Preprocessing of hydrological models’ input in eWaterCycle with ESMValTool

Abstract

<p>Hydrological models exhibit great complexity and diversity in the exact methodologies applied, competing for hypotheses of hydrologic behaviour, technology stacks, and programming languages used in those models. The preprocessing of forcing (meteorological) data is often performed by various sets of scripts that may or may not be included with model source codes, making it hard to reproduce results. Moreover, forcing data can be retrieved from a wide variety of forcing products with discrepant variable names and frequencies, spatial and temporal resolutions, and spatial coverage. Even though there is an infinite amount of preprocessing scripts for different models, these preprocessing scripts use only a limited set of operations, mainly re-gridding, temporal and spatial manipulations, variable derivation, and unit conversion. Also, these exact same preprocessing functions are used in analysis and evaluation of output from Earth system models in climate science.</p><p>Within the context of the eWaterCycle II project (https://www.ewatercycle.org/), a common preprocessing system has been created for hydrological modelling based on ESMValTool (Earth System Model Evaluation Tool). ESMValTool is a community-driven diagnostic and performance metrics tool that supports a broad range of preprocessing functions. Using a YAML script called a recipe, instructions are provided to ESMValTool: the datasets which need to be analyzed, the preprocessors that need to be applied, and the model-specific analysis (i.e. diagnostic script) which need to be run on data. ESMValTool is modular and flexible so all preprocessing functions can also be used directly in a Python script and additional analyses can easily be added.</p><p>The current preprocessing pipeline of the eWaterCycle using ESMValTool consists of hydrological model-specific scripts and supports ERA5 and ERA-Interim data provided by the ECMWF (European Centre for Medium-Range Weather Forecasts), as well as CMIP5 and CMIP6 climate model data. The pipeline starts with the downloading and CMORization (Climate Model Output Rewriter) of input data. Then a recipe is prepared to find the data and run the preprocessors. When ESMValTool runs a recipe, it produces preprocessed data that can be passed as input to a hydrological model. It will also store provenance and citation information to ensure transparency and reproducibility. This leads to less time spent on building custom preprocessing, more reproducible and comparable hydrological science.</p><p>In this presentation, we will give an overview of the current preprocessing pipeline of the eWaterCycle, outline ESMValTool preprocessing functions, and introduce available hydrological recipes and diagnostic scripts for the PCRGLOB, WFLOW, HYPE, MARRMOT and LISFLOOD models.</p>