Abstract
Abstract. Terrestrial surface water temperature is a key variable affecting water quality and energy balance, and thermodynamics and fluid dynamics are tightly coupled in fluvial and lacustrine systems. Streamflow generally plays a role in the horizontal redistribution of heat, and thermal exchange in lakes predominantly occurs in a vertical direction. However, numerical models simulate the water temperature for uncoupled rivers and lakes, and the linkages between them on a global scale remain unclear. In this study, we proposed an integrated modeling framework: Tightly Coupled framework for Hydrology of Open water Interactions in River–lake network (TCHOIR, read as “tee quire”). The objective is to simulate terrestrial fluvial and thermodynamics as a continuum of mass and energy in solid and liquid phases redistributed among rivers and lakes. TCHOIR uses high-resolution geographical information harmonized over fluvial and lacustrine networks. The results have been validated through comparison with in situ observations and satellite-based data products, and the model sensitivity has been tested with multiple meteorological forcing datasets. It was observed that the “coupled” mode outperformed the “river-only” mode in terms of discharge and temperature downstream of lakes; moreover, it was observed that seasonal and interannual variation in lake water levels and temperature are also more reliable in the “coupled” mode. The inclusion of lakes in the coupled model resulted in an increase in river temperatures during winter at midlatitudes and a decrease in temperatures during summer at high latitudes, which reflects the role of lakes as a form of large heat storage. The river–lake coupling framework presented herein provides a basis for further elucidating the role of terrestrial surface water in Earth's energy cycle.
Highlights
The temperature of terrestrial surface water plays a vital role in biogeochemical cycles, as it affects the solubility and reactivity of materials and organismal activity (Abril et al, 2014; Ozaki et al, 2003; Webb, 1996)
Because the physical schemes used in this study to represent hydro- and thermodynamics in rivers are identical to an existing model (Tokuda et al, 2019), this section focuses on the lake model and the coupling framework after briefly summarizing those riverine schemes
While the river model employed in our study is a state-of-the-art model that can be applied on a global scale, it is evident that the lake model requires more improvement compared with previous studies
Summary
The temperature of terrestrial surface water plays a vital role in biogeochemical cycles, as it affects the solubility and reactivity of materials and organismal activity (Abril et al, 2014; Ozaki et al, 2003; Webb, 1996). A breakthrough in model development came through a proposal by Henderson-Sellers (1985) for the parameterization of vertical mixing to bypass explicit calculations of turbulent exchanges by shear stress This led to the development of several numerical models (e.g., Hostetler and Bartlein, 1990) that solved a diffusion equation with the boundary conditions set at the water surface and lake bottom. The research reported initially developed a method that enabled the location and shape of lakes to be represented explicitly on a river channel network on a global scale This technique is an extension of the upscaling method for highresolution topographic data, representing the shape of a hydrological unit catchment area instead of assuming a rectangular grid system (Yamazaki et al, 2009). The remainder of this paper is structured as follows: Sect. 2 describes the algorithm used to develop the river–lake network dataset, Sect. 3 presents the development details of the coupling framework and the one-dimensional lake model, Sect. 4 shows the validation results of the river–lake network dataset, Sect. 5 provides the experimental configuration used to validate the framework and the corresponding results, Sect. 6 discusses the effects of thermodynamics of lakes on rivers, Sect. 7 shows the sensitivity test of the validation results of the meteorological forcing datasets, Sect. 8 summarizes the further development of the framework, and Sect. 9 presents the conclusion
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