the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A long-term dataset of debris-flow and hydrometeorological observations from 1961 to 2024 at Jiangjia Ravine, China
Abstract. The study of mechanisms of debris-flow formation and movement is constrained by the lack of comprehensive and long-term field monitoring data. In 1961, the Dongchuan Debris Flow Observation and Research Station (DDFORS) was established in the highly active debris-flow catchment of Jiangjia Ravine to conduct continuous field observations of debris flows. With the advancement of technology, more high-precision instruments have been employed to monitor the entire process of debris flows. This paper presents a unique and comprehensive dataset of debris flow and hydrometeorological observations collected over a 64-year period (1961–2024) at Jiangjia Ravine, China. The dataset documents 17,001 surges for a total of 278 debris-flow events, encompasses detailed measurements of kinematic parameters of debris flow, including velocity, depth, and discharge, as well as physical-mechanical parameters such as particle size distribution of debris flow, yield stress, and viscosity of debris-flow slurry. It also incorporates the induced seismic data, providing insights into the dynamic characteristics of debris flows. Furthermore, it includes continuous records of rainfall at minute intervals, soil moisture, and suspended sediment concentrations at the catchment scale. This extensive dataset provides invaluable insights into the initiation, transportation, and deposition processes of debris flows. It can be utilized to analyze flow resistance and dynamic characteristics of debris flows, to validate various computational models, to investigate the effects of debris flows on channel morphology, and evaluate the impact of climate change on sediment transport within watersheds. The dataset is publicly accessible through the National Cryosphere Desert Data Center (NCDC) (https://www.ncdc.ac.cn/) and is organized into several categories to facilitate ease of use and analysis.
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Status: open (until 22 Jun 2025)
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RC1: 'Comment on essd-2025-190', Anonymous Referee #1, 16 May 2025
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This is a rich and unique dataset, which can potentially very useful for future research. I am not aware of anything similar in landslide/debris-flow research. Therefore, this could be a seminal contribution in terms of open data research. The authors did a great job in collecting and synthesizing the different datasets and translating descriptions and headers to English. All links to the dataset work and the data can be downloaded. Before publication, I think some figures and example data could be presented differently. Furthermore, some more details on assumptions and calculations could be added (see comments below). In my opinion, it can be published in ESSD if these points are addressed.
L42-L57: this is a nice list of monitoring installations, but would be much more accessible if you could put them in a map or table
L44: I don’t think Erlenbach produces debris flows, but bed load transport. Please double check
Figure 3: this is a bit small. Maybe you can flip it by 90° and fill a page?
Figure 4: I would add the channel bed to panel c. Is flow depth H+h or only H?
L220: please add that you assume a rectangular channel cross section
L224: why T/2?
L225: please define how you differentiate surge and continuative flow
L230: what is rs?
L234: I don’t get the logic with the subscripts c and s. Why is sediment volume Ws, but sediment transport rate Qc and not Qs?
Figure 6: consider using other colors because in the previous figures you use these to differentiate surge types
Figure 7: these are huge inter-annual variabilities and the catchment seems inactive now. Is this correct? Can this variability be explained with interruptions of systematic monitoring?
L384: why is rainfall and meteorological data separated but still rainfall is mentioned here again?
Figure 12d/13/15: I understand that you want to show what you’re data looks like and this makes sense for data of debris flow events, but I don’t think this is very informative for long-term meteo data. I would consider bar plots (like 12a) showing monthly mean and error bars with e.g. min/max values from you’rr observation period
Dataset links:
- Data repositories: on the webpage in the box to the right « how to get the data » it says « download via FTP » instead of « http »
- Soil moisture of runoff plot at Jiangjia Ravinein 1966.xlsx: should relative water content be in unit g?
- Rainfall data: this seems to be in several data sets (meteorological, rainfall, kinematic). Maybe you could explain the differences in the text where you mention Table 3?
Citation: https://doi.org/10.5194/essd-2025-190-RC1
Data sets
Debris-flow kinematic data at Jiangjia Ravine, Dongchuan, Yunnan, China, from 1961 to 2024 Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6803.2025
Seismic data of debris flow at Jiangjia Ravine, Yunnan, China, from 2023 to 2024 Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6804.2025
Particle size distribution of debris flows at Jiangjia Ravine, Dongchuan, Yunnan, China, in 1965, 1966, 1974, 1975, 1982, and from 2003 to 2024 Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6721.2025
Rheological data of debris-flow slurry at Jiangjia Ravine, Yunnan, China, from 2003 to 2024 Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6720.2025
Debris flow video at Jiangjia Ravine in 2023 and 2024, Yunnan, China Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6807.2025
Cross-sectional measurement data at Jiangjia Ravine, Yunnan, China, from 1999 to 2024 Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6719.2025
Meteorological data at Jiangjia Ravine and Xiaojiang River Catchment, Yunnan, China Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6805.2025
Rainfall data at Jiangjiag Ravine and Xiaojiang River Catchment, Yunnan, China Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6716.2025
Soil moisture and temperature data at Jiangjia Ravine, Yunnan, China, from 2017 to 2024 Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6718.2025
Sediment concentration and grain size distribution data at Jiangjiag Ravine and Xiaojiang River Catchment, Yunnan, China Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6802.2025
Observation data at runoff plots at Jiangjia Ravine, Yunnan, China Dongri Song et al. http://dx.doi.org/10.12072/ncdc.ddfors.db6806.2025
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