Next generation of global water risk modelling

观测表明，气候变化增加了全球极端天气事件的频率和严重程度。干旱正变得越来越严重，洪水现在越来越频繁地影响世界许多地区。在自然变率的基础上，气候变化的加剧是人类目前面临的重大挑战之一。应对这些问题所需的措施包括智能地使用水文模型。一个新的领域是在全球范围内开发和应用这些模型，这是由可用数据和计算能力的巨大增长所实现的。除此之外，这些模型还被用于气候变化影响评估、洪水和季节性可用水量预测以及大坝项目决策。因此，全球水文模型在保险、适应和应急响应方面的应用是一个非常有价值的工具。基于物理的模型为其他类型的水文模型提供了一种替代工具，具有一些诱人的优势。它们提供了在集水区发生的物理过程的更接近的表示。这意味着，在气候和土地利用变化等非平稳条件下的模拟中，它们在理论上更为稳健。更简单的概念模型通常更适合全局建模，因为它们更容易和更快地设置和运行。它们在校准后通常也表现良好。然而，真正的全球建模意味着模拟未测量的流域，而直接校准通常是不可能的。这对概念模型提出了挑战，因为通常很难指定所有所需的参数。基于物理的模型有大量的数据和计算能力需求，这在历史上一直是它们使用的障碍。然而，近年来，可用于配置和驱动基于物理的水文模型的大陆和全球尺度数据集的数量和质量都有了极大的增长。对于降雨来说尤其如此。可用的数据来源包括基于仪表的、卫星的、再分析的和混合的产品。土壤和土地利用数据的收集也越来越好，通常是利用卫星产品，但所有国家仍然难以获得关于3D地质的直接信息。对于模型评估，传统的测量方法，如河流流量测量，越来越多地被遥感所补充。例如，土壤湿度变化的模式现在可以以前所未有的分辨率进行调查。使用基于物理的模型来帮助比较输入和评估，提供了一种探索不同数据产品的信息内容的方法。因此，这项研究将开发一个全球基于物理的水文模拟系统，以改进现有的业务水文预报。该项目的目标是：通过调整SHETRAN，在Azure等云计算平台上部署，创建一个全球性的、基于物理的水文建模系统。通过使用替代数据源，在流量计数据有限或没有流量计数据的区域改进自动验证。评估不同全球和国家水文数据集的信息内容及其对全球尺度分析的适用性。通过与欧洲中期天气预报中心（ECMWF）合作，评估基于物理的水文预报模型的价值，并将其全球洪水预警系统（GloFAS）的性能与本研究中建立的基于物理的建模系统进行比较。

Observations show that climate change has increased the frequency and severity of extreme weather events around the globe. Droughts are becoming more severe and flooding now affects many regions of the world with increasing frequency. This intensification in a changing climate on top of the natural variability is one of the big challenges currently facing humanity. Measures needed for coping with these issues include an intelligent use of hydrological models. A new field is the development and application of such models at the global scale, which is enabled by the vast increases in available data and computing power. Amongst other things, these models are being used in climate change impact assessments, forecasting of floods and seasonal water availability, and decision-making for dam projects. With applications in insurance, adaption and emergency response, global hydrological models thus are a very valuable tool. Physically based models offer an alternative tool to other types of hydrological models with some attractive advantages. They provide a closer representation of physical processes occurring in catchments. This means that they are theoretically more robust for simulations under non-stationary conditions, such as climate and land use change. Simpler, conceptual models are often favoured for global modelling, as they are easier and faster to set up and run. They also typically perform well after calibration. However, truly global modelling means simulating ungauged catchments, where direct calibration often is not possible. This presents a challenge to conceptual models, as it is often difficult to specify all of the required parameters. Physically based models have large data and computing power requirements which has historically acted as a barrier to their use. However, the number and quality of continental and global scale datasets available for configuring and driving a physically based hydrological model has grown immensely in recent years. This is particularly so for rainfall. Available data sources include gauge-based, satellite, reanalysis and blended products. There is also an increasingly good collection of soil and land use data, often drawing on satellite products, but direct information on 3D geology remains harder to obtain for all countries. For model evaluation, traditional measurements such as river flow gauging is increasingly complemented by remote sensing. For example, patterns of soil moisture variation can now be investigated at unprecedented resolution. Using a physically based model to aid comparison of input and evaluation provides a way to explore the information content of different data products. This research will therefore develop a global physically based hydrological modelling system to improve existing operational hydrological forecasts. The aims of this project are to: Create a global, physically based hydrological modelling system by adapting SHETRAN for deployment on a cloud computing platform such as Azure. Improve automated validation in areas with limited or no flow gauge data through use of alternative data sources. Assess the information content of different global and national hydrological datasets and their suitability for global scale analysis. Assess the value of physically based modelling for operational hydrological forecasting by collaborating with the European Centre for Medium-Range Weather Forecasts (ECMWF), comparing the performance of their Global Flood Awareness System (GloFAS) with the physically based modelling system set up in this research.