| 28 Jul 2025
From grid to ground: How well do gridded products represent soil moisture dynamics in natural ecosystems during precipitation events?
Abstract. Soil moisture (SM) is a critical variable governing land–atmosphere interactions and influencing ecohydrological and climatic processes. Despite substantial progress in estimating SM through remote sensing and land surface models, considerable uncertainties still remain, especially in near-natural and poorly monitored ecosystems interacting with deeper soil layers. In this study, the performance of four state-of-the-art gridded SM products (SPL4SMAU, GLDAS-Noah, ERA5 and ERA5-Land) is evaluated against in situ observations at ten natural monitoring sites in central and southern Chile, covering different hydroclimatic conditions (five semi-arid and five humid sites). The evaluation is performed at a 3-hourly temporal resolution, using well-known statistical metrics of performance, including unbiased root mean square error (ubRMSE), modified Kling–Gupta efficiency (KGE′), deseasonalised Spearman’s rank correlation coefficient (ρ), and percent bias (PBIAS), each applied separately for surface soil moisture (SSM) and root zone soil moisture (RZSM). Finally, the dynamic SM responses to precipitation events is evaluated using rising time (RT) and amplitude (A) SM signatures during the first and the most intense precipitation events of the year.
Our results show that ERA5 and ERA5-Land consistently outperform SPL4SMAU and GLDAS-Noah on most metrics and in most regions, with ERA5-Land being particularly strong in humid areas. However, SPL4SMAU achieved the best SSM performance in selected northern arid locations, based on KGE′; while GLDAS-Noah performed the worst overall, with the exception of moderate correlation values in southern RZSM. During the first precipitation event of the year, all products systematically overestimated both rising times and amplitudes in the arid north, indicating challenges in capturing SM responses under dry antecedent conditions. In contrast, all the gridded products aligned more closely with in situ measurements during intense precipitation events, particularly in humid regions. Our findings suggest that both ERA5 and ERA5-Land are valuable datasets for monitoring SM variability in near-natural and data-scarce ecosystems, while highlighting the value of event-based SM signatures to complement traditional performance metrics. Finally, we recommend the use of the deseasonalised Spearman rank correlation to better detect inconsistencies in temporal dynamics, especially in regions with strong seasonal cycles, such as arid environments.
General assessment
The manuscript tackles an important and timely topic: evaluating state-of-the-art gridded soil moisture (SM) products in natural ecosystems of central and southern Chile. The study is original, methodologically robust, and provides valuable insights into the performance of SM datasets in under-monitored areas of the Southern Hemisphere. The combination of standard statistical performance metrics with event-based soil moisture signatures (rising time and amplitude) is a notable strength and introduces novelty. Overall, the manuscript is well-prepared and deserves publication after significant revisions. Below, I offer detailed comments that could help enhance the clarity, impact, and wider relevance of the research.
Strengths
Methodological robustness: Employing both statistical performance metrics (ubRMSE, KGE′, Spearman, PBIAS) and event-based analyses offers a thorough evaluation of SM dynamics.
Unique dataset: The Kimün-Ko network provides a valuable contribution, delivering new ground-based data from natural ecosystems in the Southern Hemisphere.
Balanced interpretation: Conclusions appropriately emphasise the strengths and weaknesses of each product (ERA5 and ERA5-Land excel in humid regions, SPL4SMAU performs better in arid areas, GLDAS-Noah is the weakest).
Scientific relevance: The findings are highly pertinent for hydrological modelling, ecohydrology, and climate research in regions with limited data.: The use of both statistical performance metrics (ubRMSE, KGE′, Spearman, PBIAS) and event-based analyses provides a comprehensive assessment of SM dynamics.
Major Suggestions
Conclusions (clarity and structure). The conclusions are dense and could be reorganized into a concise list of take-home messages. This would enhance readability and emphasise the main findings for a broader audience.
Implications and applications. Expand the discussion on the practical relevance of the results. For example: How can the findings support water management or drought monitoring? What are the implications for regional climate modeling in semi-arid Chile?
Limitations and future work: The relatively short observational period (2022–2023) limits the assessment of interannual variability. This should be explicitly acknowledged, with suggestions on how longer records (or complementary datasets) could improve robustness. The definition of RZSM (0–100 cm) is harmonized across products but may obscure differences in vertical soil processes. A brief sensitivity discussion would strengthen the analysis. Consider proposing specific future research avenues (e.g., integration with Sentinel-1 or other high-resolution sensors, extension of the monitoring network, cross-comparisons with ISMN data).
International connection: The study would gain broader relevance if results were briefly compared with findings from other arid/humid regions (e.g., Africa, Asia). This would highlight the global implications of the Chilean case study.
Verification of assumptions: You averaged all data to 3-hour resolution. Could this temporal aggregation mask short-term dynamics, particularly in ERA5/ERA5-Land, which have hourly outputs? Please justify. Moreover, precipitation drives soil moisture. A more explicit evaluation of precipitation inputs in ERA5/ERA5-Land (and their consistency with local rain gauges) would strengthen confidence in the results.
Minor Suggestions
Writing style: Sometimes the text is dense and filled with acronyms. Making the prose simpler and cutting down on jargon where possible would make it easier to understand, especially for readers who are less familiar with SM modelling.
Figures: Some figures are very detailed and hard to interpret. Think about adding schematic diagrams or visual summaries that compare key differences (e.g., “north arid vs south humid”) to make the main points clearer.
Terminology: Make sure the terms are used consistently (e.g., SSM vs “surface SM”) and check that all acronyms are explained when first introduced.
Formatting: While tables that summarise site details and datasets are useful, they could be made clearer by streamlining their layout. At times, the text is dense and acronym-heavy. Simplifying the prose and reducing jargon where possible would improve accessibility, especially for readers less familiar with SM modelling.