The model, based on a mixed-integer programming model, can be included in traditional forest planning systems and can incorporate global constraints to help an analyst make trade-offs between timber production and water quality levels, the report says. The model enables forest managers to generate a range of plans and select the one most appropriate in the long run.
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