Location: Water Reuse and Remediation Research
Project Number: 2036-61000-016-00-D
Project Type: In-House Appropriated
Start Date: Dec 8, 2011
End Date: Dec 7, 2016
Objective 1: Evaluate a multi-sensor platform for salinity and irrigation management for the use of combined sensor technology, such as high resolution satellite imagery, EMI, and RTK-GPS (high resolution 3-D spatial information) for management of degraded waters. Objective 2: Improve our ability to predict the impact of degraded waters on infiltration into soils and plant response to irrigation with these waters by; a) determining the impact of using degraded waters for irrigation, including the effect of solution chemistry, high dissolved organic matter, and application of organic wastes, on soil physical and chemical properties; b) developing a new plant response sub model which considers salinity, soil water ion composition, drought stress, and evapotranspiration based on existing data sets, suitable for making field scale management decisions; and c) develop decision tools for use of waters impacted by salinity and potentially toxic elements, with emphasis on boron. Objective 3: Evaluate management strategies for use of degraded waters;a) test and validate applications of stream tube technology for field-scale parameterization of transport models that are applicable as decision tools for determination of plant response, leaching needs and management recommendations; b) develop a decision support tool for the field that utilizes salinity mapping, stream tube technology for delineating regions in the field and simplified modeling for salinity management of these regions.
Objective 1: We will evaluate the ability of a multi-sensor platform (ER, EMI, gamma-ray spectrometry) coupled to RTK-GPS to characterize the spatial distribution of texture, water content, salinity, and sodicity. Statistical analysis will be performed by determining correlation coefficients between sensor measurements and soil properties followed by a more extensive analysis using spatial regression models. Linear model-based statistical tests will be used to assess the adequacy and precision of the regression equations derived from the single and multi-sensor directed sampling strategies. Objective 2: a) We will examine the effects of a high dissolved organic carbon (DOC) treated municipal waste water on infiltration. We will next examine the effects of DOC and its interaction with SAR and pH on infiltration clay flocculation and saturated hydraulic conductivity on soils selected with a range in properties to develop new soil stability relationships. b) Plant relative yield functions will be developed and incorporated into UNSATCHEM model. c) We will develop a new B soil test for adsorbed and soluble B. d) Treated municipal waste waters will be measured for DOC, EC, pH and major ion composition, next utilized in boron adsorption experiments, and the boron adsorption as related to DOC described using a chemical surface complexation model. We will also investigate B desorption on soils having varying amounts of organic matter and examine B adsorption-desorption reaction on hysteretic soils after organic matter removal. If needed, we will develop a predictive model relating hysteresis to organic matter content. Objective 3: a) An intensive geospatial ECa survey will be used to delineate stream tubes in each of the two fields at a site with different quality irrigation water. Geospatial ECa measurements will be obtained with the multiplatform sensors from Objective 1. Eight classes of stream tubes will be identified using the EMh/EMv ratio and geometric mean of EMh and EMv as the classification criteria. Within each of 16 sub-classes a stream tube will be selected and 4 random sites within the tube will be selected for parameterization. Irrigation frequency, volumes of water applied and infiltration rates will be measured, soil samples collected and analyzed for EC and ion composition. b) We will evaluate changes in soil physical and chemical properties due to differences in irrigation water quality.