High-resolution datasets for lake level changes in the Qinghai-Tibetan Plateau from 2002 to 2021 using multi-altimeter data

Chen, Jiaming; Liao, Jingjuan; Lou, Yanhan; Ma, Shanmu; Shen, Guozhuang; Zhang, Lianchong

doi:https://doi.org/10.5194/essd-2022-313

Preprints

https://doi.org/10.5194/essd-2022-313

Preprints

18 Oct 2022

| 18 Oct 2022

Status: this preprint is currently under review for the journal ESSD.

High-resolution datasets for lake level changes in the Qinghai-Tibetan Plateau from 2002 to 2021 using multi-altimeter data

Jiaming Chen, Jingjuan Liao, Yanhan Lou, Shanmu Ma, Guozhuang Shen, and Lianchong Zhang

Abstract. The Qinghai Tibet Plateau (QTP), known as the Roof of the World and the Water Tower of Asia, has the largest number of lakes in the world, and because of its high altitude and near absence of disturbances by human activity, the plateau has long been an important site for studying global climate change. Hydrological stations cannot be readily set up in this region, and in situ gauge data are not always publicly accessible. Satellite radar altimetry has become a very important alternative to in situ observations as a source of data. Estimation of the water levels of lakes via radar altimetry is often limited by temporal and spatial coverage, and, therefore, multi-altimeter data are often used to monitor lake levels. Restricted by the accuracy of waveform processing and the interval period between different altimetry missions, the accuracy and the sampling frequency of the water level series are typically low. By processing and merging data from eight different altimetry missions, the developed datasets provided the water level changes for 362 lakes (larger than 10 km²) in the QTP from 2002 to 2021. The period for the lake level change series, which affords high accuracy, can be much longer for many lake systems. The present datasets and associated approaches are valuable for calculating the changes in lake storage, trend analyses of the lake levels, short-term monitoring of the overflow of lakes, flooding disasters on the plateau, and the relationships between changes in the lake ecosystems and changes in the water resources.

Received: 06 Sep 2022 – Discussion started: 18 Oct 2022

Jiaming Chen et al.

Status: open (extended)

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RC1: 'Comment on essd-2022-313', Anonymous Referee #1, 17 Nov 2022 reply

This study provides the time series of water level for lakes in the Qinghai-Tibetan Plateau between 2002 and 2021 using altimeter data from Envisat, ICESat-1, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, and Sentinel-3A. The water level data in 2002-2021 provided by this study is not new, and have been reported by couple of previous studies. This study did not present well such as time series of lake level and not story focused such as the mention the discharge without close relation with study study. Moreover, the authors did not know the background information of lakes over the Tibetan Plateau well. I can not recommend the publication of this manuscript (dataset).

1) Although this study provides the time series of water level of lakes in the Qinghai-Tibetan Plateau from eight altimetry products, this dataset is not new compared with published studies, especially in a limited period (2002-2021). Hydroweb and other websites have provided open access lake level data since 1992, which has covered the altimetry data used in this study.

2) The presentation of this study is not good such as Figures 3 and 4. Flowchart 1 and 5 should be combined together. For a scientific paper, the figures should be drawn by a scientific standard. Moreover, the offset among the different altimetry data was addressed? How?

3) What is the difference of boundaries between Qinghai-Tibetan Plateau and Tibetan Plateau? How the comparison of time series of altimetry data and in-situ? Why the streamflow and discharge data are used, but the analysis of water level and advantage of your study are not clear?

Citation: https://doi.org/10.5194/essd-2022-313-RC1

AC1: 'Reply on RC1', Jingjuan Liao, 27 Nov 2022 reply

The authors would like to express thanks to the anonymous reviewer for his voluntary work and the constructive comments to improve this manuscript. All of the comments are of great benefit to us. During the past few days, we did much work to revise the manuscript according to the reviewer’s comments. All of the comments have been addressed as following:

RC1: 'Comment on essd-2022-313', Anonymous Referee #1, 17 Nov 2022

Q: This study provides the time series of water level for lakes in the Qinghai-Tibetan Plateau between 2002 and 2021 using altimeter data from Envisat, ICESat-1, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, and Sentinel-3A. The water level data in 2002-2021 provided by this study is not new, and have been reported by couple of previous studies. This study did not present well such as time series of lake level and not story focused such as the mention the discharge without close relation with study study. Moreover, the authors did not know the background information of lakes over the Tibetan Plateau well. I can not recommend the publication of this manuscript (dataset).

Reply: Although the water level data provided by this study is not new, we provide the largest number of monitored lakes among the available studies. In the revised version, we added the analysis of the variation of lake level in different basin, while the discharge mentioned in the manuscript is mainly to illustrate the application of lake level data, which refers to the fact that we can explore the regulation of the rivers associated with the lake using lake level data.

Q: 1) Although this study provides the time series of water level of lakes in the Qinghai-Tibetan Plateau from eight altimetry products, this dataset is not new compared with published studies, especially in a limited period (2002-2021). Hydroweb and other websites have provided open access lake level data since 1992, which has covered the altimetry data used in this study.

Reply: These eight altimetry products are already used in published studies, but the number of lakes monitored based on them is very limited, as can be seen in the following table.

Paper	No. of Lakes	Period	Data Source	Dataset Public or not
Jiang et al. (2017)	70	2003-2015	IceSat-1, Cryosat-2	N
Zhang et al. (2017)	68	1989-2015	IceSat-1, Landsat	N
Li et al. (2017)	167	2002-2012	IceSat-1, Envisat	N
Hwang et al. (2019)	59	2003-2016	Jason-2/3, SARAL, IceSat-1, Cryosat-2	N
Li et al. (2019)	52	2000-2017	Jason-1/2/3, Envisat, Cryosat-2, IceSat-1	Y
Zhang et al. (2019)	62	2003-2018	IceSat-1/2	Y
Hydroweb (Cretaux et al. 2011)	36	1993-2022	ERS-2, Envisat, T/P, IceSat-1, SARAL, Jason-1/2/3, Cryosat-2, Sentinel-3A	Y
DAHITI (Schwatke et al. 2015)	62	2003-2022	ERS-2, Envisat, SARAL, Sentinel-3A, Cryosat-2, IceSat-1, Jason-2/3,	Y
This Study	362	2002-2021	Envisat, SARAL, IceSat-1, Cryosat-2, Jason-1/2/3, Sentinel-3A	Y

As we know, a large number of lakes exist on the Tibetan Plateau, and monitoring the water levels of these lakes is very important for understanding the water cycle on the this plateau. Our study monitored the largest number of lakes with an area greater than 10 km² compared to previous studies. We believe that considering more lakes will be useful to find more details, driving mechanisms, and patterns of changes in the Tibetan Plateau. So this is also the objective of this manuscript.

Q: 2) The presentation of this study is not good such as Figures 3 and 4. Flowchart 1 and 5 should be combined together. For a scientific paper, the figures should be drawn by a scientific standard. Moreover, the offset among the different altimetry data was addressed? How?

Reply: Thank for your comments. We have combined the Figure 3 and 4, and drawn the figure again, please see the revised manuscript. Since Flowchart 1 and 5 represent different processes for altimetry data, Flowchart 1 is mainly the waveform retracking processing of altimetry data, while process 5 is mainly the fusion processing of water level data extracted from multi-source altimeters, which are different and not suitable for merging, so the two flowcharts are retained.

In this manuscript, the main merged method removes the offset between different altimetry data by subtracting the mean discrepancy obtained during the overlap period.

Thus, we will pick the dynamic reference time series to make the mergerd time series as long as possible for each time in case there are no overlap period.

On the other hand, it also exists some lakes with no overlap period when merging ICEsat-1 and Cryosat-2. In this case, we will consider using a combined linear-periodic-residual model (Liao et al., 2014) to simulate and forecast lake-level time series in the no-overlap period, and then make it possible to obtain the offset between ICEsat-1 and Cryosat-2. These details are presented in section 3.2.

