extended streamflow prediction ——ESP的起源以及定义

19.2.15 更新

ESP特指是  extended streamflow prediction

ESP; i.e. note that ESP nowadays stands for ensemble streamflow prediction, although it refers to the same forecasting method

参考文献:

Skilful seasonal forecasts of streamflow over Europe?


18.11.28 更新

“One thing you need to know about seasonal forecasts”

“关于季节性预测你需要知道的一件事”

1985年Day提出的扩展流量预测(ESP)框架仍然是用于长期流量预测的系统(例如García-Morales和Dubus,2007; Singh,2016)。在此框架内,过去的气象观测被视为等概率潜在的未来情景这些情景被提供给水文模型。ESP与人类根据我们对过去经历的记忆判断实际情况的倾向是一致的。

来自S2S / S2D会议的四条重要信息
我们确定了与水文界有关的四条关键信息。


今日阅读了关于ESP的两篇帖子。

1.TRACING THE ORIGINS OF ESP  https://hepex.irstea.fr/tracing-the-origins-of-esp/

2.SKILFUL SEASONAL FORECASTS OF STREAMFLOW OVER EUROPE?   https://hepex.irstea.fr/skilful-seasonal-streamflow-europe/

先写一部分,我目前的理解。随着理解的加深,将进行更新。


第一篇  注意,本篇博客是先写的第二篇,后写的第一篇。阅读起来,请注意逻辑关系。

第一段:

洪水预报是联邦的责任,理论上,长期预报是一项联合活动。长期预测的两个部分是:
1.连续土壤水分会计模型(萨克拉门托模型,由NWS开发; Burnash等,1973)和
2.将估计的初始水文条件(IHC)与基于历史序列季节性气候预测的未来气象预报相结合的概念,以推动水文模拟延伸到季节性提前期。

Flood forecasting was a federal responsibility, and in theory, longer-range forecasts were a joint activity. The two parts of longer range forecasts were:
1.a continuous soil moisture accounting model (the Sacramento model, developed by NWS; Burnash et al, 1973) and
2.the concept of combining estimated initial hydrologic conditions (IHCs) with future meteorological forecasts based on historical sequences or seasonal climate predictions to drive hydrologic simulations extending out to seasonal lead times.

——和第二篇中的两类方法一致,模型+A or B,另外,不出所料,第二篇中的ESP确实是特指。

第二段:

ESP的两个首要主要应用是干旱相关 – 加利福尼亚干旱期间的预测,以及弗吉尼亚州较小但强烈的干旱,影响了Occoquan水库,它构成了华盛顿特区供水的一部分。基于ESP和其他分析技术,可以编制储层目标水平故障的概率预测。今天,我们认为这种方法是理所当然的,但当时它们是水文学中随机方法的创新应用。


第二篇

第一段:

近几十年来,季节性流量预测方法已经发展和多样化,反映了我们对季节性时间尺度的流量可预测性和我们不断增加的计算机能力的科学理解的变化。第一个基于运行模型的集合季节性流量预测,称为ESP[1,2](集合流量预测),依赖于对初始水文条件(IHC;即积雪,土壤湿度,溪流和水库水位等)的正确认识。 )和一个大的地表记忆,并没有关于未来气候的信息。(如果你想了解关于ESP的更多信息,我建议阅读这篇关于它的非常好的HEPEX博客文章!)。

Over recent decades, seasonal streamflow forecasting methods have evolved and diversified, reflecting changes in our scientific understanding of streamflow predictability on seasonal timescales and our increasing computer power. The first operational model-based ensemble seasonal streamflow forecast, called the ESP[1,2] (ensemble streamflow prediction), relies on the correct knowledge of the initial hydrological conditions (IHC; i.e. of snowpack, soil moisture, streamflow and reservoir levels, etc.) and a large land surface memory, and contains no information on the future climate. (if you’d like to know more about the ESP, I recommend reading this very good HEPEX blog post about it!).

——Some morphometric variables store information about past land surfaces longer than others. This property of morphometric variables is recognised as land surface memory. 一些形态变量存储有关过去陆地表面的信息比其他变量更长。形态变量的这个属性被识别为地表记忆

——这里的ESP是特指的一个方法【在参考文献中使用】???不包含未来的气象信息???

第二段:

然而,在气象强迫驱动流量可预测性的盆地中,最后一点是ESP的限制。这促使季节性气候预报的使用为水文模型提供补充,并扩大水文变量对季节性时间尺度的可预测性[3],我们将其称为基于气候模型的季节性流量预测(CM-SSF)。虽然在欧洲之外探索CM-SSF技术的研究很多,但在这个地区仍然相对稀缺,部分原因是季节性气候预报的质量有限(特别是对水文感兴趣的变量:降水和温度) )对于热带地区。

In basins where the meteorological forcings drive the streamflow predictability however, this last point is a limitation of the ESP. This motivates the use of seasonal climate forecasts to feed hydrological models and extend the predictability of hydrological variables on seasonal timescales[3], which we refer to as climate-model-based seasonal streamflow forecasts (CM-SSF). While studies exploring the skill of CM-SSF are abundant outside of Europe, they are still relatively scarce in this part of the world, partly due to the limited quality of seasonal climate forecasts (particularly for the variables of interest to hydrology: precipitation and temperature) for the extra-tropics.

