Commonly used hydrological models often require much implementation and computational efforts, while their accuracy is limited, especially in areas with strong anthropogenic controls. In this study, two alternative, non-parametric data-driven approaches are tested to supplement existing hydrological models for the assessment of water scarcity along rivers and the potential impact of mitigation strategies. These approaches can assess the water availability at a regional scale in a spatially detailed way, taking into account both the flow regulation effects and other anthropogenic influences as reflected in river flow observations. After application to a network of rivers in Flanders (Belgium), a leave-one-out cross-validation shows that the data-driven approaches are promising, with good NSE values on daily river flows of 0.58–0.65 and high coefficient of determination of 0.72–0.76. The overall performance in representing the relative changes of flows in space and time is similar to that of two state-of-the-art hydrological models.

常用的水文模型通常需要大量的实施和计算工作，而其准确性有限，特别是在人为控制较强的地区。在本研究中，测试了两种替代的非参数数据驱动方法，以补充现有的水文模型，以评估河流沿岸的水资源短缺以及缓解策略的潜在影响。这些方法可以以空间详细的方式评估区域尺度的水资源可用性，同时考虑河流流量观测中反映的流量调节效应和其他人为影响。在应用于佛兰德斯（比利时）的河流网络后，留一法交叉验证表明，数据驱动的方法很有前景，每日河流流量的 NSE 值良好，为 0.58-0.65，决定系数较高0.72–0.76。表示空间和时间流量相对变化的整体性能与两个最先进的水文模型相似。