Q: 3) What is the difference of boundaries between Qinghai-Tibetan Plateau and Tibetan Plateau? How the comparison of time series of altimetry data and in-situ? Why the streamflow and discharge data are used, but the analysis of water level and advantage of your study are not clear?

Reply: There is no difference of boundaries between Qinghai-Tibetan Plateau and Tibetan Plateau. In China, people used to call Qinghai-Tibetan Plateau (QTP), but internationally, it is used to call Tibetan Plateau (TP). To avoid misunderstanding, we have revised as the Tibetan Plateau in this manuscript.

Due to the unknown datum of in situ data, here we consider comparing the water level anomaly between in situ data and lake level in this study by removing the mean value over the validation period. In the revised vision, in order to show the validate results of lake level in this study, we added a figure comparing the in situ data with the lake level in this study.

The streamflow and discharge data are used to show that lake levels can be used to explore the regulation of the rivers associated with the lake, and this is just one case of the application of lake levels. In addition, we added the analysis of water level changes in lakes in different basin, so it further indicates that our study is much clear in reflecting the spatial and temporal variability of lakes on the Tibetan Plateau.

Citation: https://doi.org/10.5194/essd-2022-313-AC1

CC1:
'Comment on essd-2022-313', Nancy Swift, 08 Dec 2022 reply

I totally agree with the first Anonymous Referee. Below I would like to introduce some interesting works:
1) Liu, J., Fang, P., Que, Y., Zhu, L.-J., Duan, Z., Tang, G., Liu, P., Ji, M., and Liu, Y.: A dataset of lake-catchment characteristics for the Tibetan Plateau, Earth Syst. Sci. Data, 14, 3791–3805, https://doi.org/10.5194/essd-14-3791-2022, 2022.
This study also provides water level time series of lakes from Li et al. (2019), Zhang et al. (2021), and Xu et al. (2022).
2) Tourian, M. J., Elmi, O., Shafaghi, Y., Behnia, S., Saemian, P., Schlesinger, R., and Sneeuw, N.: HydroSat: geometric quantities of the global water cycle from geodetic satellites, Earth Syst. Sci. Data, 14, 2463–2486, https://doi.org/10.5194/essd-14-2463-2022, 2022.

This study provides water level, area and storage variation time series of global lakes/reservoirs/rivers from multisource datasets.

3) Hou, J., Van Dijk, A. I. J. M., Renzullo, L. J., and Larraondo, P. R.: GloLakes: a database of global lake water storage dynamics from 1984 to present derived using laser and radar altimetry and optical remote sensing, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-266, in review, 2022.

This study provides global lake water storage dynamics using water area and water level from multiple altimeters. However, there are critical comments from referees.

Regarding the increase in the spatial coverage of the lakes you claimed, I doubt its usefulness as there is very little data available for these small lakes. This is the main reason Hydroweb/other databases (also using multisource satellite altimeters) do not provide time series of water levels for these lakes. This issue may be addressed from future SWOT mission.

Overall, your datasets are neither new nor exhaustive and complete (such as Tourian et al. 2022 and Liu et al. 2022)! I could not see any novelty in your work (new method such as Li et al. 2019 addressed the inter-satellite biases) and a comprehensive validation. For above reasons, I do not support its publication in ESSD.

Reply

Citation: https://doi.org/10.5194/essd-2022-313-CC1
- AC2:
  'Reply on CC1', Jingjuan Liao, 12 Jan 2023 reply
  
