Accurate simulation of reservoirs has been a challenge for global hydrological models due to highly discontinuous water management and uncertainties in reservoir shape representation. In addition, at a global scale, it is crucial to consider those reservoirs that disrupt the downstream flow regime. We augment the mesoscale Hydrological Model with a newly developed lake module (LM) that incorporates an existing reservoir regulation scheme with non-consumptive demand predictions from random forest. We also evaluate the sensitivity of reservoir shape on streamflow and evaporation for three shape approximations of varying complexities. We tested the LM across 31 non-consumptive reservoirs covering an extensive range of hydroclimatic characteristics and demonstrate the applicability by using freely available global reservoir information. Streamflow simulations with reservoirs and model calibration show a median Kling-Gupta Efficiency improvement of +0.94 (calibration) and +0.77 (validation) when compared against model simulations without reservoirs and default parameter set. We find reservoir evaporation highly sensitive to reservoir shape with half-pyramid approximation consistently resulting in best fit at reservoirs with surveyed bathymetry. In contrast, the linear approximation (rectangular prism) produced a median bias of +114% relative to half-pyramid, for estimating evaporation, across all the reservoirs. Streamflow simulations were insensitive to the reservoir shape. Our analysis shows that 30% of the non-consumptive hydropower reservoirs of the GRanD data set are non-disruptive and can be excluded without loss to model realism. Further work is necessary for testing the regulation approach in reservoirs with consumptive water usage.

中文翻译：

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改进全球水文模型中破坏性水库的模拟

由于高度不连续的水管理和水库形状表示的不确定性，水库的精确模拟一直是全球水文模型的挑战。此外，在全球范围内，考虑那些破坏下游流态的水库至关重要。我们用新开发的湖泊模块（LM）增强了中尺度水文模型，该模块将现有的水库调节方案与随机森林的非消耗性需求预测相结合。我们还评估了不同复杂度的三种形状近似值的水库形状对水流和蒸发的敏感性。我们在 31 个非消耗性水库中测试了 LM，涵盖了广泛的水文气候特征，并通过使用免费提供的全球水库信息证明了其适用性。与没有水库和默认参数集的模型模拟相比，使用水库和模型校准的水流模拟显示，Kling-Gupta 效率中值提高了 +0.94（校准）和 +0.77（验证）。我们发现储层蒸发对储层形状高度敏感，半金字塔近似始终导致与测深测量最适合的储层。相比之下，在估计所有水库的蒸发量时，线性近似（矩形棱柱）相对于半金字塔产生了 +114% 的中值偏差。水流模拟对水库形状不敏感。我们的分析表明，GRanD 数据集中 30% 的非消耗性水电站水库不会造成破坏，可以在不影响模型真实性的情况下排除。需要进一步开展工作来测试水库消耗水的调节方法。