Efficient machine learning techniques for hydrological data assimilation and forecast post-processing

我的研究计划旨在开发新的数据同化和后处理技术，利用机器学习（ML）来改善水文集合预报。ML技术已经被研究人员应用于解决水文相关问题超过25年;到目前为止，最常见的应用是径流预测，其中ML技术（通常是神经网络）取代基于过程的水文模型。尽管如此，正如Abrahart等人（2012年）所指出的，除了极少数例外，业务预报机构不使用基于ML的模型进行流量预报。业务预报员和水资源管理人员对其决定负责，他们必须能够根据水文过程的合理推理支持这些决定。ML技术的黑盒性质可能是其在从业者中不受欢迎的原因之一。尽管如此，业务水文预报可以受益于ML技术的计算能力，如果它们被用来改善预测链的部分是严格的数学，与物理过程的关系不涉及。具体而言，ML可以改善（1）数据同化以及（2）偏差校正，气象预报和径流预报的后处理。神经网络（NN，一系列机器学习技术）可以学习输入和输出之间的几乎任何非线性关系。假设他们可以学习模拟的径流（从任何水文模型）和相应的状态变量之间的关系。一旦学会，这种关系可以被转置到观察到的，而不是模拟流量，以获得校正的状态变量。这一想法是本提案中为数据同化而制定的方法的核心。基于类似的原理，神经网络也可用于校正预测流量中的偏差和分散（后处理）。水文模型在输入气象观测数据后，会产生径流模拟结果作为输出。当气象预报输入时，水文模型产生径流预报作为输出。如果神经网络可以被训练来学习模拟和观察到的流量之间的关系，那么这种关系可以被应用到预测的流量，以纠正模型偏差（后处理）。拟议的研究将允许更有效的数据同化和后处理，以推进径流集合预报。后者对社会越来越重要，因为气候变化预计将改变极端事件的频率和规模。

My research program aims at developing new data assimilation and post-processing techniques that exploit machine learning (ML) to improve hydrological ensemble forecasts. ML techniques have been applied by researchers to solve hydrology-related problems for more than 25 years; the most common application, by far, is streamflow forecasting, where ML techniques (often neural networks) replace process-based hydrological models. Still, as noted in Abrahart et al. (2012), with very few exceptions, operational forecasting agencies do not use ML-based models for streamflow forecasting. Operational forecasters and water resource managers are held accountable for their decisions, and they must be able to support these decisions on sound reasoning based on hydrological processes. The black box nature of ML techniques may be one of the reasons for their lack of popularity among practitioners. Nevertheless, operational hydrological forecasting could benefit from the computing capabilities of ML techniques if they were used to improve portions of the forecasting chain that are strictly mathematical, where relationships with physical processes are not involved. Specifically, ML could improve (1) data assimilation as well as (2) bias correction, post-processing of meteorological forecasts and post-processing of streamflow forecasts. Neural networks (NN, a family of machine-learning techniques) can learn almost any non-linear relationship between inputs and outputs. It is assumed that they could learn the relationship between the simulated streamflow (from any hydrological model) and the corresponding state variables. Once learned, this relationship could be transposed to observed rather than simulated streamflow to obtain corrected state variables. This idea is at the core of the methodology developed for data assimilation in this proposal. Based on a similar principle, NN could also be used to correct bias and dispersion (post-processing) in forecasted streamflows. When fed with meteorological observations, a hydrological model produces streamflow simulations as outputs. When fed with meteorological forecasts, the hydrological model produces streamflow forecasts as outputs. If neural networks could be trained to learn the relationships between simulated and observed streamflow, then this relationship could be applied to forecasted streamflow for correcting model bias (post-processing). The proposed research will allow for more efficient data assimilation and post-processing to advance streamflow ensemble forecasting. The latter is increasingly important for society, as climate change is expected to modify the frequency and magnitude of extreme events.