——CM-SSF 包含了预报的climate信息

第三段:

在我们最近的论文,发表在上HESS专刊分季节到季节水文预报,我们进行了使用原料的新业务EFAS的技能的欧洲范围的分析(欧洲洪水感知系统)CM-SSF(生产ECMWF系统4季节气候预报[Sys4]作为Lisflood水文模型的输入),以ESP为基准(使用历史气象观测作为Lisflood的输入产生;两种预测均为7个月的预测时间)。以下是本文中提到的两个主要问题。

In our recent paper, published in the HESS special issue on Sub-seasonal to seasonal hydrological forecasting, we carried out a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) CM-SSF (produced using the raw ECMWF System 4 seasonal climate forecast [Sys4] as an input to the Lisflood hydrological model), benchmarked against the ESP (produced using historical meteorological observations as an input to Lisflood; both forecasts go out to 7 months lead time). Below are the two main questions tackled in this paper.

——居然有专刊。。。哭,又有文献要阅读了。。。

——CM-SSF=seasonal climate forecast+Lisflood hydrological model, ESP=historical meteorological observations+Lisflood.

——懂了他俩的区别!!!输入不同!!!

第四段:

季节性气候信息是否会提高季节性流量对欧洲的可预测性?
总的来说,我们发现Sys4仅提高了过去一个月的泛欧季节性流量预测的历史气象信息的可预测性(在准确性,清晰度和整体性能方面)。这表明了IHC和地表记忆对于欧洲季节性流量预测的重要性。然而,每个季节的可预测性不同,我们表明CM-SSF在预测秋季和冬季流量方面比春季和夏季更有技巧。我们的研究结果表明,CM-SSF技术中的模式并未反映在Sys4降水和温度后报中,这需要更深入地研究欧洲季节性时间尺度上气象和水文变量之间的联系。

Does seasonal climate information improve the predictability of seasonal streamflow over Europe?
Overall, we found that Sys4 improves the predictability over historical meteorological information for pan-European seasonal streamflow forecasting for the first month of lead time only (in terms of accuracy, sharpness and overall performance). This shows the importance of the IHC and the land surface memory for seasonal streamflow forecasting in Europe. However, the predictability varies per season and we show that the CM-SSF is more skilful at predicting autumn and winter streamflows than for the spring and summer.Our findings suggest that patterns in the CM-SSF skill are not mirrored in the Sys4 precipitation and temperature hindcasts, which calls for a more in depth look into the link between meteorological and hydrological variables on seasonal timescales over Europe.

——传统方法ESP也不错,新方法还需要深入研究。

第五段:

EFAS季节性流量预测在防洪方面的潜在用途是什么?
随着季节性流量预测质量的提高,其决策的可用性已经落后。将预测质量转化为决策的附加值对于短期预测来说并不简单,更不用说季节性时间尺度了。虽然已经针对许多与水有关的应用进行了探索,例如导航4,水库管理5,水资源管理6和水电7等,但洪水准备社区尚未采用季节性流量预测。在本文中,我们另外研究了CM-SSF和ESP预测未来7个月的低于和高于正常的流量条件的能力,我们称之为潜在的有用性。
在这里,我们仅显示高于正常流量的结果(参见下图); 低于正常流量的结果可以在论文中找到。总的来说,我们发现两个预测系统中至少有一个能够提前几个月预测高于正常的流量,ESP通常是最具潜在用途的系统。然而,我们的结果表明,对于某些欧洲地区和季节,CM-SSF在超过1个月的交付时间内比ESP更具潜在用途,在冬季明显占欧洲约40%。

What is the potential usefulness of the EFAS seasonal streamflow forecasts for flood preparedness?

As the quality of seasonal streamflow forecasts increases, their usability for decision-making has lagged behind. Translating forecast quality into added value for decision-making is not straightforward for short-range forecasting, let alone on seasonal timescales. While this has been explored for many water-related applications, such as navigation4, reservoir management5, water resource management6 and hydropower7, among others, seasonal streamflow forecasts have yet to be adopted by the flood preparedness community. In this paper, we additionally investigated the ability of the CM-SSF and the ESP to predict lower and higher than normal streamflow conditions up to 7 months ahead, which we term potential usefulness.

Here, we show results for higher than normal streamflows only (see the figure below); results for lower than normal streamflows can be found in the paper. Overall, we found that at least one of the two forecasting systems is capable of predicting higher than normal streamflows months in advance, with the ESP the most potentially useful system generally. However, our results suggest that the CM-SSF is more potentially useful than the ESP beyond 1 month of lead time for certain European regions and seasons, noticeably in winter for ~40 % of Europe.

——难用。

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