Improving global hydrological simulations through bias-correction and multi-model blending

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization's (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment's Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions

The multiscale routing model mRM v1.0: simple river routing at resolutions from 1 to 50 km

Routing streamflow through a river network is a fundamental requirement to verify lateral water fluxes simulated by hydrologic and land surface models. River routing is performed at diverse resolutions ranging from few kilometres to 1∘. The presented multiscale routing model mRM calculates streamflow at diverse spatial and temporal resolutions. mRM solves the kinematic wave equation using a finite difference scheme. An adaptive time stepping scheme fulfilling a numerical stability criterion is introduced in this study and compared against the original parameterisation of mRM that has been developed within the mesoscale hydrologic model (mHM). mRM requires a high-resolution river network, which is upscaled internally to the desired spatial resolution. The user can change the spatial resolution by simply changing a single number in the configuration file without any further adjustments of the input data. The performance of mRM is investigated on two datasets: a high-resolution German dataset and a slightly lower resolved European dataset. The adaptive time stepping scheme within mRM shows a remarkable scalability compared to its predecessor. Median Kling–Gupta efficiencies change less than 3 % when the model parameterisation is transferred from 3 to 48 km resolution. mRM also exhibits seamless scalability in time, providing similar results when forced with hourly and daily runoff. The streamflow calculated over the Danube catchment by the regional climate model REMO coupled to mRM reveals that the 50 km simulation shows a smaller bias with respect to observations than the simulation at 12 km resolution. The mRM source code is freely available and highly modular, facilitating easy internal coupling in existing Earth system models

Tracking large-scale simulations through unified metadata handling

Simulation is an essential pillar of knowledge generation in science. The numerical models used to describe, predict, and understand real-world systems are typically complex. Consequently, applying these models by means of simulation often poses high demands on computational resources, and requires high-performance computing (HPC) or other dedicated hardware architectures. Metadata describing the details of a numerical experiment arise at all stages of the simulation process: the conceptual description of the model, the model implementation, and the tools and machines used to run the simulation. Capturing these metadata and provenance information along the processing chain is a vital requirement for several purposes, e.g. reproducibility, benchmarking and validation, assessment of the reliability of the simulations, and data exploration [1,2]. The ability to search, share, and evaluate metadata and provenance traces from heterogeneous simulations and environments is a major challenge in provenance-driven analysis. The availability of a common metadata framework, which can be adopted by scientists from different scientific domains, would foster the meta-analysis of HPC simulation workflows [3]. Here, we develop a metadata management framework for generic HPC-based simulation research comprising concepts and tools for efficiently generating, organizing, and exploring metadata along a given simulation workflow. The derived solutions cope with the modularity and flexibility demands of rapidly progressing science and are applicable to diverse research fields. As a proof of concept, we will apply these solutions to use cases from environmental research and computational neuroscience.[1] Guilyardi, E., et. al. (2013) doi: 10.1175/BAMS-D-11-00035.1[2] Manninen, T., et. al. (2018) doi: 10.3389/fninf.2018.00020[3] Ivie, P., & Thain, D. (2018). doi: 10.1145/318626

Improving global hydrological simulations through bias-correction and multi-model blending

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization’s (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment’s Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions