Spatial and temporal variabilities of subsurface drainage in irrigated agriculture
AdvisorGuitjens, J. C.
Geological Sciences and Engineering
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In 1982, fifteen subsurface drains on 23 acres of irrigated agricultural land at Fallon, Nevada, were sampled in 27 consecutive weeks. The temporal and spatial variabilities of electrical conductivity (EC), temperature, pH, dissolved oxygen (DO), and nitrate nitrogen (NO^-N) were evaluated using time series and geostatistical analyses. An autocorrelation function (ACF) was used to evaluate temporal and spatial variations of each parameter. Results indicate that the 11-week, 3-week, 8-week, 9-week, and 11-week periods are the maximum sampling temporal intervals for EC, temperature, pH, DO, and NO^-N, respectively. In addition, the sampling spatial interval of 120 feet is too wide for EC, DO, and NO^-N. A shorter distance should be considered in future studies. The maximum sampling spatial intervals for temperature and pH are 36O feet and 120 feet, respectively. Knowledge of the optimum spacing provides important information in the design of efficient sampling strategies. The semivariogram function was also used to evaluate temporal and spatial variations of each parameter. Results indicate that the 19-week, 50-week, 11-week, 17-week, and 11-week periods are the maximum sampling temporal intervals for EC, temperature, pH, DO, and NO^-N, respectively. In addition, the maximum sampling spatial intervals for EC, temperature, pH, DO, and NO^-N are 600 feet, 450 feet, 920 feet, 490 feet, and 648 feet, respectively. The Autoregressive Integrated Moving Average (ARIMA) and kriging models for temporal and spatial series were established for each parameter through the Box-Jenkins time domain modeling processes and kriging modeling processes, respectively. The precision of the forecasts were tested using after-the-fact forecast procedures. These models can he used for various purposes such as forecasting future temporal and spatial values and determining the transfer function and co-kriging models which provide a way to relate water management plans with water quality control.
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irrigated agricultural land
times series analysis
sampling temporal intervals
autoregressive integrated moving average
Box-Jenkins time domain modeling processes
after-the-fact forecast procedures
water management plans
water quality control