预测自然界中天然有机物（NOM）的数量和性质的能力 饮用水供应可以让操作员预测混凝剂或消毒剂的需求。在里面 由两个水质不同的水库串联而成的系统 水质会影响从上游水库向下游水库的输水决策 水库CE QUAL W2版本2是一个二维横向平均流体力学模型 以及能够模拟水库蓄水的水质建模程序 温度和21种水质成分的垂直和纵向梯度。 具有 稍作修改后，该程序可用于建模NOM代理，包括total 有机碳（TOC）、溶解有机碳（DOC）和紫外线吸收率 波长为254 nm（UV 254）的光。UV 254可以有效预测 消毒副产物形成潜力（DBPFP）。UV 254和DOC一起可以 用于计算比紫外线吸收率（SUVA），这是NOM的有效预测因子 通过混凝去除。瓦丘塞特水库是大型水库未过滤供水的一部分 大都会区。该储层接收来自另一个储层的大量输送，且 降低NOM水平。CE QUAL W2有效地预测了 通过包括温度和光照，从Wachusett水库撤离 藻类生长引起的有机物衰变和有机物形成 强化水动力模型。可以实现更简单的模型，包括 同样的机制可以预测NOM替代物的浓度，这可能有助于 制定运营决策。包括15个参考文献、表格和图表。

The ability to predict the amount and character of natural organic matter (NOM) in a drinking water supply can allow operators to anticipate coagulant or disinfectant demand. In systems consisting of two reservoirs of differing water quality in series, the ability to predict water quality can impact decisions of transfer from an upstream reservoir to a downstream reservoir. CE QUAL W2 version 2 is a two dimensional laterally averaged hydrodynamic and water quality modeling program that is capable of modeling reservoir storage, and vertical and longitudinal gradients of temperature and 21 water quality constituents. With minor modifications, this program can be used to model NOM surrogates including total organic carbon (TOC), dissolved organic carbon (DOC) and the absorbance of ultraviolet light at a wavelength of 254 nm (UV 254). UV 254 can be an effective predictor of disinfection byproduct formation potential (DBPFP). Together, UV 254 and DOC can be used to calculate specific UV absorbance (SUVA) which is an effective predictor of NOM removal by coagulation. Wachusett Reservoir is part of an unfiltered supply for a large metropolitan area. This reservoir receives significant transfer from another reservoir with lower NOM levels. CE QUAL W2 effectively predicted TOC, DOC, UV 254, and SUVA at the withdrawal from Wachusett Reservoir by including temperature dependent and light induced decay of organic matter and organic matter formation by algal growth with an intensive hydrodynamic model. It may be possible to implement simpler model and include the same mechanisms to predict concentrations of NOM surrogates, which could aid in making operational decisions. Includes 15 references, table, figures.