Large-sample comparative hydrologic modelling computational laboratory

拟议的研究计划将建立一个新的大样本水文模型计算实验室，世界各地的环境建模人员将利用并受益于该实验室。研究计划的目标是：1)进行开放、比较、大样本的分布式水文模型相互比较研究和模型挑战，以启发、基准并随后利用随时间产生的知识2)了解深度学习(例如人工智能)水文模型方法相对预测能力背后的原因，以便设计下一代混合数据驱动和水文模型3)发现新的水文模型校准方法，增强我们模拟和预测未来条件(如洪水和干旱)的能力，4)设计和进行独特的大样本比较实证测试，帮助向模型师清楚地展示哪种模型发展选择效果最好。与我一起工作的学生将接受现代水文建模软件(Raven水文建模框架)的培训，目前加拿大各地的行业和政府流量预报机构以及世界各地的国际研究人员都在使用该软件。学生还将接受培训，并为下一代水文建模方法做好准备，这些方法利用传统水文建模方法的大数据革命。改进的水文模型和模型开发过程通过生成更准确的洪水和干旱预测来改善公共安全。实地进展的成果将包括：1)改进传统的水文模型建立方法；2)新一代模型；3)新的基准模型对比研究。从根本上说，我所在领域的进展只能通过研究界的基准测试标准(NEAR等人，2021年)来跟踪，而创建一个易于访问的大数据存储库的这种基准测试努力被视为我所在领域的最佳投资之一(NEARING等人，2021年)。我的团队将与世界各地的许多研究人员合作建立这样的基准，因此他们将实际使用它们，然后重要的是，随着时间的推移，进一步推广它们的使用。总体而言，5年后，我的大样本比较水文模型计算实验室将成为我这个领域的从业者和研究人员找到世界级证据的地方，他们相信这些证据可以指导他们如何建立水文模型。专家们还将进行虚拟访问，以便与我的团队和来自世界各地的其他人合作，以客观的方式比较他们的水文模型，这将推动我们领域的科学发展。

The proposed research program will build a new large-sample hydrological modelling computational laboratory that environmental modellers around the world will utilize and benefit from. Research program goals are to: 1) produce open and comparative, large-sample distributed hydrologic modelling intercomparison studies and model challenges to inspire, benchmark and then leverage knowledge generation over time 2) seek to understand the reasons behind the relative predictive power of deep learning (e.g., Artificial Intelligence) methods for hydrological modelling in order to design next generation hybrid data-driven and hydrological models 3) discover new hydrologic model calibration methods that enhance our ability to simulate and predict future conditions such as floods and drought and 4) design and conduct unique, large-sample comparative empirical tests that help clearly demonstrate to modellers which model development choices work best. Students working with me will be trained on modern hydrological modelling software (The Raven Hydrological Modelling Framework) used today across Canada by industry and governmental flow forecasting agencies, as well as international researchers around the world. Students will also be trained and ready for next generation hydrological modelling approaches that leverage the big data revolution with traditional hydrologic modelling approaches. Improved hydrological models and model development processes improve public safety by generating more accurate predictions of flooding and drought. Field advancement outcomes will include: 1) improved traditional hydrological model building methods 2) next-generation models and 3) new benchmark model intercomparison studies. Fundamentally, the progress in my field can only be tracked via a research community standard for benchmarking (Nearing et al., 2021) and such a benchmarking effort to create an easily accessible big data repository is viewed as one of the best investments in my field (Nearing et al., 2021). My team will have built such benchmarks in collaboration with numerous researchers around the world who will therefore actually use them and then importantly propagate their further use over time. Overall, after 5 years, my large-sample comparative hydrological modelling computational laboratory will be a place where both practitioners and researchers in my field come to find world-class evidence they trust that guides how they build their hydrological models. Experts will also virtually visit in order to collaborate with my team and others from around the world to compare their hydrological models in an objective way that will advance the science in our field.