the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A dataset of lake-catchment characteristics for the Tibetan Plateau
- 1Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou, 730000, China
- 2Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
- 3Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China
- 4State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- 5Department of Physical Geography and Ecosystem Science, Lund University, Lund, 22100, Sweden
- 6State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
- 1Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou, 730000, China
- 2Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
- 3Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China
- 4State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- 5Department of Physical Geography and Ecosystem Science, Lund University, Lund, 22100, Sweden
- 6State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
Abstract. The management and conservation of lakes should be conducted in the context of catchment because lakes collect water and materials from their upstream catchments. So the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP) with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with area from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream contributing area of each lake, and the inter-lake catchments which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e. lake body, topographic, climatic, land cover/use, soil & geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the daily time series of climatic variables are also extracted, which can be used to drive lumped hydrological models or machine learning models for hydrological simulation. The LCC-TP dataset contains fundamental information for analysing the impact of catchment-level characteristics on lake properties, which on the one hand can deepen our understanding on the drivers of lake environment change, and on the other hand, can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially-explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272026, Liu, 2022).
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Junzhi Liu et al.
Status: open (until 24 Jun 2022)
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CC1: 'Comment on essd-2022-124', abc Peter, 24 Apr 2022
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Obviously, this data set is a CAMELS-type dataset on catchment hydrology for the Tibetan Plateau. However, without runoff data, people could easily follow the freely accessible codes of (1) Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. and (2) most recent watershed delineation code (WRR paper: Rapid Watershed Delineation Using an Automatic Outlet Relocation Algorithm, https://doi.org/10.1029/2021WR031129) to reproduce this work.
With runoff/ lake inflow, (streamflow) data, which is included in all CAMEL datasets of different countries, such as published papers essd-12-2075 and essd-12-2459). I could not see any novelty of this dataset, thus the paper is obviously not recommended for the publication of ESSD.
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AC1: 'Reply on CC1', Junzhi Liu, 24 Apr 2022
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Thanks for the comments.
Firstly, we do not think this dataset can be reproduced with freely accessible codes due to the following reasons: This dataset is lake oriented, while the CAMELS dataset and the watershed delineation code you mentioned are river oriented. There are many terminal lakes and nested catchments on the Tibetan Plateau (shown in Fig. 3 of our manuscript), which makes existing code not applicable. Therefore, in this study, we developed an algorithm that can delineate the upstrem catchment of lakes and meanwhile can construct the upstream and downstream relationships among lakes/lake-catchments.
Secondly, lakes are widely spread on the Tibetan Plateau (TP) with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics on the TP, which makes our study novel. This dataset provides exciting opportunities for lake studies in a spatially-explicit context and promotes the development of landscape limnology on the TP.
Thirdly, as the comments mentioned, there have been several related papers published in ESSD (e.g. essd-12-2075 and essd-12-2459) in recent two years. I also notice that these papers have many citations. These facts highlighted that 1) the topic of this manuscript is highly relavant to the readers of ESSD, 2) the editors and reviewers of these published papers acknowledge the importance of such contributions, and 3) the related datasets are urgently needed by the community as can be seen from the high citations. Therefore, as the first dataset of lake-catchment characteristics on the TP, our dataset will have important contribution to the hydrology and limnology community.
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CC2: 'Reply on AC1', abc Peter, 25 Apr 2022
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Thank you for the authors’ clarifications. I appreciate your efforts to develop a lake-scale dataset, but still have concerns about the novelty.
Firstly, I agree that collecting all valuable variables is useful for hydrological applications, such as CAMLES-type datasets (e.g., CMALES-BR, CAMLES-AUS, CAMLES-GB, LamaH-CE, and CCAM), which have been published ESSD. Please note that all these datasets are prepared with catchment attributes including streamflow! The reason why these publications are probably highly cited can be attributed to their streamflow data for hydrological applications. See the google scholar citations. The basic framework of these datasets is simple, i.e., deriving the catchment shapefiles and extract catchment attributes (soil, land cover, climate, topography, and geology) from various datasets, that’s why I attribute your dataset to a CAMLES-type dataset. It is quite strange that your paper does not cite any CAMLES-type papers, which your paper follows the same structure of.
From the authors’ responses , we could see the point that authors constructed the first dataset of lake-catchment characteristics on the TP, and claimed to develop an algorithm that can delineate the upstream catchment of lakes, which make their study novel. However, it is not clear where the uniqueness and novelty of your database are, the new methodology of catchment delineation from a lake-oriented approach? or the first kind of dataset on the TP? If the former one, we would like to see a new methodology; unfortunately, we cannot find it in the manuscript. People could not see any literature review of catchment delineation, comparison of these approaches or yours. It seems like a collection of catchment-based variables using already published algorithms and procedures, without any novel and unique data like streamflow or any new advanced approach of catchment lineation. If the latter one, the first doesn’t work all the time.
Let’s see the introduction and main content of this paper, authors exaggerated that such tasks are time-consuming and needs to be automatically processed. I don't think so and believe that authors fail to acknowledge many published papers to address these issues. People could upload the shapefiles and extract catchment-based attributes in a few days, thanks to the GEE. Catchment shapefiles can be automatically processed or obtained from the freely accessible codes or existing databases.
