ABSTRACT Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve – FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.

中文翻译：

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通过多目标校准实现更一致的生态水文模型：秘鲁安第斯维尔卡诺塔河流域的案例研究

摘要 大多数水文研究依赖于单独使用流量校准的模型。然而，基于这种校准来判断模型的可靠性是有问题的，因为它不能保证内部水文过程的正确表示。本研究旨在 (a) 除排放外，还使用遥感植被数据和水文特征（流量持续时间曲线 - FDC 和基流指数）开发一个综合的多目标校准框架，以及 (b) 将该框架应用于校准典型安第斯流域的土壤和水评估工具 (SWAT)。总体而言，我们的校准方法在植被、水流和水流分区模拟方面优于传统的基于排放和基于 FDC 特征的校准策略。该地区的新水文见解如下：基流是维持长旱季水流的主要组成部分，牧区提供比其他土地覆盖类型更高的产水量和基流。所提出的方法可用于具有复杂地形的其他数据稀缺区域。