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The principles of irrigation and drainage in cracking soils differ markedly from non-cracking soils, and are not thoroughly understood. This paper presents a conceptual model to simulate water and salt flows in cracking soils of the Imperial Valley, CA, in the presence of ground water that contributes partially to ET demand of crops. A salt reactivity function is introduced in the model to account for mineral precipitation (salt deposition) and mineral dissolution (salt pick up). The conceptual water flow model assumes that surface irrigation water moves into the cracks, infiltrates horizontally to wet the soil profile and a fraction bypasses below the root zone into the shallow ground water and is retained for later crop extraction via upflow. Then, water drains vertically through the soil profile step by step, and root water extractions are calculated. When ET exceeds available water upflow of ground water is calculated. Provision for reclamation leaching before the next crop is also made. The associated conceptual salt transport model involves complete mixing of invading and resident soil water. Salt concentration from ET is subjected to a salt reactivity function to obtain salt deposition of calcite and gypsum to obtain salt concentration after precipitation. This reactivity function is also used in the inverse when two or more waters mix to transform salt after precipitation to salt concentration after ET. The flow of salts follows the water transport algorithum. The model has been applied to a point in the Imperial Valley and observed data from Bali et al. () was used for calibration. Simulated point data from four successive years of alfalfa, reclamation leaching, wheat and lettuce are evaluated in this paper. [ABSTRACT FROM AUTHOR]

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend preliminary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean. [ABSTRACT FROM AUTHOR]