A continuous-variable Bayesian network (cBN) model is used to link watershed development and climate change to stream ecosystem indicators. A graphical model, reflecting our understanding of the connections between climate change, weather condition, loss of natural land cover, stream flow characteristics, and stream ecosystem indicators is used as the basis for selecting flow metrics for predicting macroinvertebrate-based indicators. Selected flow metrics were then linked to variables representing watershed development and climate change. We fit the model to data from two river basins in southeast US and the resulting model was used to simulate future stream ecological conditions using projected future climate and development scenarios. The three climate models predicted varying ecological condition trajectories, but similar worst-case ecological conditions. The established modeling approach couples mechanistic understanding with field data to develop predictions of management-relevant variables across a heterogeneous landscape. We discussed the transferability of the modeling approach.

连续变量贝叶斯网络 (cBN) 模型用于将流域发展和气候变化与河流生态系统指标联系起来。反映我们对气候变化、天气条件、自然土地覆盖丧失、河流流量特征和河流生态系统指标之间联系的理解的图形模型被用作选择流量指标以预测基于大型无脊椎动物的指标的基础。然后将选定的流量指标与代表流域发展和气候变化的变量联系起来。我们将模型与美国东南部两个流域的数据进行拟合，所得模型用于使用预测的未来气候和发展情景模拟未来的河流生态条件。三种气候模型预测了不同的生态条件轨迹，但类似的最坏情况生态条件。已建立的建模方法将机械理解与现场数据相结合，以开发跨异构景观的管理相关变量的预测。我们讨论了建模方法的可转移性。