Accurate streamflow forecasts enable the appropriate management of water resources. Although there is a general consensus that climate information can enhance hydrological predictability, this might not be the case if the accuracy of the given climate information is unreliable. Hence, this study has developed a modeling framework to estimate the role of climate information in forecasting accurate streamflow. Ensemble streamflow prediction (ESP) technology was adopted as a dynamic hydrologic forecast method to 35 watersheds in South Korea. The probabilistic precipitation forecast (PPF), issued by the Korea Meteorological Administration, was used as climate information for updating the probabilities of climate scenarios. First, we found that the current PPF is not accurate enough for significantly enhancing the streamflow forecasting accuracy. Subsequently, multiple sets of PPF were synthetically generated to evaluate the role of climate information. Given the perfect categorical climate forecasts, we found that there is much potential for the enhancement of streamflow forecast skill especially in the seasons that exhibit greater streamflow variability. However, there is less potential for increasing the streamflow forecasting skill under below-normal conditions. The proposed modeling framework is capable of quantifying the magnitude of potential improvement in hydrological predictability under the assumption that better climate information will be available in the future. We expect that this modeling framework can be effectively applied to other regions across a wide range of climate regimes.

准确的流量预测可以对水资源进行适当的管理。尽管人们普遍认为气候信息可以增强水文可预测性，但是如果给定气候信息的准确性不可靠的话，情况可能并非如此。因此，本研究开发了一个建模框架来估算气候信息在预测准确流量中的作用。集合流预测（ESP）技术被用作韩国35个流域的动态水文预测方法。由韩国气象局发布的概率降水预报（PPF）被用作更新气候情景可能性的气候信息。首先，我们发现当前的PPF不够准确，无法显着提高流量预测的准确性。随后，合成了多组PPF，以评估气候信息的作用。给定完美的分类气候预测，我们发现提高流量预报技能的潜力很大，尤其是在流量变化较大的季节。但是，在低于正常水平的条件下，增加流量预报技能的潜力较小。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。综合生成了多组PPF，以评估气候信息的作用。给定完美的分类气候预测，我们发现提高流量预报技能的潜力很大，尤其是在流量变化较大的季节。但是，在低于正常水平的条件下，增加流量预测技能的潜力较小。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。综合生成了多组PPF，以评估气候信息的作用。给定完美的分类气候预测，我们发现提高流量预报技能的潜力很大，尤其是在流量变化较大的季节。但是，在低于正常水平的条件下，增加流量预报技能的潜力较小。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。我们发现提高流量预报技能的潜力很大，特别是在流量变化较大的季节。但是，在低于正常水平的条件下，增加流量预报技能的潜力较小。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。我们发现提高流量预报技能的潜力很大，特别是在流量变化较大的季节。但是，在低于正常水平的条件下，增加流量预测技能的潜力较小。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。假设未来会获得更好的气候信息，那么拟议的建模框架能够量化水文可预测性潜在改善的幅度。我们希望该建模框架可以有效地应用于各种气候条件下的其他区域。