Search

Parameter regionalization of hydrological models is one of the most commonly used methods for hydrological prediction over ungauged catchments. Although there were many regional studies, there is no clear conclusion on the best-performing regionalization method for global hydrological modelling. The objective of this study is to determine an appropriate global-scale regionalization scheme (GSRS) for global hydrological modelling. To this end, the performance of five regionalization methods with two different average options, two weighting approaches, and seven efficiency thresholds (i.e. Kling-Gupta efficiency (KGE) values to measure hydrological model performances) was compared over thousands of catchments based on four conceptual hydrological models. Results of nine global models from the Global Earth Observation for Integrated Water Resource Assessment (EartH2Observe) project were selected to validate the accuracy of GSRS in estimating global runoff. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting method and the output average option offers the best regionalization result when using the KGE ≥ 0.5 as an efficiency threshold for all four hydrological models, (2) the regionalization-based global hydrological simulation schemes (RGHSs), i.e. the proposed GSRS combining with four hydrological models, consistently performs better than the nine global models from EartH2Observe project in the estimation of runoff for most catchments, with varying degrees of improvement in the median, upper and lower quartiles, and whiskers of each performance metric, and (3) the global long-term annual water resources estimated by RGHSs range between 42,592 and 46,810 km3/yr. HIGHLIGHTS The performance of regionalization methods is evaluated with multiple hydrological models globally.; A global-scale regionalization scheme (GSRS) is proposed for global hydrological modelling.; The robust regionalization-based global hydrological simulation schemes (RGHSs), i.e. the proposed GSRS combining with four hydrological models, can produce reliable simulations of global water resources.;

Abstract Annotating cells in the analysis of single-cell RNA-seq (scRNA-seq) data is one of the most challenging tasks that researchers are actively addressing. Manual cell annotation is generally considered the gold standard method, although it is labor intensive and independent of prior knowledge. At present, the relationship between high-quality, known marker genes and cell types is very limited, especially for a variety of species other than humans and mice. The singleCellBase is a manually curated resource of high-quality cell types and gene markers associations across multiple species. In details, it offers 9,158 entries spanning a total of 1,221 cell types and linking with 8,740 genes (cell markers), covering 464 diseases/status, and 165 types of tissues across 31 species. The singleCellBase provides a user-friendly interface to the scientific community to browse, search, download and submit records of marker genes and cell types. The resource providing ineluctable prior knowledge required by manual cell annotation, which is valuable to interpret scRNA-seq data and elucidate what cell type or cell state that a cell population represents.