Simulating water and nitrogen loss from an irrigated paddy field under continuously flooded condition with Hydrus-1D model.

Agricultural non-point source pollution is a major factor in surface water and groundwater pollution, especially for nitrogen (N) pollution. In this paper, an experiment was conducted in a direct-seeded paddy field under traditional continuously flooded irrigation (CFI). The water movement and N transport and transformation were simulated via the Hydrus-1D model, and the model was calibrated using field measurements. The model had a total water balance error of 0.236 cm and a relative error (error/input total water) of 0.23%. For the solute transport model, the N balance error and relative error (error/input total N) were 0.36 kg ha-1 and 0.40%, respectively. The study results indicate that the plow pan plays a crucial role in vertical water movement in paddy fields. Water flow was mainly lost through surface runoff and underground drainage, with proportions to total input water of 32.33 and 42.58%, respectively. The water productivity in the study was 0.36 kg m-3. The simulated N concentration results revealed that ammonia was the main form in rice uptake (95% of total N uptake), and its concentration was much larger than for nitrate under CFI. Denitrification and volatilization were the main losses, with proportions to total consumption of 23.18 and 14.49%, respectively. Leaching (10.28%) and surface runoff loss (2.05%) were the main losses of N pushed out of the system by water. Hydrus-1D simulation was an effective method to predict water flow and N concentrations in the three different forms. The study provides results that could be used to guide water and fertilization management and field results for numerical studies of water flow and N transport and transformation in the future.

Experimental study on soluble chemical transfer to surface runoff from soil.

Prevention of chemical transfer from soil to surface runoff, under condition of irrigation and subsurface drainage, would improve surface water quality. In this paper, a series of laboratory experiments were conducted to assess the effects of various soil and hydraulic factors on chemical transfer from soil to surface runoff. The factors include maximum depth of ponding water on soil surface, initial volumetric water content of soil, depth of soil with low porosity, type or texture of soil and condition of drainage. In the experiments, two soils, sand and loam, mixed with different quantities of soluble KCl were filled in the sandboxes and prepared under different initial saturated conditions. Simulated rainfall induced surface runoff are operated in the soils, and various ponding water depths on soil surface are simulated. Flow rates and KCl concentration of surface runoff are measured during the experiments. The following conclusions are made from the study results: (1) KCl concentration in surface runoff water would decrease with the increase of the maximum depth of ponding water on soil surface; (2) KCl concentration in surface runoff water would increase with the increase of initial volumetric water content in the soil; (3) smaller depth of soil with less porosity or deeper depth of soil with larger porosity leads to less KCl transfer to surface runoff; (4) the soil with finer texture, such as loam, could keep more fertilizer in soil, which will result in more KCl concentration in surface runoff; and (5) good subsurface drainage condition will increase the infiltration and drainage rates during rainfall event and will decrease KCl concentration in surface runoff. Therefore, it is necessary to reuse drained fertile water effectively during rainfall, without polluting groundwater. These study results should be considered in agriculture management to reduce soluble chemical transfer from soil to surface runoff for reducing non-point sources pollution.

Uncertainty analysis of multi-rate kinetics of uranium desorption from sediments.

Multi-rate surface complexation models have been proposed to describe the kinetics of uranyl (U(VI)) surface complexation reactions (SCR) rate-limited by diffusive mass transfer to and from intragranular sorption sites in subsurface sediments. In this study, a Bayesian-based, Differential Evolution Markov Chain method was used to assess the uncertainty and to identify factors controlling the uncertainties of the multi-rate SCR model. The rate constants in the multi-rate SCR were estimated with and without assumption of a specified lognormal distribution to test the lognormal assumption typically used to minimize the number of the rate constants in the multi-rate model. U(VI) desorption under variable chemical conditions from a contaminated sediment at US Hanford 300 Area, Washington was used as an example. The results indicated that the estimated rate constants without a specified lognormal assumption approximately followed a lognormal distribution, indicating that the lognormal is an effective assumption for the rate constants in the multi-rate SCR model. However, those rate constants with their corresponding half-lives longer than the experimental durations for model characterization had larger uncertainties and could not be reliably estimated. The uncertainty analysis revealed that the time-scale of the experiments for calibrating the multi-rate SCR model, the assumption for the rate constant distribution, the geochemical conditions involved in predicting U(VI) desorption, and equilibrium U(VI) speciation reaction constants were the major factors contributing to the extrapolation uncertainties of the multi-rate SCR model. Overall, the results from this study demonstrated that the multi-rate SCR model with a lognormal distribution of its rate constants is an effective approach for describing rate-limited U(VI) desorption; however, the model contains uncertainties, especially for those smaller rate constants, that require careful consideration for predicting U(VI) sorption and desorption.