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The Illinois River is a major contributor of nitrate-N to the Mississippi River and the Gulf of Mexico, where nitrate is a leading cause of summertime benthic hypoxia. Corn-soybean production on tile-drained land is a leading source of nitrate-N in this river system, in addition to municipal wastewater discharge. We calculated annual nitrate-N loads in the Illinois River at Valley City from 1976 to 2014 by linear interpolation. Although there was not a significant trend in annual loads during the entire study period, there was a significant downward trend in flow-weighted nitrate-N concentration after 1990 despite high concentrations in 2013 after the 2012 drought. Multivariate regression analysis revealed a statistically significant association between annual flow-weighted nitrate-N concentration and cumulative residual agricultural N inputs to the watershed during a 6-yr window. This suggests that declines in flow-weighted nitrate-N concentration may reflect increasing N use efficiency in agriculture and a depletion of legacy N stored in the watershed. The watershed appears to have transitioned from a state of stationarity in nitrate concentration to nonstationarity. The average annual nitrate-N load at Valley City from 2010 to 2014 was 10% less than the 1980- 1996 average load, indicating recent progress toward Illinois' nutrient loss reduction milestone of 15% reduction by 2025 and ultimate target of 45% reduction. [ABSTRACT FROM AUTHOR]

Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies. Specifically, the climate predictions can be very different based on a variety of global climate models and alternative greenhouse emission scenarios. In this study, we coupled a forest landscape model, LANDIS-II, and a forest process model, PnET-II, to examine the uncertainty (that results from the uncertainty in the future climate predictions) in the forest-type composition prediction for a transitional forest landscape [the Boundary Water Canoe Area]. Using an improved global-sensitivity analysis technique [Fourier amplitude sensitivity test], we also quantified the amount of uncertainty in the forest-type composition prediction contributed by different climate variables including temperature, CO2, precipitation and photosynthetic active radiation (PAR). The forest landscape response was simulated for the period 2000–2400ad based on the differential responses of 13 tree species under an ensemble of 27 possible climate prediction profiles (monthly time series of climate variables). Our simulations indicate that the uncertainty in the forest-type composition becomes very high after 2200ad, which is close to the time when the current forests are largely removed by windthrow disturbances and natural mortality. The most important source of uncertainty in the forest-type composition prediction is from the uncertainty in temperature predictions. The second most important source is PAR, the third is CO2 and the least important is precipitation. Our results also show that if the optimum photosynthetic temperature rises due to CO2 enrichment, the forest landscape response to climatic change measured by forest-type composition may be substantially reduced. [ABSTRACT FROM AUTHOR]

Projected temperature increases under global warming could benefit southern tree species by providing them the optimal growing temperature and could be detrimental to northern species by exposing them to the supra optimal growing temperatures. This benefit-detriment trade-off could increase the competitive advantage of southern species in the northern species range and cause the increase or even dominance of southern species in the northern domain. However, the optimum temperature for photosynthesis of C3 plants may increase due to CO2 enrichment. An increase in the optimum temperature could greatly reduce the benefit-detriment effect. In this study, we coupled a forest ecosystem process model (PnET-II) and a forest GAP model (LINKAGES) with a spatially dynamic forest landscape model (LANDIS-II) to study how an optimum temperature increase could affect forest landscape response due to global warming. We simulated 360 years of forest landscape change in the Boundary Water Canoe Area (BWCA) in northern Minnesota, which is transitional between boreal and temperate forest. Our results showed that, under the control scenario of continuing the historic 1984–1993 mean climate (mainly temperature, precipitation and CO2), the BWCA will become a spruce-fir dominated boreal forest. However, under the scenario of predicted climatic change [the 2000–2099 climates are predicted by Canadian Climate Center (CCC), followed by 200 years of continuing the predicted 2090–2099 mean climate], the BWCA will become a pine-dominated mixed forest. If the optimum temperature increases gradually with [CO2] (the increase in optimum temperature is assumed to change gradually from 0 °C in year 2000 to 5 °C in year 2099 when [CO2] reaches 711 ppm and stabilizes at 5 °C after year 2099), the BWCA would remain a fir-dominated boreal forest in areas with relatively high water-holding capacity, but not in areas with relatively low water-holding capacity. Our results suggest that the [CO2] induced increases in optimum temperature could substantially reduce forest landscape change caused by global warming. However, not all tree species would be able to successfully adapt to future warming as predicted by CCC, regardless of optimum temperature acclimations. [ABSTRACT FROM AUTHOR]

In this study, an uncertainty analysis procedure for joint sequential simulation of multiple attributes of spatially explicit models used in geographical informational systems was developed based on regression analysis. This procedure utilizes information obtained from joint sequential simulation to establish the relationship between model uncertainty and variation of model inputs. Using this procedure, model variance can be partitioned by model input parameters on a cell by cell basis. In the partitioning, the correlation of neighboring cells is accounted for. With traditional uncertainty analysis methods, this is not possible. In a case study, spatial variation of soil erodibility from a joint sequential simulation of soil properties was analyzed. The results showed that the regression approach is a very effective method in the analysis of the relationship between variation of the model output and model input parameters. It was also shown for the case study that: (1) the uncertainty of soil erodibility of a cell is mainly propagated from its own soil properties; (2) the interactions of soil properties of neighboring cells could reduce uncertainty of soil erodibility; (3) it is sufficient for uncertainty analysis to include the nearest three neighboring cell groups; and (4) the largest uncertainty contributors vary by soil properties and location. [ABSTRACT FROM AUTHOR]

A quantitative understanding of the relationship between terrestrial N inputs and riverine N flux can help guide conservation, policy, and adaptive management efforts aimed at preserving or restoring water quality. The objective of this study was to compare recently published approaches for relating terrestrial N inputs to the Mississippi River basin (MRB) with measured nitrate flux in the lower Mississippi River. Nitrogen inputs to and outputs from the MRB (1951 to 1996) were estimated from state‐level annual agricultural production statistics and NOy (inorganic oxides of N) deposition estimates for 20 states that comprise 90% of the MRB. A model with water yield and gross N inputs accounted for 85% of the variation in observed annual nitrate flux in the lower Mississippi River, from 1960 to 1998, but tended to underestimate high nitrate flux and overestimate low nitrate flux. A model that used water yield and net anthropogenic nitrogen inputs (NANI) accounted for 95% of the variation in riverine N flux. The NANI approach accounted for N harvested in crops and assumed that crop harvest in excess of the nutritional needs of the humans and livestock in the basin would be exported from the basin. The U.S. White House Committee on Natural Resources and Environment (CENR) developed a more comprehensive N budget that included estimates of ammonia volatilization, denitrification, and exchanges with soil organic matter. The residual N in the CENR budget was weakly and negatively correlated with observed riverine nitrate flux. The CENR estimates of soil N mineralization and immobilization suggested that there were large (2000 kg N ha−1) net losses of soil organic N between 1951 and 1996. When the CENR N budget was modified by assuming that soil organic N levels have been relatively constant after 1950, and ammonia volatilization losses are redeposited within the basin, the trend of residual N closely matched temporal variation in NANI and was positively correlated with riverine nitrate flux in the lower Mississippi River. Based on results from applying these three modeling approaches, we conclude that although the NANI approach does not address several processes that influence the N cycle, it appears to focus on the terms that can be estimated with reasonable certainty and that are correlated with riverine N flux. [ABSTRACT FROM AUTHOR]