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A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.

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Natural lakes in South Korea are limited in number and generally quite small. As a result, reservoirs and regulated rivers are the major sources of freshwater for society. About 18 000 reservoirs have been constructed in South Korea, and they are particularly important for domestic water supply. Thirteen of the major reservoirs were surveyed in this general assessment of the trophic state of South Korean reservoirs. Ten reservoirs were from the upper or middle reaches of rivers (including eight of the ten largest reservoirs in Korea), and three were estuarine reservoirs. Reservoirs in the mountainous district of South Korea were usually mesotrophic, whereas the estuarine reservoirs were highly eutrophic. Because total nitrogen to total phosphorus ratios were always between 18 and 163, phosphorus was probably more limiting than nitrogen for algal growth. However, hydraulic residence time and light penetration may be limiting in the nutrient-enriched downstream reservoirs. In winter, algal density was lowest in deep reservoirs, perhaps due to deep mixing. During the same season, algal density was high in shallow reservoirs, perhaps due to a favorable hydraulic residence time. Factors contributing to the observed eutrophication patterns, including nutrient runoff from agriculture, animal farms, fish aquaculture, and urban areas, are discussed. According to the national budget of phosphorus, fertilizer and livestock manure are major source of phosphorus, contributing 133 400 and 73 700 tons of phosphorus per year, respectively, while human excretion discharges 30 000 tons P year Ϫ1 . Reduction of the application of fertilizer, proper treatment of manure, and conservation of topsoil may be the most practical and effective measures to prevent further lake eutrophication.

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Abstract. Watershed-scale modeling can be a valuable tool to aid in quantification of water quality and yield; however, several challenges remain. In many watersheds, it is difficult to adequately quantify hydrologic partitioning. Data scarcity is prevalent, accuracy of spatially distributed meteorology is difficult to quantify, forest encroachment and land use issues are common, and surface water and groundwater abstractions substantially modify watershed-based processes. Our objective is to assess the capability of the Soil and Water Assessment Tool (SWAT) model to capture eventbased and long-term monsoonal rainfall-runoff processes in complex mountainous terrain. To accomplish this, we developed a unique quality-control, gap-filling algorithm for interpolation of high-frequency meteorological data. We used a novel multi-location, multi-optimization calibration technique to improve estimations of catchment-wide hydrologic partitioning. The interdisciplinary model was calibrated to a unique combination of statistical, hydrologic, and plant growth metrics. Our results indicate scale-dependent sensitivity of hydrologic partitioning and substantial influence of engineered features. The addition of hydrologic and plant growth objective functions identified the importance of culverts in catchment-wide flow distribution. While this study shows the challenges of applying the SWAT model to complex terrain and extreme environments; by incorporating anthropogenic features into modeling scenarios, we can enhance our understanding of the hydroecological impact.