State-of-the-art hydrological datasets exhibit low water balance consistency globally
Abstract. The proliferation and diversification of hydrological datasets have significantly advanced hydrological research. However, the coherence across these datasets remains poorly understood, hindering the comparability of findings derived from different data sources and variables. Here, we demonstrate that state-of-the-art hydrological datasets exhibit overall low consistency when evaluated through the lens of water balance – specifically, the relationship between variations in soil moisture and the difference between precipitation, evapotranspiration, and runoff. Our analysis reveals that satellite-based precipitation datasets generally show the highest consistency, while gauge-based datasets perform better in densely monitored regions of the Northern Hemisphere. For evapotranspiration, runoff, and soil moisture, reanalysis datasets demonstrate broader areas of higher consistency compared to gauge- or satellite-based products. Spatial patterns of consistency are strongly influenced by aridity and temperature, which affect measurement and modelling accuracy, while vegetation cover further modulates the performance of soil moisture datasets. Notably, dataset consistency has improved significantly in northern mid-latitudes over recent decades, likely reflecting advancements in observational technologies and the effects of climate warming. These findings underscore the importance of continued efforts to enhance dataset coherence and reliability for robust hydrological assessments.
Manuscript: ESSD-2025-376
Huang et al. contribute to understanding the limitations of hydrological datasets (ground-, satellite-, and reanalysis-based) in capturing the relationship between monthly variations in soil moisture (SM) and the difference between precipitation (P), evapotranspiration (ET), and runoff (R) at a pixel scale around the world. Additionally, the manuscript’s results contribute to identifying the most suitable datasets for different geographical and ecological regions, which is important for reducing uncertainty in ecological, climatological, and hydrological studies using the evaluated datasets.
Overall, I found the paper well written and organized, and suitable for publication in the ESSD journal, but I have some comments that should be addressed before publication consideration. Particularly, some work is required to improve the clarity of the methods and results sections: (i) Explain how lateral flows and water table depth may potentially bias the proposed water balance at pixel scale, leading to the low water balance consistency reported in the manuscript; (ii) provide a clearer explanation of the linear relationship between SM, P, ET, and R (P – ET - R)s = k ΔSMs) at the monthly scale, including the potential limitations of assuming a linear relationship.
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