Towards effective control of deep drainage under border-check irrigated pasture in the Murray-Darling Basin: a review

2004 ◽  
Vol 55 (5) ◽  
pp. 485 ◽  
Author(s):  
Matthew Bethune
Keyword(s):  
Irrigation System ◽  
Temporal Trends ◽  
Dairy Industry ◽  
Effective Control ◽  
Irrigation Season ◽  
Deep Drainage ◽  
Winter Rainfall ◽  
Murray Darling Basin ◽  
Spatial And Temporal Trends ◽  
Drainage Data

High watertables and land salinisation threaten the sustainability and prosperity of the irrigated dairy industry in the Murray–Darling Basin of Australia. High watertables and salinisation result from excessive deep drainage. Spatial and temporal trends in deep drainage data pertinent to the dairy industry in the Murray–Darling Basin are reviewed with a view towards reducing deep drainage. The reviewed data indicate that deep drainage under border-check irrigated pasture is generally greater than the leaching requirement and can be reduced without affecting pasture growth. Deep drainage through levee soils is likely to contribute significantly to regional groundwater. Consideration needs to be given to the appropriateness of using the border-check irrigation system on levee soils. Pressurised irrigation systems or land use change may be required to reduce deep drainage under these soils. The majority of deep drainage under floodplain soils occurs during winter and spring, when rainfall exceeds pasture water use. Developing a soil water deficit prior to winter will reduce deep drainage. This can be achieved by ending the irrigation season earlier. A later start to the irrigation season also offers potential to divert more winter rainfall into evapotranspiration rather than deep drainage.

Simulating the water balance of border-check irrigated pasture on a cracking soil

2004 ◽  
Vol 44 (2) ◽  
pp. 163 ◽  
Author(s):  
M. Bethune ◽  
Q. J. Wang
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Water Storage ◽  
Dairy Industry ◽  
Soil Water Storage ◽  
Deep Drainage ◽  
Annual Crop ◽  
Murray Darling Basin ◽  
Physically Based ◽  
Soil Cracks

The irrigated dairy industry relies on perennial pasture and is a major user of water in the Murray–Darling Basin of Australia. The sustainability of the irrigated dairy industry is threatened by high watertables and land salinisation. Options to alleviate these problems by reducing deep drainage are required. This paper assesses the potential to use the simulation model 'SWAP' to appraise options for reducing deep drainage. Minor modifications were made to SWAP so that it could simulate border-check irrigated pasture on a cracking soil. The model was tested against lysimeter data describing the water balance of irrigated pasture. Evapotranspiration, runoff, infiltration, soil water storage and deep drainage were well simulated when infiltration through soil cracks was modelled using the physically based approached in SWAP. Large errors in evapotranspiration, infiltration, runoff, soil water storage and deep drainage occurred when the process of infiltration through cracks was not simulated. Slight improvements in model predictions were achieved by specifying monthly crop factors, as opposed to a constant annual crop factor. However, a constant annual crop factor should be sufficiently accurate for most studies of deep drainage under border-check irrigated pastures.

Spatial and temporal trends and effects of contaminants in the Canadian Arctic marine ecosystem: a review

1999 ◽  
Vol 230 (1-3) ◽  
pp. 83-144 ◽  
Author(s):  
D Muir ◽  
B Braune ◽  
B DeMarch ◽  
R Norstrom ◽  
R Wagemann ◽  
...  
Keyword(s):  
Marine Ecosystem ◽  
Temporal Trends ◽  
Canadian Arctic ◽  
Spatial And Temporal Trends

scholarly journals Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

2021 ◽  
Vol 18 (16) ◽  
pp. 8259
Author(s):  
Sarah L. Jackson ◽  
Sahar Derakhshan ◽  
Leah Blackwood ◽  
Logan Lee ◽  
Qian Huang ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
Explanatory Power ◽  
Temporal Trends ◽  
The United States ◽  
First Year ◽  
Significant Case ◽  
Urban Rural ◽  
Spatial And Temporal Trends ◽  
Mitigation Policies

This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020–January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban–rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban–rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

scholarly journals Nitrogen Balance of Effluent Irrigated Silage Cropping Systems in Southern Australia

2001 ◽  
Vol 1 ◽  
pp. 35-41
Author(s):  
Chris J. Smith ◽  
Val O. Snow ◽  
Ray Leuning ◽  
David Hsu
Keyword(s):  
Soil Profile ◽  
Irrigation System ◽  
Cropping Systems ◽  
Irrigation Season ◽  
Alkaline Ph ◽  
High Recovery ◽  
Mineral N ◽  
Effluent Reuse ◽  
Spray Irrigation ◽  
N Loading

The nitrogen (N) balance in a double-cropped, effluent spray irrigation system was examined for several years in southern Australia. The amounts of N added by irrigation, removed in the crop, and lost by ammonia (NH3) volatilisation, denitrification, and leaching were measured. Results from the project provide pig producers with the knowledge necessary to evaluate the efficiency of such systems for managing N, and enable sustainable effluent reuse practices to be developed. Oats were grown through the winter (May to November) without irrigation, and irrigated maize was grown during the summer/autumn (December to April). Approximately 18 mm of effluent was applied every 3 days. The effluent was alkaline (pH 8.3) and the average ammoniacal-N (NH4++ NH3) concentration was 430 mg N/l (range: 320 to 679 mg N/l). Mineral N in the 0- to 1.7-m layer tended to increase during the irrigation season and decrease during the winter/spring. About 2000 kg N/ha was found in the profile to a depth of 2 m in October 2000. N removed in the aboveground biomass (oats + maize) was 590 and 570 kg N/ha/year, equivalent to ≈25% of the applied N. Average NH3volatilisation during the daytime (6:00 to 19:00) was 2.74 kg N/ha, while volatilisation at night (19:00 to 6:00) was 0.4 kg N/ha, giving a total of 3.1 kg N/ha/day. This represents ≈12% of the N loading, assuming that these rates apply throughout the season. The balance of the N accumulated in the soil profile during the irrigation season, as 15N-labelled N studies confirmed. The high recovery of the15N-labelled N, and the comparable distribution of 15N and Br in the soil profile, implied that there was little loss of N by denitrification, even though the soil was wet enough for leaching of both tracers.

scholarly journals Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2737
Author(s):  
Leandro Ordonez-Ante ◽  
Gregory Van Seghbroeck ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Temporal Trends ◽  
Multidimensional Data ◽  
Data Sets ◽  
Distributed Data ◽  
Key Factors ◽  
Distributed Data Processing ◽  
Spatial And Temporal Trends ◽  
Query Latency

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

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