Understanding the contributions of potential drivers on runoff is essential for the sustainable management of water resources; however, the impacts of climate variability and human activities on runoff at inter-annual and inter-decadal scales have rarely been assessed quantitatively. To achieve this goal, this study develops a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method. ESMD allows to separate the times series of drivers and runoff into different time scales. BPANN is then used to simulate the relation between the drivers and runoff at each time scale separately. Weights connection method is employed to quantify the impacts of climate variability and human activities on runoff. The performance of this proposed model is compared with multiple linear regression (MLR). The mountainous area of the Hotan River Basin is selected as case study area. Results reveal that runoff exhibits significant fluctuations at inter-annual (2 and 9 years) and inter-decadal (14 years) scales. Climate variables are responsible for 81% of the runoff variations, while human activities account for 8%. The nonlinear hybrid model substantially outperforms MLR in all performance measures. We attribute this improvement to the ability of the proposed model to represent nonlinear relations and to simulate the association between drivers and runoff at different time scales. For instance, water vapor affects runoff positively at the inter-annual time scale but negatively at the inter-decadal time scale. Such opposing relations cannot be represented by MLR or many other, more traditional methods.

了解潜在的径流驱动因素对水资源的可持续管理至关重要；但是，很少对气候变化和人类活动对年际和年代际尺度上径流量的影响进行定量评估。为了实现这一目标，本研究开发了一个非线性混合模型，该模型集成了极点对称模式分解（ESMD），反向传播人工神经网络（BPANN）和权重连接方法。ESMD允许将驱动程序和径流的时间序列分为不同的时间范围。然后使用BPANN分别模拟每个时间尺度上驱动力与径流之间的关系。权重连接法用于量化气候变化和人类活动对径流的影响。将该模型的性能与多元线性回归（MLR）进行比较。选择和田河流域山区作为案例研究区域。结果表明，径流量在年际（2和9年）和年代际（14年）尺度上表现出显着的波动。气候变量占径流变化的81％，而人类活动占8％。在所有性能指标上，非线性混合模型均明显优于MLR。我们将此改进归因于所提出的模型表示非线性关系以及模拟不同时间尺度上的驱动程序与径流之间关联的能力。例如，水蒸气在年际时间尺度上对径流产生正向影响，而在年代际时间尺度上则对径流产生负向影响。