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Seamless Hydrological prediction of east Indian summer monsoon and Variance Analysis of its meteorological and hydrological uncertainty (SHIVA)

东印度夏季风的无缝水文预测及其气象和水文不确定性的方差分析（SHIVA）

基本信息

批准号：
265653116
负责人：
Professor Dr.-Ing. Axel Bronstert
金额：
--
依托单位：
Institut für Umweltwissenschaften und Geographie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/265653116?language=en
关键词：
Seamless Hydrological prediction east Indian

项目摘要

Conducting predictions seamlessly, in a streamlined and uniform way for lead times ranging from one day to one season, is a relatively novel method in meteorological research; it provides the mutual benefit that is often found when two formerly separated research branches are merged. Probabilistic predictions by means of ensemble simulations, on the other hand, is the de-facto standard in operational meteorological forecasting, and it is becoming a standard in hydrological forecasting as well. The underlying physics and statistics of meteorological and hydrological ensembles are very different, however, especially along the full range of lead times. The main constraint for meteorological predictability is the uncertain initial state: once initialized, each ensemble member evolves according to deterministic dynamics, which largely characterizes meteorological predictability as a function of lead time. Naturally, that is one major constraint for hydrological predictability as well, but other factors, collectively termed hydrological uncertainty, are at least as important: an imperfect modeling structure, uncertain parameters, and (like in meteorology) uncertain initial states. Structural and parameter uncertainty are so important because a river basin is a far more heterogeneous object of study than it is the global atmosphere for meteorology. Simultaneous treatment of meteorological and hydrological uncertainties in the framework of seamless prediction poses a major challenge, accordingly, and coping with this challenge represents two out of three tasks in our proposed project SHIVA.The third task deals with a given seamless probabilistic streamflow forecast. We analyze the relative contributions of meteorological and hydrological uncertainty to the overall prediction. They can be quantified using a two-way analysis of variance (ANOVA) of the predicted streamflow for each lead time. Three aspects deserve attention: 1) we use dedicated predictands for the short, medium, and long range; 2) for any single lead time the meteorological and hydrological uncertainties reveal important sources of errors and, perhaps, possible remedies; 3) while the uncertainty estimates for the single lead times are likely affected by data insufficiencies, it should be possible to define a smooth function that maps lead times to relative uncertainties, revealing the principal limitations of seamless probabilistic streamflow prediction. The Mahanadi Catchment (A_C = 141,500km²) of East India constitutes an ideal site for the planned research. First, due to the strong atmosphere-ocean interaction in the tropical Monsoon belt, the potential for making skillful long-term predictions is high compared to, for example, the mid latitudes. Second, streamflow forecasts covering lead times from days to months are of utmost importance for effective water resources management in the Mahanadi Basin, including flood warning and control, reservoir operation, and irrigation.

在气象研究中，以一种精简统一的方式无缝地进行预测，从一天到一个季节，是一种相对新颖的方法;它提供了两个以前独立的研究分支合并时经常发现的互惠互利。另一方面，通过集合模拟进行的概率预报是业务气象预报的事实上的标准，它也正在成为水文预报的标准。然而，气象和水文集合的基本物理和统计是非常不同的，特别是沿着整个提前期。气象可预报性的主要约束是不确定的初始状态：一旦初始化，每个集合成员根据确定性动力学发展，这在很大程度上表征了气象可预报性作为提前时间的函数。当然，这也是水文可预测性的一个主要约束，但其他因素，统称为水文不确定性，至少同样重要：不完善的建模结构，不确定的参数，以及（如气象学）不确定的初始状态。结构和参数的不确定性非常重要，因为对于气象学来说，河流流域是一个远比全球大气更异质的研究对象。在无缝预测的框架内同时处理气象和水文的不确定性是一个重大挑战，因此，应对这一挑战是我们提出的项目SHIVA的三项任务中的两项。我们分析了气象和水文不确定性对整体预测的相对贡献。它们可以量化使用的双向方差分析（ANOVA）的预测流量为每个前置时间。三个方面值得关注：1）我们使用短期、中期和长期的专用预报量; 2）对于任何单一的提前期，气象和水文的不确定性揭示了重要的误差来源，也许还有可能的补救措施; 3）虽然单个提前期的不确定性估计可能受到数据不确定性的影响，但应该可以定义一个平滑函数，将提前期映射到相对不确定性，揭示了无缝概率径流预测的主要局限性。印度东部的Mahanadi流域（A_C = 141，500 km ²）是计划研究的理想地点。首先，由于热带季风带强烈的大气-海洋相互作用，与中纬度地区相比，进行熟练的长期预测的潜力很大。第二，涵盖从几天到几个月的前置时间的径流预测对于Mahanadi流域的有效水资源管理至关重要，包括洪水预警和控制、水库运行和灌溉。