  Thanks for the comments. Actually, the number of lakes monitored based on them is very limited, as can be seen in the table 1 in revised version. Our study monitored the largest number of lakes with an area greater than 10 km² in Tibetan Plateau (not Global) compared to previous studies. We believe that considering more lakes will be useful to find more details, driving mechanisms, and patterns of changes in the Tibetan Plateau. This is also the main objective of this manuscript.
  It is not necessary to say some one lake level time series is usefulness, even though some lakes are only available on Cryosat-2, because it could also give us a clear linear trend at recently 10 years. Once we monitored more lakes as much as possible, we could find some interesting and different spatial driving mechanisms, and patterns of changes in the Tibetan Plateau. Such as most lakes located in the southeastern Qiangtang Basin and near the Nyainqen Tanglha Mountain shows a decreasing trend which is different from patterns of changes of the whole Qiangtang basin. If we don’t try to monitor these lakes, we cannot find this pattern of changes. Moreover, in the southern Tibetan Plateau (in Brahmaputra Basin), we can find 8 lakes shown a decreasing trend, it is no more than 4 lakes were monitored in this area in previous area. Comparing with this, it is obvious that the dataset from this manuscript can be more suitable to explore the spatial driving mechanisms, and patterns of changes in the Tibetan Plateau. You can see details of the spatio-temporal analysis of changes in lake levels in the TP in revised version.
  Additionally, we use our in-home developed retracker to retrack all the altimeters. And we also develop a method (a combined linear-periodic-residual model) to merge the lake level time series for those lakes without overlap period between two different altimeters. We don’t mention too much details in this manuscript, because we have published related methodology papers (Chen et al., 2020; Liao et al., 2014), and this is a dataset description manuscript.
  
  Reply
  
  Citation: https://doi.org/10.5194/essd-2022-313-AC2
  - CC2: 'Reply on AC2', Nancy Swift, 14 Jan 2023 reply
    
    The authors contend that a high-resolution dataset might be an intended novelty. Of the 362 lakes I studied, 190 only had time-series from a single altimeter (CryoSat-2 and/or ICESat-2). Lake measurements are only available once per year on average, limiting their use to a wide range of applications. Furthermore, I noticed you provided operational Sentinel-3A and Jason-3 data as of 2019.08. Shouldn't this data be updated to 2021 (based on your title) or even 2023? Moreover, why not include other satellite missions like ICESat-2 and Sentinel-3B? Only eight lakes with in situ data were assessed for data-quality. You have not provided a detailed evaluation of your data product, so I am seriously concerned about its reliability.
    Despite mentioning this reference in my previous comment, the authors failed to include it, HydroSat (Tourian et al., 2022 ESSD). Hydrosat provides water level time series for global rivers, lakes, and reservoirs using Envisat, Saral, ICESat-2, CryoSat-2, Jason-1/-2/-3, and Sentinel-3A. Tourian presented a comprehensive overview of all methods for dealing with inter-satellite bias (Section 2.2.3). I do not see any novelty in your method for addressing inter-satellite biases. A new methodology has been proposed by Li et al. (2019), which allows them to publish their high-resolution datasets in ESSD. Despite three years passing, I am unable to discover any novelty in your work except for a high rate of similar follow-up. The application of the developed dataset demonstrated in the manuscript is not the novelty of this work and the overall framework is highly similar to Li et al. (2019).
    In terms of its general structure, the framework is very similar to Li et al. (2019) and does not provide any significant improvements over the existing works, in line with ESSD standards. As such, I would suggest a rejection.
    References:
    Tourian, M. J., Elmi, O., Shafaghi, Y., Behnia, S., Saemian, P., Schlesinger, R., and Sneeuw, N.: HydroSat: geometric quantities of the global water cycle from geodetic satellites, Earth Syst. Sci. Data, 14, 2463–2486, https://doi.org/10.5194/essd-14-2463-2022, 2022.
    Li, X., Long, D., Huang, Q., Han, P., Zhao, F., and Wada, Y.: High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions, Earth Syst. Sci. Data, 11, 1603–1627, https://doi.org/10.5194/essd-11-1603-2019, 2019.
    
