Seasonal hydrological forecasts are vital for managing water resources and adapting to climate change, aiding in diverse planning and decision-making processes. Currently it is unknown how different forecasting methods, considering initial hydrological conditions and dynamic meteorological forcing, perform across the Swedish river systems, despite the significant socio-economic implications. Here we explore the drivers that mostly impact streamflow predictions and attribute the added quality of these predictions to local hydrological regimes. We compare the accuracy of seasonal streamflow forecasts driven by dynamic GCM-based meteorological forecasts with those generated by the Ensemble Streamflow Prediction (ESP) method. The analysis spans across about 39,500 sub-catchments in Sweden encompassing various climatic, geographical and human-influenced factors. Results show that the streamflow predictability varies in space due to the country’s diverse hydrological regimes. Regardless of the regime, updating the models to achieve the best possible initial conditions is crucial for enhancing forecast skill across all seasons for up to 4 months. GCM-based meteorological forcing notably improves short-term streamflow accuracy, showing significant impact particularly up to 4–8 weeks lead time depending on the local hydrological regime. In the snow-driven northern regions, ESP demonstrates superior performance over GCM-based streamflow forecasts in winter. Conversely, in the southern regions, where conditions are predominantly influenced by rainfall, GCM-based forecasts show higher performance up to 4–6 weeks ahead, regardless of the season. In river systems with high human influences, streamflow climatology outperforms ESP and GCM-based forecasts underscoring the challenges of accurately modelling artificial reservoir management and the need for better access to management data. These insights guide the development of an advanced national seasonal hydrological forecasting service, and highlight the need for region-specific forecasting strategies indicating areas where predictability is enhanced by improved monitoring, hence initial conditions, and/or meteorological forcings. Finally, we discuss the applicability of these forecasting methods to other regions worldwide, thereby placing our new insights within a global context.

中文翻译：

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利用基于 GCM 的预测来增强不同水文状况下的季节性径流预测


季节性水文预报对于管理水资源和适应气候变化至关重要，有助于多样化的规划和决策过程。目前尚不清楚考虑到初始水文条件和动态气象强迫的不同预报方法在瑞典河流系统中如何发挥作用，尽管存在重大的社会经济影响。在这里，我们探讨了主要影响径流预测的驱动因素，并将这些预测的提高质量归因于当地的水文状况。我们将基于 GCM 的动态气象预报驱动的季节性径流预报的准确性与集成径流预测 （ESP） 方法生成的预报的准确性进行了比较。该分析跨越瑞典约 39,500 个子流域，包括各种气候、地理和人为影响因素。结果表明，由于该国不同的水文状况，径流的可预测性在空间上有所不同。无论采用何种制度，更新模型以实现最佳初始条件对于提高所有季节的预报技能至关重要，最长可达 4 个月。基于 GCM 的气象强迫显著提高了短期径流的准确性，根据当地的水文状况，显示出显着的影响，特别是长达 4-8 周的提前期。在降雪驱动的北部地区，ESP 在冬季表现出优于基于 GCM 的径流预报的性能。相反，在条件主要受降雨影响的南部地区，基于 GCM 的预报显示，无论季节如何，未来 4-6 周的表现都会更高。 在人类影响较大的河流系统中，径流气候学优于基于 ESP 和 GCM 的预测，这凸显了准确模拟人工水库管理的挑战以及更好地获取管理数据的需求。这些见解指导了先进的国家季节性水文预报服务的发展，并强调了针对特定区域的预报策略的必要性，这些策略表明通过改进监测、初始条件和/或气象强迫来增强可预测性的地区。最后，我们讨论了这些预测方法对全球其他地区的适用性，从而将我们的新见解置于全球背景下。