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Reductions in emissions have successfully led to a regional decline in atmospheric nitrogen depositions over the past 20 years. By analyzing long-term data from 110 mountainous streams draining into German drinking water reservoirs, nitrate concentrations indeed declined in the majority of catchments. Furthermore, our meta-analysis indicates that the declining nitrate levels are linked to the release of dissolved iron to streams likely due to a reductive dissolution of iron(III) minerals in riparian wetland soils. This dissolution process mobilized adsorbed compounds, such as phosphate, dissolved organic carbon and arsenic, resulting in concentration increases in the streams and higher inputs to receiving drinking water reservoirs. Reductive mobilization was most significant in catchments with stream nitrate concentrations <6 mg L . Here, nitrate, as a competing electron acceptor, was too low in concentration to inhibit microbial iron(III) reduction. Consequently, observed trends were strongest in forested catchments, where nitrate concentrations were unaffected by agricultural and urban sources and which were therefore sensitive to reductions of atmospheric nitrogen depositions. We conclude that there is strong evidence that the decline in nitrogen deposition toward pre-industrial conditions lowers the redox buffer in riparian soils, destabilizing formerly fixed problematic compounds, and results in serious implications for water quality.

Abstract:Reliable estimates of groundwater recharge are required for the sustainable management of surface and ground water resources in semi-arid regions particularly in irrigated regions. In this study, groundwater recharge was estimated for an irrigated catchment in southeast Australia using a semi-distributed hydrological model (SWAT). The model was calibrated under the dry climatic conditions for the period from August 2002 to July 2003 using flow and remotely sensed evapotranspiration (ET). The model was able to simulate observed monthly drain flow and spatially distributed remotely sensed ET. Recharge tended to be higher for irrigated land covers, such as perennial pasture, than for non-irrigated land. On average, the estimated annual catchment recharge ranged between 147 and 289 mm which represented about 40% of the total rainfall and irrigation inputs. The optimized soil parameters indirectly reflected flow bypassing the soil matrix that could be responsible for this substantial amount of recharge. Overall, the estimated recharge was much more than that previously estimated for the wetter years.

[1] A lysimeter experiment was conducted in southeastern Australia to quantify the deep percolation response under irrigated pasture to different soil types, water table depths, and ponding times during surface irrigation. Deep percolation was governed by the final infiltration rate of the subsoil, the ponding time, the water table depth, and the amount of water stored in the rootzone between saturation and field capacity. These key variables were used to characterize both steady-and nonsteady-state percolation in a conceptual model of deep percolation. The conceptual model was found to provide an effective representation of deep percolation for both the lysimeter and field-scale water balance data. Steady-state percolation during irrigation was the dominant process contributing to deep percolation on most of the studied soils. Nonsteady-state percolation (redistribution) was very important for the sandiest soil type. The conceptual model provided better prediction of deep percolation than both data-based model (artificial neural network) and process-based modeling approach (1-D Richards' equation model).

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Adequate irrigation inputs are essential for the application of hydrological models in irrigated catchments, but reliable data on both the amount and the frequency of irrigation applications are often missing at an appropriate spatial scale. In this paper, we demonstrate and test approaches to estimate irrigation inputs for distributed hydrological modelling. In this context, the Soil and Water Assessment Tool was applied to simulate water balances for an irrigated catchment in southeast Australia during the period 2008–2010. Two methods for estimating irrigation inputs were tested. One method was based on a fixed irrigation application rate, whereas the other one had variable irrigation rates depending on season and the irrigated crop. These two approaches were also compared with the ‘auto‐irrigation’ method within the Soil and Water Assessment Tool model. The method with variable irrigation rates resulted in the most reasonable interpretation of the readily available irrigation data, consistent estimates of irrigation runoff coefficients throughout the year and the best fit to observed data on both drain flows at the catchment outlet and spatial evapotranspiration patterns. We also found that the different irrigation inputs significantly affected simulated water balances, in particular deep percolation under relatively dry climatic conditions. All these results suggest that it is possible to infer irrigation inputs from readily available data and local knowledge, adequate for hydrological modelling in irrigated catchments. Our study also demonstrates that, in order to predict reliable water balances in irrigated catchments, an accurate knowledge of irrigation scheduling and irrigation runoff is required. Copyright © 2015 John Wiley & Sons, Ltd.