    Reply
    
    Citation: https://doi.org/10.5194/essd-2022-313-CC2

RC2: 'Comment on essd-2022-313', Anonymous Referee #2, 26 Dec 2022 reply

Line 13: Here is for global climate change. But the text cannot discuss the relation between lake level change and global climate change.
Line 16: What about the detail altimetry satellite missions?
Line 19: Here the time spans 2002 to 2021. Are all lakes’ levels in this time span?
Section 1: There are many literatures about the lake level changes in QTP, which should be further summarized and generalized.
Line 72: Here there are 364 lakes. But there are 262 lakes in line 19.
Table 1: One altimetry satellite can only pass some lakes. Some lakes can be covered by two or three altimetry satellite mission. How to process these conditions to precisely determine one level series for one lake? How to get the lake level series of all lakes from 2002 to 2021?
Table 2: What unit is used for coordinate? 1985 should be the National Height Datum of China.
(1): How to get these corrections? How to improve these corrections?
Section 3.1.1: How to verify the effectiveness and accuracy of the retracking method?
Section 3.1.2: Here is noise footprints. How about the abnormal footprint?
Section 3.1.3: How to process the gross errors in time series?
Section 3.2: Coordinate system transfer and coordinate frame transfer all should be made.
(5): How to determine p in the Eq. ?
Section 4: How about resolution?
Table 3: RMS of Zhari Namco is 0.25m. But RMS is about 10.1cm in Sun et al. (Detecting lake level change from 1992 to 2019 of Zhari Namco in Tibet using altimetry data of TOPEX/Poseidon and Jason-1/2/3 missions. Frontiers in Earth Science, 2021, 9:640553, https://doi.org/10.3389/feart.2021.640553). Wang et al. (Robust, long-term lake level change from multiple satellite altimeters in Tibet: observing the rapid rise of Ngangzi Co over a new wetland. Remote Sensing, 11: 558, doi: 10.3390/rs11050558.) also shown the more precise lake levels.
Section 5: How about the physical mechanism? Why not the applications to climate change?

Citation: https://doi.org/10.5194/essd-2022-313-RC2

AC3: 'Reply on RC2', Jingjuan Liao, 12 Jan 2023 reply

Line 13: Here is for global climate change. But the text cannot discuss the relation between lake level change and global climate change.

Reply: Thanks for the suggestions, but here we are not discussing the relation between lake level change and global climate change. Just an introduction for the Tibetan Plateau and describe the importance of lakes in Tibetan Plateau.

Line 16: What about the detail altimetry satellite missions?

Reply: Thanks for the suggestions, the altimetry missions we used in this manuscript is added in the revised version.

Line 19: Here the time spans 2002 to 2021. Are all lakes’ levels in this time span?

Reply: Thanks for the suggestions, not all the lakes staring from 2002, among all of this, 168 lakes water level staring from 2010. The details also added into revised version.

Section 1: There are many literatures about the lake level changes in QTP, which should be further summarized and generalized.

Reply: Thanks for the suggestions, we added related literatures in past 5 years and summarized in the table 1.

Table 1 Comparison of this study with previous studies

Reference	No. of Lakes	Period	Data Source	Dataset Public or not
Jiang et al. (2017)	70	2003-2015	IceSat-1, Cryosat-2	N
Zhang et al. (2017)	68	1989-2015	IceSat-1, Landsat	N
Li et al. (2017)	167	2002-2012	IceSat-1, Envisat	N
Hwang et al. (2019)	59	2003-2016	Jason-2/3, SARAL, IceSat-1, Cryosat-2	N
Li et al. (2019)	52	2000-2017	Jason-1/2/3, Envisat, Cryosat-2, IceSat-1	Y
Zhang et al. (2019)	62	2003-2018	IceSat-1/2	Y
Hydroweb	36	1993-2022	ERS-2, Envisat, T/P, IceSat-1, SARAL, Jason-1/2/3, Cryosat-2, Sentinel-3A	Y
DAHITI	62	2003-2022	ERS-2, Envisat, SARAL, Sentinel-3A, Cryosat-2, IceSat-1, Jason-2/3,	Y
This Study	362	2002-2021	Envisat, SARAL, IceSat-1, Cryosat-2, Jason-1/2/3, Sentinel-3A	Y

Line 72: Here there are 364 lakes. But there are 262 lakes in line 19.

Reply: Thanks for the suggestions, we have revised it, the correct number is 362. We also scrutiny the whole text, and revised this mistake.

Table 1: One altimetry satellite can only pass some lakes. Some lakes can be covered by two or three altimetry satellite mission. How to process these conditions to precisely determine one level series for one lake? How to get the lake level series of all lakes from 2002 to 2021?

