River water quality assessment, affected by pollution load, and river regime changes in various climate conditions, is an implementation that simplifies water resources management, and justifies terms for increases or decreases in human activities. The current paper aims to offer a water quality model of a river considering parametric, hydrologic, and pollution load uncertainty by using uncertainty indexes like Plevel, ARIL, and NUE. These indexes were used to analyze the influences of the model's parameters and the river's regime alternations on the results. A Qual2K model, calibrated with PSO algorithm, is presented and connected to GLUE algorithm to assess the model's uncertainties like effective input parameters on the modeled variations, headwater flow, and input pollutions. Zarjoob River, in the north of Iran, was chosen as the case study. The results illustrate that the interaction among parameters, hydrologic and pollutant discharge data should be considered in river water quality simulation. The presented methodology can analyze the influences of parametric uncertainty, parametric and hydrologic uncertainty, and pollution input load uncertainty according to any quantity of observations and the modeled results of any river.

河流水质评估受污染负荷和各种气候条件下河势变化的影响，是一种简化水资源管理的实施，并证明人类活动增加或减少的合理性。本文旨在通过使用 Plevel、ARIL 和 NUE 等不确定性指标，提供考虑参数、水文和污染负荷不确定性的河流水质模型。这些指标用于分析模型参数和河道变迁对结果的影响。提出了一个使用 PSO 算法校准的 Qual2K 模型，并将其连接到 GLUE 算法以评估模型的不确定性，例如模拟变化的有效输入参数、源头流量和输入污染。伊朗北部的扎尔霍布河，被选为案例研究。结果表明，河流水质模拟应考虑参数、水文和污染物排放数据之间的相互作用。所提出的方法可以根据任何数量的观测和任何河流的建模结果分析参数不确定性、参数和水文不确定性以及污染输入负荷不确定性的影响。