Comment on hess-2022-320

Abstract

The majority of ungauged regions around the world are in protected areas and rivers with non-perennial flow regimes, which are vital to water security and conservation. There is a limited amount of ground data available in such regions, making it difficult to obtain streamflow information. This study examines how in situ streamflow datasets in data- rich regions can be used to extrapolate streamflow information into regions with poor data availability. These data-rich regions include North America (987 catchments), South America (813 catchments), and Western Europe (457 catchments). South Africa and Central Asia are defined as data-poor regions. We obtained 81 catchments and 133 catchments for these two data-poor regions, respectively, and assumed they are pseudo ungauged regions for our analysis. We trained machine learning (ML) algorithms using climate and catchments attributes input variables in data-rich (i.e., source) regions and analyzed the possibility of using these pre-trained ML models to estimate climatological monthly streamflow over data-poor (i.e., target) regions. We found that including diverse climate and catchment attributes in training data sets can greatly improve ML algorithms' performance regardless of significant geographical distance between input datasets. The pre-trained ML models over North America and South America could be used effectively to estimate streamflow over data-poor regions. This study provides insight into the selection of input datasets and ML algorithms with different sets of hyperparameters for a geographic streamflow extrapolation.