Hydrologic and water quality (H/WQ) models are being used with increasing frequency to devise alternative pollution control strategies. It has been recognized that such models may have a large degree of uncertainty associated with their predictions, and that this uncertainty can significantly impact the utility of the model. In this study, ARRAMIS (Advanced Risk & Reliability Assessment Model) software package was used to analyze the uncertainty of the SWAT2000 (Soil and Water Assessment Tool) outputs concerning nutrients and sediment losses from agricultural lands. ARRAMIS applies Monte Carlo simulation technique connected with Latin hypercube sampling (LHS) scheme. This technique is applied to the Warner Creek watershed located in the Piedmont physiographic region of Maryland, and it provides an interval estimate of a range of values with an associated probability instead of a point estimate of a particular pollutant constituent. Uncertainty of model outputs was investigated using LHS scheme with restricted pairing for the model input sampling. Probability distribution functions (pdfs) for each of the 50 model simulations were constructed from these results. Model output distributions of interest in this analysis were stream flow, sediment, organic nitrogen (organic-N), organic phosphorus (organic-P), nitrate, ammonium, and mineral phosphorus (mineral-P) transported with water. Developed probability distribution functions for the model provided information with desirable probability. Results indicate that consideration of input parameter uncertainty produces 64% less mean stream flow along with approximately 8.2% larger sediment loading than obtained using mean input parameters. On the contrary, mean of outputs regarding nutrients such as nitrate, ammonia, organic-N, and organic-P (but not mineral-P) were almost the same as the one using mean input parameters. The uncertainty in predicted stream flow and sediment loading is large, but that for nutrient loadings is the same as that of the corresponding input parameters. This study concluded that using a best possible distribution for the input parameters to reflect the impact of soils and land use diversity in a small watershed on SWAT2000 model outputs may be more accurate than using average values for each input parameter.

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Phosphorus (P) is one of the main nutrients controlling algal production in aquatic systems. Proper management of P in agricultural production systems can greatly enhance our ability to combat pollution of aquatic environments. To address this issue, a decision support system (DSS) consisting of the Maryland Phosphorus Index (PI), diagnosis expert system (ES), prescription ES, and a nonpoint-source pollution model, Ground Water Loading Effects of Agricultural Management Systems (GLEAMS), was developed and applied to an agricultural watershed in southern Sweden. This system can identify critical source areas (CSAs) regarding phosphorus losses within the watershed, make a diagnosis of probable causes, prescribe the most appropriate best management practices (BMPs), and test the environmental effects of the applied BMPs. The PI calculations identified small parts of the watershed as CSAs. Only 10.4% of the total watershed area in 1995 and 5.2% of the total watershed area in 1996 were classed as "high potential P movement." Four probable causes (high P level in soil, excessive P fertilization, stream proximity, and subsurface drainage) and three BMPs (riparian buffer strips, reduced P fertilizer application, and P fertilizer incorporation) were identified by a diagnosis and prescription expert system. The GLEAMS simulations conducted for one selected CSA field for a 24-yr period showed that the recommended BMP reduced runoff P losses by 55% and sediment P losses by 71%, if applied from the first year. Results showed that using DSS may enable us to select a proper BMP implementation strategy and to realize the beneficial effect of BMPs on a long-term basis.

Background: Quantification of in-vivo biomolecule mass transport and reaction rate parameters from experimental data obtained by Fluorescence Recovery after Photobleaching (FRAP) is becoming more important.