Reply: Thanks for the comments.

After constructing the time series for each altimetry over related lakes. We can follow the section 3.2 to fuse the multi-altimeter time series.

Specially, we first merged the two products with the longest period for the time series and chose the altimeter-derived water level with the longer time series as the baseline. Then systematic biases between another altimeter and the baseline will be removed by subtracting the mean discrepancy during the overlap period compared with the reference series (Lee et at., 2011; Kropáček et al., 2012) according to Eq. (4). Then, the same process was applied to the remaining products and the merged products connecting the three altimeters.

Additionally, some lakes cannot merged successfully (if just pass ICEsat-1 and Cryosat-2 before 2013), we will try to use a combined linear-periodic-residual model (Liao et al., 2014) to simulate and forecast the lake-level time series in the no-overlap period to merge the two altimeters with no overlap period.

We added some details to make it easier to follow in revised version.

Unfortunately, this manuscript only estimate lake level has valid observations from multi-altimetry, so not all the 364 lakes for time series from 2002 to 2021, 168 lakes for the time series from 2010 to 2021.

Table 2: What unit is used for coordinate? 1985 should be the National Height Datum of China.

Reply: Thanks for the suggestions, the unit for coordinate is degree. We revised the table notes for coordinates and 1985.

(1): How to get these corrections? How to improve these corrections?

Reply: Thanks for the comments. With the exception for retracking correction, all the corrections are included in the altimetry data product. We just need to improve the retracking corrections which is the most important to decide the accuracy of lake level. More detail can be seen in section 3.1.1.

Section 3.1.1: How to verify the effectiveness and accuracy of the retracking method?

Reply: Thanks for the suggestions. Actually, the retracking method we used here is the automatic multiscale-based peak detection retracker (AMPDR), having a special paper for it (Chen et al., 2020). We try to compare several lake level time series from altimetry with in-situ time series and existing product time series. The effectiveness and accuracy of this retracker has already discussed in detail in the paper above.

Section 3.1.2: Here is noise footprints. How about the abnormal footprint?

Reply: Thanks for the suggestions. The abnormal footprints are already detected in section 3.1.1, we call as unavailable off-nadir observation, which will be removed during retracking.

The lake level from invalid off-nadir observation usually shows a large bias with the DistanceThres. So, if the difference is larger than the half of the range window, it may not provide the signals from water surface and this observation will be regarded as an invalid off-nadir point (using Sentinel-3 as an example, it is 1/2∗128∗0.4684). (Chen et al., 2020)

Section 3.1.3: How to process the gross errors in time series?

Reply: Thanks for the suggestions. Here we used a state-space model (Nielsen et al., 2015), the gross errors can be obtained from their models. The obtained time series are not easily averaging, they considering continuous time steps and a hypothetical error model. Then according to the Laplace estimation, the mean value and error will be calculated from the model. More detail can be seen in Nielsen et al. (2015).

Section 3.2: Coordinate system transfer and coordinate frame transfer all should be made.

Reply: Thanks for the suggestions. Coordinate system transfer and coordinate frame transfer are all made in this manuscript. All the data will be transferred into WGS84/EGM 2008.

(5): How to determine p in the Eq. ?

Reply: Thanks for the suggestions. p stands for the number of periodic components. This can be determined by the reality. Such as considering 10 years periodic, 5 yeasr periodic, 2 years periodic, and 1 year periodic, then p equals to 4.

Section 4: How about resolution?

Reply: Thanks for the suggestions. Due to the complexity of fusion of multi-altimeter, it is hard to give a spatial resolution for the data set. The high resolution in title means high spatial coverage and large numbers of lakes.

Table 3: RMS of Zhari Namco is 0.25m. But RMS is about 10.1cm in Sun et al. (Detecting lake level change from 1992 to 2019 of Zhari Namco in Tibet using altimetry data of TOPEX/Poseidon and Jason-1/2/3 missions. Frontiers in Earth Science, 2021, 9:640553, https://doi.org/10.3389/feart.2021.640553). Wang et al. (Robust, long-term lake level change from multiple satellite altimeters in Tibet: observing the rapid rise of Ngangzi Co over a new wetland. Remote Sensing, 11: 558, doi: 10.3390/rs11050558.) also shown the more precise lake levels.

