No abstract

Water temperature is a fundamental parameter for both hydrological and ecological processes in aquatic systems. Usually, a sinusoidal function is used to represent the seasonal variation of water temperature in natural surface water. However, a large number of dams have been built on rivers worldwide, and water mass transport is retarded due to the reservoir impoundment. As a result, the water temperature lags seasonally behind the natural cycle due to seasonal dam-operation, and the ordinary sinusoidal function is no more valid in describing the thermal process. In this study, a composite cosine function (CCF) is introduced to address the dam-influenced seasonally varying water temperature pattern, which includes a time varying phase-lag for temperature variations. The proposed method is applied to the Three Gorges Reservoir (TGR), a major impoundment on the Yangtze River, China. It is found that the annual thermal process of warming and cooling becomes asymmetry during the post-TGR stage, that is, the seasonally varying temperature follows an asymmetric sinusoidal wave pattern. In frequency spectrum, both annual and semi-annual components are main contributors to the time varying water temperature. The minimum temperature of TGR's discharge has increased by 1.86 C after the normal operation of the reservoir, and the coldest water arrival timing is postponed by up to 40 days in the dry season. The benefit of using CCF in temperature reconstruction is that it can prevent the mistake of ascribing the semi-annual component to short-term fluctuations.The present study can help in improving the understanding of the asymmetric seasonal temperature processes due to dam operation.

River water temperature (RWT), a primary parameter for hydrological and ecological processes, is influenced by both climate change and anthropogenic intervention. Studies on such influences have been severely restricted due to the scarcity of river temperature data. This paper proposed a three-stage method to obtain long-term daily water temperature for rivers and river-type reservoirs by integrating remote sensing technique and river water temperature modelling. The proposed three-stage method was applied to the Three Gorges Reservoir (TGR) and validated against in situ measured RWTs in the two study sites, Cuntan and Huanglingmiao. The result showed improvements in the method: the quadrate window selection and RWT correction jointly reduce RMSE from 1.8 to 0.9 °C in Cuntan and from 2.1 to 1.2 °C in Huanglingmiao. As a whole, the estimated daily RWT has a consistent RMSE of 1.1–1.9 °C. Meanwhile, by analysing the Landsat-derived daily RWT, we demonstrated that the TGR had a significant impact on the outflow’s thermal regime. At the downstream reach of TGR, an apparent increase in RWT in the cold season and interannual thermal regime delay compared to inflow were found with the increasing water level after the dam construction. All the results and analyses indicate that the proposed three-stage method could be applied to obtain long time series of daily RWT and provide a promising approach to qualitatively analyse RWT variation in the poorly gauged catchment for river water quality monitoring and management.

No abstract

Water temperature in the Yangtze River mainstream has been experiencing significant changes due to the climate change and the operation of a series of world-class large dams, i.e., the Three Gorges Dam (TGD) and upstream cascade reservoirs (CDs). However, quantitative effects of these factors are not fully known, which hinders our understanding on the thermal regime alterations and further prediction in ecosystem response. Here, we will simulate the riverine water temperature (RWT) variations by building a physics-based model, and quantify the respective impacts from TGD, CDs and climate change through a model-based framework. In the framework, both the dam-regulated hydro-thermodynamic processes and the spatial heterogeneity of the meteorological condition in this large river-reservoir system are thoroughly considered. The results show a fluvial warming of 0.31~0.56 oC/10a in recent three decades, mainly attributed to climate change (44%~80% for different reaches). The dam has caused a substantial seasonal thermal lag in the dry season, e.g., ~40 days near the TGD, and accompanying severe alterations in the monthly RWT. A reduction of 10% in seasonal RWT is identified, which is attributed to both dam and climate change.