Abstract Uncertainty in climate forcings causes significant uncertainty in estimating streamflow and other land-surface fluxes in hydrologic model simulations. Earlier studies primarily analyzed the importance of reproducing cross-correlation between precipitation and temperature (P-T cross-correlation) using various downscaling and weather generator schemes, leaving out how such biased estimates of P-T cross-correlation impact streamflow simulation and other hydrologic variables. The current study investigates the impacts of biased P-T cross-correlation on hydrologic variables using a fully coupled hydrologic model (Penn-state Integrated Hydrologic Model, PIHM). For this purpose, a synthetic weather generator was developed to generate multiple realizations of daily climate forcings for a specified P-T cross-correlation. Then, we analyzed how reproducing/neglecting P-T cross-correlation in climate forcings affect the accuracy of a hydrologic simulation. A total of 50 synthetic data sets of daily climate forcings with different P-T cross-correlation were forced into to estimate streamflow, soil moisture, and groundwater level under humid (Haw River basin in NC, USA) and arid (Lower Verde River basin in AZ, USA) hydroclimate settings. Results show that climate forcings reproducing the P-T cross-correlation yield lesser root mean square errors in simulated hydrologic variables (primarily on the sub-surface variables) as compared to climate forcings that neglect the P-T cross-correlation. Impacts of P-T cross-correlation on hydrologic simulations were remarkable to low flow and sub-surface variables whereas less significant to flow variables that exhibit higher variability. We found that hydrologic variables with lower internal variability (for example: groundwater and soil-moisture depth) are susceptible to the bias in P-T cross-correlation. These findings have potential implications in using univariate linear downscaling techniques to bias-correct GCM forcings, since univariate linear bias-correction techniques reproduce the GCM estimated P-T cross-correlation without correcting the bias in P-T cross-correlation.

中文翻译：

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降水与温度互相关在流域尺度水文变量模拟中的作用

摘要 气候强迫的不确定性导致在水文模型模拟中估算水流和其他地表通量时存在很大的不确定性。早期的研究主要分析了使用各种降尺度和天气发生器方案再现降水和温度之间的互相关（PT 互相关）的重要性，而忽略了这种对 PT 互相关的有偏估计如何影响水流模拟和其他水文变量。当前的研究使用完全耦合的水文模型（宾夕法尼亚州综合水文模型，PIHM）调查有偏差的 PT 互相关对水文变量的影响。为此，开发了一种合成天气发生器，以针对指定的 PT 互相关生成每日气候强迫的多种实现。然后，我们分析了在气候强迫中再现/忽略 PT 互相关如何影响水文模拟的准确性。共有 50 个具有不同 PT 互相关的每日气候强迫合成数据集被强制输入，以估计潮湿（美国北卡罗来纳州的霍河流域）和干旱（亚利桑那州的下佛得河流域）下的河流流量、土壤湿度和地下水位, 美国）水文气候设置。结果表明，与忽略 PT 互相关的气候强迫相比，再现 PT 互相关的气候强迫在模拟水文变量（主要是地下变量）中产生较小的均方根误差。PT 互相关对水文模拟的影响对低流量和地下变量显着，而对表现出较高可变性的流量变量则不太重要。我们发现内部变异性较低的水文变量（例如：地下水和土壤水分深度）容易受到 PT 互相关偏差的影响。这些发现对使用单变量线性降尺度技术对 GCM 强迫进行偏差校正具有潜在意义，因为单变量线性偏差校正技术可重现 GCM 估计的 PT 互相关，而无需校正 PT 互相关中的偏差。