Reply: Thanks for the suggestions. For the RMSE of Zhari Namco, there is two possible reasons for this problem, one is that Zhari Namco is available on the eight altimeters in this manuscript, the data quality of Envisat is not stable, another is that fusion many different altimeter will also introduce noise. Actually, if we just consider Cryosat-2, Sentinel-3, Jason-2, and Jason-3, the RMSE is just 9.9 cm (as reported in Chen et al., 2020).

For the Ngangzi Co, no in situ gauge data are available in Ngangzi Co, so they are considering inter-compare with ICEsat and SARAL. In this manuscript, we just consider to compare with in-situ dada or existing product.

Section 5: How about the physical mechanism? Why not the applications to climate change?

Reply: There are many factors influencing lake level changes on the Tibetan Plateau, including Precipitation, and glacier and snow melt and degradation of permafrost brought by temperature changes, etc. This manuscript, as a dataset paper, mainly shows the changes of lake level and provides the basic data of lake level changes, so the physical mechanisms of lake level changes are not explored here, which will be further explored in future work.

Lake level changes on the TP are an indicator of climate change in the region, which has been addressed in many papers (such as Gao et al., 2013; Hwang et al., 2016; Hwang et al., 2019; Jiang et al., 2017), but here this manuscript does not expand on the application, but only points out the importance in the introduction.

Citation: https://doi.org/10.5194/essd-2022-313-AC3

RC3:
'Reply on AC3', Anonymous Referee #2, 14 Jan 2023 reply

Only one altimetry satellite passes some lakes. But there are many lakes covered by two or three altimetry satellites. How to combine several satellite altimeters's data to optimize lake level changes? How to imporve lake level data quality further?

Reply

Citation: https://doi.org/10.5194/essd-2022-313-RC3
- AC4: 'Reply on RC3', Jingjuan Liao, 18 Jan 2023 reply
  
  Q. Only one altimetry satellite passes some lakes. But there are many lakes covered by two or three altimetry satellites. How to combine several satellite altimeters's data to optimize lake level changes? How to imporve lake level data quality further?
  Reply: Thanks for the comments.
  For combining several satellite altimetry lake level time series, we need to remove the systematic biases first. The systematic biases between another altimeter and the baseline altimeter will be removed by subtracting the mean discrepancy during the overlap period compared with the reference series. For several altimeter data available on one lake in some day, we will try to consider the average value as the final water level.
  Additionally, some lakes cannot merge successfully (if just pass ICEsat-1 and Cryosat-2 before 2013), a combined linear-periodic-residual model (Liao et al., 2014) is used to simulate and forecast the lake-level time series in the no-overlap period to make it possible for merging the two altimeters with no overlap period.
  In order to improve lake level data quality further, we also intended to develop a new model to consider different altimeter data together in the future, regarding the systematic biases and error (assuming followed by some kind of errors model) as a fitting parameter. Using this way will be helpful for improve the data quality for multi-altimeter.
  
  Reply
  
  Citation: https://doi.org/10.5194/essd-2022-313-AC4

Jiaming Chen et al.

Data sets

High-space-coverage lake level change data sets on the Tibetan Plateau from 2002 to 2021 using multiple altimeter data Chen, J., Liao, J., Deng, W., Shen, G., Zhang, L., Wang, C. https://doi.org/10.1594/PANGAEA.939427

Jiaming Chen et al.

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The Qinghai Tibet Plateau (QTP) has the largest number of lakes in the world. Hydrological stations cannot be readily set up in this region due to high altitude and remoteness. Satellite radar altimetry has become a very important alternative to in situ observations as a source of data. By combining eight sets of altimeter data, the trends of the changes in the water levels for 362 lakes in the QTP during 2002–2021 were estimated, and the spatio-temporal changes in lake levels were explored.


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