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

在气象研究中，以简化和统一的方式以从一天到一个季节的时间进行无缝预测是一种相对新颖的方法；它提供了以前分离的两个研究部门合并时经常出现的互惠互利。另一方面，集合模拟的概率预报是业务气象预报的事实标准，也正在成为水文预报的标准。然而，气象和水文总体的基本物理和统计数据非常不同，特别是在整个筹备时间范围内。气象可预报性的主要制约因素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演化，这在很大程度上表征了气象可预报性是提前时间的函数。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，至少也同样重要：不完善的建模结构，不确定的参数，以及(就像气象学中的)不确定的初始状态。结构和参数的不确定性是如此重要，因为与气象学的全球大气相比，河流流域是一个更加多样化的研究对象。因此，在无缝预报框架内同时处理气象和水文不确定性是一个重大挑战，而应对这一挑战是我们建议的项目SHIVA中的三项任务中的两项。第三项任务涉及给定的无缝概率径流预报。我们分析了气象和水文不确定性对总体预报的相对贡献。它们可以使用对每个提前期的预测流量的双向方差分析(ANOVA)来量化。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报；2)对于任何单个提前期，气象和水文不确定性揭示了重要的误差来源，并可能揭示可能的补救措施；3)虽然对单个提前期的不确定性估计可能受到数据不足的影响，但应该有可能定义一个将提前期映射到相对不确定性的平滑函数，从而揭示无缝概率径流预报的主要局限性。东印度的马哈纳迪流域(A_C=141,500平方公里)是计划进行研究的理想地点。首先，由于热带季风带中强烈的大气-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高。其次，涵盖从几天到几个月的提前期的径流预报对于马哈纳迪盆地的有效水资源管理至关重要，包括洪水预警和控制、水库调度和灌溉。

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.