Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

中文翻译：

---

使用国家水文模型获取区域气候变化对具有未表示过程的流域的影响


气候变化对水资源供应的影响越来越大。国家规模的水文模型模拟了许多重要过程产生的径流，但通常没有人类用水和管理活动等过程。这项工作使用一个国家规模的水文模型探索和测试了解释此类遗漏过程的方法。两种偏差校正方法，即流期曲线 （FDC） 和自回归综合移动平均 （ARIMA），在美国地质调查局国家水文模型 （NHM-PRMS） 模拟的溪流上进行了测试，该模型省略了灌溉抽水。使用半干旱农业案例研究。FDC 和 ARIMA 分别在校正低流量和高流量方面表现更好。混合方法在低流量和高流量下均表现良好;典型的 Nash-Sutcliffe 值从 <-1.00 增加到约 0.75。结果提出了一些方法，可以使用这些方法对国家规模的水文模型进行偏差校正，以改进区域径流估计。讨论了这些校正方法在模拟未来预测中的效用。