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

在气象研究中，以一种简化和统一的方式对从一天到一个季节的交货时间进行无缝预测是一种相对新颖的方法；它提供了当两个以前分离的研究分支合并时经常发现的互利。另一方面，基于集合模拟的概率预报是业务气象预报的事实标准，它也正在成为水文预报的标准。然而，气象和水文组合的基本物理和统计数据非常不同，特别是在整个前置时间范围内。气象可预测性的主要制约因素是初始状态的不确定性：一旦初始化，每个集成成员就会根据确定性动力学发展，这在很大程度上表征了气象可预测性是提前期的函数。当然，这也是水文可预测性的一个主要制约因素，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、不确定的参数和（如气象学）不确定的初始状态。结构和参数的不确定性是如此重要，因为对气象学来说，流域是一个比全球大气更多样化的研究对象。因此，在无缝预测框架下同时处理气象和水文不确定性构成了一个重大挑战，应对这一挑战代表了我们提议的SHIVA项目中三个任务中的两个。第三个任务处理给定的无缝概率流预测。我们分析了气象和水文不确定性对总体预测的相对贡献。它们可以使用双向方差分析（ANOVA）对每个提前期的预测流量进行量化。有三个方面值得注意：1)我们对短期、中期和长期使用专门的预测；2)对于任何一个提前期，气象和水文的不确定性揭示了错误的重要来源，也许还有可能的补救措施；3)虽然单个前置时间的不确定性估计可能受到数据不足的影响，但应该可以定义一个平滑函数，将前置时间映射为相对不确定性，从而揭示无缝概率流预测的主要局限性。东印度的Mahanadi集水区（A_C = 141,500平方公里）是进行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可能性更高。其次，从几天到几个月不等的流量预报对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。

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.