See the refences below:
Gorelick, Noel, et al. "Google Earth Engine: Planetary-scale geospatial analysis for everyone." Remote sensing of Environment 202 (2017): 18-27.
Liu K., et al. “Automatic watershed delineation in the Tibetan endorheic basin: A lake- oriented approach based on digital elevation models”, Geomorphology, 358, 107127, 370 https://doi.org/10.1016/j.geomorph.2020.107127, 2020.
Xie J, et al. Rapid watershed delineation using an automatic outlet relocation algorithm[J]. Water Resources Research, 2022: e2021WR031129.
Lehner B, Roth A, Huber M, et al. HydroSHEDS v2. 0-Refined global river network and catchment delineations from TanDEM-X elevation data[C]. EGU General Assembly Conference Abstracts. 2021: EGU21-9277.
Interestingly, two similar studies (mentioned in the introduction) were published in “Gigascience” and “Freshwater Science”, which could be a choice for this paper.
For above reasons, I do not support its publication in the ESSD. Anyway, I still look forward to seeing the reviewers’ comments and editor’s decision.
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AC2: 'Reply on CC2', Junzhi Liu, 27 Apr 2022
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Thank you very much for your time and further comments, which will help us improve the manuscript substantially.
First, we totally agree that streamflow is important for river-oriented CAMLES-type datasets. Your comments reminded us that for lake-oriented datasets, the data of lake area, water level and volume should be added during our revision, which will make the dataset more comprehensive. These lake-specific data will also make our dataset different from other CAMLES-type datasets.
We acknowledge the CAMLES-type datasets and papers are very important, and the related citations will be added during revision.
Secondly, you attributed our dataset to a CAMLES-type dataset, which means the amount of our work should be similar to other CAMLES-type datasets, especially considering that we did not use existing code to generate the dataset as there is no existing open-source code for the delineation of nested lake-catchments (including the delineation of full lake catchments and inter-lake catchments, the construction of topological relationship among lakes/lake-catchments, and the tracing of flow path among upstream and stream lakes; as shown in Fig.3 of the manuscript). Instead, we developed a software using the C and Python programming language to implement the above-mentioned functions, and the source code are open (https://github.com/LoserOne-ovo/basin_delineation).
We guess that you must be an expert on geospatial techniques, so you think the related work was relatively simple. Actually, for the researchers who are not experts on geospatial techniques but need catchment-level attributes, it is a tough task to construct such datasets. The CAMLES-type datasets you mentioned and our dataset are of great value, especially for such researchers. Therefore, in our opinion, the contributions of this study and the published CAMLES-type datasets are mainly from the perspective of novel datasets rather than novel methodology.
Moreover, we want to reemphasize the importance of our study. We think the criterion for whether a work should be published is not the how easy or hard it is, but the significance of the work to the community. We appreciate that you recognize we have constructed the first dataset of lake-catchment characteristics on the Tibetan Plateau. This dataset fills an important dataset gap and provides fundamental information for at least two types of studies on the Tibetan Plateau: 1) Spatial prediction of lake properties. As we all know, in situ measurements of lake properties (e.g., sedimentation rate and organic carbon content) are limited on the Tibetan Plateau due to harsh working conditions and logistic difficulties. This dataset can be used as environmental factors to predict these properties in unsampled lakes. This is important for research at the regional scale. 2) Hydrological modelling of lake catchments based on lumped hydrological models or machine learning methods. This type of studies is similar to the main aim of CAMLES-type datasets, but the target variable is not streamflow but lake area, water level or volume. Therefore, we think our dataset did make a substantial contribution to the hydrology, limnology, and cryosphere community.
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AC2: 'Reply on CC2', Junzhi Liu, 27 Apr 2022
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CC2: 'Reply on AC1', abc Peter, 25 Apr 2022
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AC1: 'Reply on CC1', Junzhi Liu, 24 Apr 2022
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RC1: 'Comment on essd-2022-124', Anonymous Referee #1, 15 May 2022
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This dataset of Tibetan Plateau lake-catchment characteristics fills a data gap for conducting a variety of potential scientific studies on the lakes in the region, which itself is of great importance to understanding the earth systems. Development of the dataset is well justified and well designed. The dataset is comprehensive and contains many aspects of information on a large number of lakes over a long period of time (including time series data). Besides, potential users of the dataset would appreciate the distinction between full-lake-catchment and inter-lake-catchment statistics.
The only suggestion from the reviewer is to add a section to briefly discuss or highlight the uncertainties of such a dataset. For example, given that most of the environmental statistics were obtained from existing datasets (DEM, Land cover, climatic data, etc.), highlighting sources/magnititude of uncertainties of such datasets, and discussing how the uncertainties propagate to the developed TP lake-catchment dataset would be beneficial to future users of this dataset.
Overall, the reviewer thinks the dataset is scientifically sound (adding discussion on uncertainty is a plus) and presented in a very clear manner. This dataset could be of great interest to the earth scientfic reserach communities.
Junzhi Liu et al.
Data sets
A dataset of lake-catchment characteristics for the Tibetan Plateau (v1.0) (1979-2018) Junzhi Liu https://doi.org/10.11888/Terre.tpdc.272026
Junzhi Liu et al.
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