@misc{reckhow_2001, title={Water resources programs under the ax}, volume={292}, number={5524}, journal={Science}, author={Reckhow, K. H.}, year={2001}, pages={2009} }
 @article{wickham_riitters_rv o'neill_reckhow_wade_jones_2000, title={Land cover as a framework for assessing risk of water pollution}, volume={36}, ISSN={["1093-474X"]}, DOI={10.1111/j.1752-1688.2000.tb05736.x}, abstractNote={ABSTRACT:  A survey of numerous field studies shows that nitrogen and phosphorous export coefficients are significantly different across forest, agriculture, and urban land‐cover types. We used simulations to estimate the land‐cover composition at which there was a significant risk of nutrient loads representative of watersheds without forest cover. The results suggest that at between 20 percent and 30 percent nonforest cover, there is a 10 percent or greater chance of N or P nutrient loads being equivalent to the median values of predominantly agricultural or urban watersheds. The methods apply to environmental management for assessing the risk to increased nonpoint nutrient pollution. Interpretation of the risk measures are discussed relative to their application for a single watershed and across a region comprised of several watersheds.}, number={6}, journal={JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION}, author={Wickham, JD and Riitters, KH and RV O'Neill and Reckhow, KH and Wade, TG and Jones, KB}, year={2000}, month={Dec}, pages={1417–1422} }
 @article{reckhow_1999, title={Water quality prediction and probability network models}, volume={56}, ISSN={["0706-652X"]}, DOI={10.1139/cjfas-56-7-1150}, abstractNote={It is a common strategy in surface water quality modeling to attempt to remedy predictive inadequacies by incorporating additional mechanistic detail into the model. This approach reflects the reasonable belief that enhanced scientific understanding of basic processes can be used to improve predictive modeling. However, nature is complex, and even the most detailed simulation model is extremely simple in comparison. At some point, additional detail exceeds our ability to simulate and predict with reasonable error levels. In those situations, an attractive alternative may be to express the complex behavior probabilistically, as in statistical mechanics, for example. This viewpoint is the basis for consideration of Bayesian probability networks for surface water quality assessment and prediction. To begin this examination of Bayes nets, some simple water quality examples are used for the illustration of basic ideas. This is followed by discussion of a set of proposed probability network models for the eutro...}, number={7}, journal={CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES}, author={Reckhow, KH}, year={1999}, month={Jul}, pages={1150–1158} }