The thermal regime of rivers plays a crucial role in chemical, biological and ecological processes. Effectively predicting water temperature is a key issue related to environment management. This study develops a probabilistic model based on C-vine copulas for water temperature prediction. The proposed framework is applied to forecast daily water temperature in the Yangtze River, considering long-term effects of preceding air temperature and daily discharge. The prediction performance of this framework is compared with the logistic regression model (LogR) and generalized regression neural network (GRNN). The results of this study indicate that the proposed C-vine copula framework and GRNN model provide better forecasting of stream temperature with weak human-related disturbances than LogR model. The outperformance of C-vine framework is reflected by its ability to accurately capture variations in the water temperature greatly affected by the Three Gorges Reservoir (TGR). This steady and reliable framework is further applied for the conservation of Chinese Sturgeon to estimate the range of suggested discharge, given the daily air temperatures, to adjust the water temperature within 18-20 ℃ at Yichang station during the spawning season. This application is verified to be more effective, providing indications for reservoir management to lower water temperatures by regulating river flow to ensure the occurrence of spawning activities. The results of this study may provide a scientific reference for the ecological operation of reservoirs in regulated rivers.

河流的热状况在化学，生物和生态过程中起着至关重要的作用。有效预测水温是与环境管理有关的关键问题。这项研究开发了一个基于C-vine copulas的概率模型，用于预测水温。考虑到先前气温和日排放量的长期影响，拟议的框架可用于预测长江中的每日水温。该框架的预测性能与逻辑回归模型（LogR）和广义回归神经网络（GRNN）进行了比较。这项研究的结果表明，与LogR模型相比，所提出的C-vine copula框架和GRNN模型可以更好地预测人为干扰较弱的河流温度。C-vine框架的出色表现体现在它能够准确捕获受三峡水库（TGR）极大影响的水温变化的能力。这个稳定可靠的框架被进一步应用到中华conservation的保护中，根据每天的气温估算出建议的放水范围，以便在产卵季节将宜昌站的水温调节在18-20℃范围内。该应用程序被证明是更有效的，它通过调节河流流量以确保产卵活动的发生，为水库管理降低水温提供了指示。研究结果可为规范河流水库的生态运行提供科学参考。