Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models
Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models
复制标题
利用多种气候模型和水文模型开发径流预测的系统性不确定性减少策略
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Sankarasubramanian
中科院分区：
文献类型：
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作者：
Harminder Singh;A. Sankarasubramanian
最近的研究表明，多模式组合通过减少模式的不确定性来改善水文气候预测。考虑到气候预报可以从多个气候模型中获得，这些气候模型可以被多个流域模型所吸收，那么减少流量预报不确定性的最佳策略是什么？为了解决这个问题，我们考虑了三种可能的策略：(1)首先通过组合气候模式减少输入的不确定性，然后使用多个流域模式的多模式气候预报（MM‐P）；(2)摄取不同流域模式的个别气候预报（没有多模式组合），然后结合所有可能的气候和流域模式组合产生的流量预测（MM‐Q）；(3)将基于策略(1)的多个流域模型的流量预测结合起来，形成一个单一的流量预测，减少气候预报和流域模型（MM‐PQ）的不确定性。为此，我们考虑生成流量和气候预报的综合方案，以便将三种策略的性能与给定水文模型生成的真实流量进行比较。综合研究结果表明，通过结合气候预报首先减少输入不确定性（MM‐P），与将单个气候模式与各种水文模式的所有可能组合的流量预报结合起来（MM‐Q）所获得的多模式流量预报误差相比，减少了预测真实流量的误差。由于真正的水文模型结构是未知的，因此考虑MM - PQ作为减少输入不确定性和水文模型不确定性的替代选择是可取的。在华北两个流域的应用也表明，在降低水文模型的不确定性之前，首先要降低输入的不确定性。
Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce the input uncertainty first by combining climate models and then use the multimodel climate forecasts with multiple watershed models (MM‐P), (2) ingest the individual climate forecasts (without multimodel combination) with various watershed models and then combine the streamflow predictions that arise from all possible combinations of climate and watershed models (MM‐Q), (3) combine the streamflow forecasts obtained from multiple watershed models based on strategy (1) to develop a single streamflow prediction that reduces uncertainty in both climate forecasts and watershed models (MM‐PQ). For this purpose, we consider synthetic schemes that generate streamflow and climate forecasts, for comparing the performance of three strategies with the true streamflow generated by a given hydrologic model. Results from the synthetic study show that reducing input uncertainty first (MM‐P) by combining climate forecasts results in reduced error in predicting the true streamflow compared to the error of multimodel streamflow forecasts obtained by combining streamflow forecasts from all‐possible combination of individual climate model with various hydrologic models (MM‐Q). Since the true hydrologic model structure is unknown, it is desirable to consider MM‐PQ as an alternate choice that reduces both input uncertainty and hydrologic model uncertainty. Application on two watersheds in NC also indicates that reducing the input uncertainty first is critical before reducing the hydrologic model uncertainty.