Investigating the Role of Snow Water Equivalent on Streamflow Predictability during Drought
Investigating the Role of Snow Water Equivalent on Streamflow Predictability during Drought
复制标题
研究干旱期间雪水当量对径流可预测性的作用
DOI：
10.1175/jhm-d-21-0229.1
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发表时间：
2022
影响因子：
3.8
通讯作者：
Livneh, Ben
中科院分区：
文献类型：
--
作者：
Modi, Parthkumar A.;Small, Eric E.;Kasprzyk, Joseph;Livneh, Ben
积雪为美国西部以雪为主的流域的供水预报（WSF）提供了大部分预测信息。干旱条件通常伴随着积雪减少和径流效率降低，对WSF产生负面影响。在这里，我们调查雪水当量（SWE）和4月至7月径流量（AMJJ-V）在干旱期间在小源头集水区之间的关系，从31个USGS流量计和54个SNOTEL站的观测。一个线性回归的方法是用来评估预测技术在不同的历史气候模式拟合，以及与不同的预测日期。实验中，极端水文干旱年被扣留模型训练，即AMJJ-V低于第15百分位数的年份。剩余年份的子集用于模型拟合，以了解不同训练子集的气候如何影响极端干旱年份的预测。我们通常在干旱年份报告过度预测。然而，在干旱年份（即低于中位数的年份）训练预测模型（P15，P57.5），相对于基线情况，干旱年份预测的残差平均最小化10%，对于寒冷地区，在4月中下旬获得最高中位数技能。我们报告类似的调查结果，使用修改后的国家资源保护局（NRCS）的程序在9个大的上科罗拉多河流域（UCRB）流域，突出积雪径流关系的重要性，在径流的可预测性。我们提出了一个“自适应抽样”的方法，动态选择训练年的基础上先行SWE条件，显示误差减少了20%，在历史干旱年份相对于记录期间。这些替代的培训协议提供了机会，以解决未来的干旱风险供水planning.Significance StatementSeasonal供水预测的基础上的峰值积雪和供水之间的关系表现出独特的错误，在干旱年份由于低雪和径流的变化，供水预测提出了一个重大挑战。在这里，我们使用固定的预测日期或固定的模型训练期，评估在干旱年份基于雪的径流可预测性的可靠性。我们批判性地评估了不同的训练协议，这些协议评估了预测性能，并确定了历史干旱年份的错误来源。我们还提出并测试了一个“自适应采样”应用程序，该应用程序根据先前的SWE条件动态选择训练年份，以克服持续的错误，并为雪引导预报提供新的见解和策略。
Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P15,P57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an “adaptive sampling” approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning.Significance StatementSeasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an “adaptive sampling” application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts.