the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Mar 2024
Interannual Variations of Terrestrial Water Storage in the East African Rift Region
Abstract. The US-German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE-Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over twenty years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes, whether naturally caused or anthropogenic, cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the East African Rift region region around lakes Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations together with surface water and meteorological observations and determine whether natural variability or human interventions caused these changes.
To this end, we apply the STL method (Seasonal Trend decomposition based on Loess) to separate the TWS signals into a seasonal signal, an interannual trend signal, and residuals. By clustering these interannual TWS dynamics for the African continent, we define the exact outline of the study's region.
In this area, a TWS decrease until 2006 was followed by a steady increase until around 2016, and Africa's most significant TWS increase occurred in 2019 and 2020. We found that besides precipitation and evaporation variability, surface water storage variations in the large lakes of the region explain large parts of the TWS variability. Storage dynamics of Lake Victoria regulated by the Nalubaale Dam alone contribute up to 50 % of the TWS changes. Satellite altimetry reveals the anthropogenically altered discharge downstream of the dam. It thus indicates that human intervention in the form of dam management at Lake Victoria substantially contributes to the TWS variability seen in the East African Rift region.
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RC1: 'Comment on egusphere-2024-641', Vagner Ferreira, 09 Apr 2024
The study “Interannual Variations of Terrestrial Water Storage in the East African Rift Region” addresses an interesting topic and provides some valuable insights. However, several issues need to be addressed before the manuscript can be considered for publication. I recommend major revisions based on the following main points and some other minor comments presented below.
Main points:
1. The authors state that “human intervention in the form of dam management at Lake Victoria substantially contributes to the TWS variability” (lines 15-16); however, they didn’t provide a clear estimation of the magnitude of this contribution. It would be interesting to see the relative contribution of natural variability and human interventions to the observed TWS fluctuations.
2. The study proposes a clustering approach to identify the East African Rift region as having similar interannual TWS dynamics. However, the justification for focusing on this specific region could be further improved by providing a stronger rationale for selecting this study area. The manuscript could highlight the East African Rift region's unique hydrological characteristics, ecological significance, or socio-economic importance.
3. Although the study compares TWS variations with precipitation, evapotranspiration, and surface water storage in the major lakes, the analysis of the underlying drivers remains somewhat superficial. The study could provide more information about the potential mechanisms that link these factors to TWS variability in the region (e.g., land use/land cover changes, soil moisture dynamics, groundwater recharge, and human water abstractions). A more comprehensive discussion of these drivers would beef up the interpretations and conclusions of the study.
4. The study cites some relevant literature; however, it could improve by discussing how the proposed study’s findings compare to or advance previous research on TWS variability in the East African Rift region. The paper would benefit from a more thorough synthesis of the existing knowledge and a clearer articulation of this study’s novel contributions.
5. The study lacks a thorough assessment of the uncertainties associated with the GRACE/GRACE-FO data, the precipitation and evapotranspiration datasets, and the surface water storage estimates. It would be interesting to see a more detailed description of the potential sources of error and their implications for the results. Also, the authors could elaborate more on the limitations, such as the coarse spatial resolution of GRACE data and the lack of ground-based validation data. These limitations could be explicitly acknowledged and discussed.
6. The current conclusion section is somewhat vague and does not fully address the broader implications of the findings for water resources management, ecosystem conservation, or climate change adaptation in the region (conditioned to the rationale for selecting the study area as per comment 2). The authors could elaborate on the potential applications of the study’s findings.
Minor comments:
7. Between lines 35-40, where it is “Niger Basin in West Africa,” it should be “Volta Basin in West Africa” in the context of the sentence.
8. Lines 134-137: The description of the water occurrence map processing is unclear. Please provide more details on how the 95% occurrence threshold was determined and how it affects the estimation of lake surface areas.
9. Lines 139-143: Please discuss the limitations of the surface water storage analysis based on a simplified relationship between lake level and area changes based on empirical cumulative distribution functions (ECDF). What could be the potential uncertainties it introduces in the storage estimates? For example, the monotonic and continuous relationship between lake level and area might not always be the case in reality. Lakes with complex bathymetry or irregular shorelines may exhibit non-monotonic or discontinuous relationships between level and area. However, the ECDF approach can handle outliers or anomalies in the input data more robustly than a linear regression used by Ferreira et al. (2018).
10. Lines 240-247: The discussion of the differences between the two SPEI datasets seems speculative. Please provide more evidence to support the claim that the divergence after 2008 is caused by differences in precipitation data rather than PET estimation methods.
11. Lines 290-295: The description of the Nalubaale Dam and its impact on Lake Victoria's water levels is incomplete. Please provide more information on the characteristics of the dam (e.g., operating rules) and downstream effects on the Victoria Nile and other water bodies. A study area section presenting the East African Rift Region would be useful.
12. Lines 314-315: Please provide a more rigorous assessment of the data quality and its impact on the correlation analysis.
13. Lines 367-368: That concluding statement seems too broad and not fully supported by the analysis. Please refine this conclusion and provide a more nuanced interpretation of the relative contributions of natural and anthropogenic factors to TWS variability.
14. Please revise the English since there are several issues (e.g., Line 5 shows “region region”, Line 92 shows “We analyses…”)Citation: https://doi.org/10.5194/egusphere-2024-641-RC1 -
AC1: 'Reply on RC1', Eva Boergens, 01 Jul 2024
Dear Vagner,
Thank you very much for your valuable comments. This first rebuttal letter will only address the more significant concerns and changes to the study. Please find them in the attached pdf.
Minor text changes, figures, or language-related comments will not all be answered individually here, but we will consider them in the revised version of the manuscript.
With kind regards,
Eva Boergens (on behalf of the authors)
- AC4: 'Reply on AC1', Eva Boergens, 09 Aug 2024
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AC1: 'Reply on RC1', Eva Boergens, 01 Jul 2024
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RC2: 'Comment on egusphere-2024-641', Susanna Werth, 15 May 2024
General Comments
The study presents an analysis of long-term variations in GRACE total water storage variations (TWS) over the last 22 years for Africa and compares the data to surface water storage (SWS) variations in major lakes derived from satellite altimetry in central Africa. The authors compare the datasets also with meteorological/drought data via time series analysis and statistical methods. They discuss the influence of human and climate on the variability in TWS and surface SWS in Central Africa. They also provide some novel insight into the TWS dataset through a cluster analysis for the continent Africa. The authors have conducted a good work. It provides detailed information on data and methods used and provides very interesting insight into the water storage variations in the study area around Lake Victoria in Africa. I do think, however, that a more structured organization of the manuscript; a quantification of uncertainty of the surface water storage estimates; and, following from that, a more comprehensive discussion and concise conclusion of the results would make the work clearer and more significant.
On organization of the work: In Section 3-7, the authors cover certain topics. For Section 3-5 they combine respective methods, results and discussions into one section. For Section 6, some of the relevant methods are explained in Section 2, then some more method description is added after results and discussion in this section. The authors often jump between results presentations along various figures and corresponding piece-wise discussion and conclusions. It makes it harder for the reader to discriminate objective facts from opinion or suggestions by the authors. Some of this becomes especially a problem in Section 5 and even more so Section 7, which are presented the least clear. A clearer structure should be introduced to the entire manuscript, for example, for example, either the methods, or the discussions should be split off in some way. Also, some of the figure organization need some improvements, for example some legends are incomplete. Introducing panel letters might help to address results in figures with more than two panels more clearly. Some figures might be better suited for a supplement. Further suggestions are given below.
On uncertainty of the results: estimates of TWS from GRACE as well as SWS from altimetry and subsequent modeling includes several sources of uncertainty, e.g. measurement errors, parameter uncertainty. These uncertainties should be discussed. But since the authors are quantifying percentage of explained signal variance, a quantification is also suggested, especially for SWS. The conclusions need to be put into perspective of the uncertainties (see also next section).
On the conclusions: The study's conclusion on the nature of the driver of TWS variations, i.e. whether it is either human or climate during certain temporal periods, is not fully supported by the results and analysis provided. First, this statement is mainly directly addressed in Section 7, where water levels of various lakes and river level are compared, and the impact of dam management is highlighted. There is no direct comparison with SWS and TWS variations provided. Second, a correlation of TWS to drought indicators is not an explanation or proof of climate dominance, as stated in the conclusion (L357-358), because human water use (e.g. of surface or groundwater) itself is typically also heavily influenced by drought conditions and might therefore similarly impact TWS. In addition, in the rest of the manuscript, the authors only analyze the SWS portion of TWS variation but no soil moisture or groundwater, hence, a large portion of TWS variation remains unexplained, and therefore a conclusion on human or climate dominance in TWS remains very speculative. I am also wondering if such a conclusion is even relevant to emphasize on the importance of the work, but rather may take away from the actual interesting quantitative and qualitative findings of the work on the importance of SWS in the region. This could be more highlighted by slightly altering the discussion of the findings.
In addition, the authors do not comprehensively quantify and discuss why TWS may be rising overall in the Central Africa/Lake Victoria region over the last two decades (they did so only for specific sections of the TWS time series or in relation to P and SWS). It was shown that precipitation plays an important role. However, the P increase (or change in ET) does not indicate if and where the water is stored. (Here, the authors could also make the role of the hydrological processes - flux versus storage - more clear in the work.) Then, is the overall TWS increase mostly due to the accumulation of water in the lakes/reservoirs, or may other storages also play a role? The results the authors show, do suggest that quite some of the increase sources from the lakes. However, since up to 50% of annual variations occur only during very specific times, e.g., dry years (further comments below), and the size of the linear trend is quite different (Figure 10, additional numeric quantification of this overall increase might be helpful to compare SWS and TWS) a large part of the interannual increase is still unexplained by SWS. However, the correlation between the time series (TWS and SWS in Figure 10, bottom) is striking and the overall rise over the last decades very congruent, just the amplitudes are not matching. So, the question is, does the uncertainty of the SWS amplitudes (from sensors and model parameter) (or from TWS) play a role here? Or are maybe other storage components besides SWS equally important for explaining TWS rise in the region? Just as an example (no need to cite), Werth et al. (2017) have suggested groundwater storage increase may play a role for the storage increase in the Niger basin, and the argument was supported by reports of increasing groundwater levels in the region. Since the cluster for Niger and Lake Victoria have some similarity, maybe groundwater might be relevant in your study area as well. Such or similar thoughts could be included in the discussion and conclusions of the work.
In addition, a few clarifications on the methods and discussions are requested in specific comments further below.
Specific Comments
Abstract: The authors state that the study’s main objective “determine whether natural variability or human interventions caused these changes” in TWS variations. However, based on the presented results, the authors can only discuss this for SWS, not for TWS, since they do not analyze other storage components (see comment above).
Introduction: Clarify why were specifically the interannual variations analyzed and not (also) the seasonal variations?
L91: SPEI is typically labeled a drought index. On the data website they define it as follows: “The SPEI is a multiscalar drought index based on climatic data.”
L130: Approach to estimate water area bases on optical data. How would the uncertainty of the water occurrence probability due to weather conditions affect the final SWS estimate of the study? Also, this drawback of visual light imagery has been solved by other studies that rely on radar data to detect surface water occurrence, with the advantage that they are not weather-dependent. The authors could include in the discussion, why they have not referred to such data instead, or how application of radar instead of visible light remote sensing images might enhance the accuracy of the method.
L137: cululative > culmulative
L145: Add a statement to further spell out what your assumption on the lake profile shape for the volume estimation is, e.g. how steep is the pyramid wall inclined?
L30ff/L147: Please clarify, if all lakes in the region were included? Or to what percentage are smaller lakes neglected?
Equation 2) How representative is such a profile for the lakes? This approach probably has some uncertainty because the lake wall angle is likely heterogeneity inclined, for example, shallower near the shore. Can this introduce a significant error to the total surface water storage estimate? And how large is the uncertainty? It would help to provide a reasonable range of uncertainty for this.
L151: I appreciate that the authors spatially filter the surface water data to mimic the sensitivity of the GRACE observations to water mass changes. The author’s did not, however, clearly state if the applied gaussian filter width of 350 km is comparable to that applied during the GRACE data processing as conducted for the COST-G dataset. A different filter width can significantly alter the amplitude in storage variations. Since the GRACE dataset used is a unified from various datasets, this might be a bit more complex to evaluate. However, a discussion of it is missing. Optionally, this could be included as another source of uncertainty in the surface water storage time series.
151: Please indicate how the filtering was conducted, e.g. in the spatial or frequency (spherical harmonic) domain.
L154: the term “simple” is vague here. I assume you are referring an assumption for stationarity of the temporal components in the time series, as stated further below in L159? Different approaches available (e.g. fourier based, or others) are not more or less simple, but instead they are potentially better applicable to climate processes. Also, the non-stationarity of climate signals is not only present in seasonal components but also in the inter-annual/trend components, hence, why STL is better applicable for both. Please rephrase to make this clearer.
L160ff: how does the smoothing parameter affect the signal decomposition? What was the criteria for choosing them. I understand this is a trial and error approach, and requires some empirical decision making. However, it would be good to try to write down what you were aiming for, when choosing the parameter.
L156/Section 3: Please indicate if the STL is loss-free or not.
L171/Figure 4: If I understand this correctly, the black time series (original in a) is corresponding to the blue long-term signal in b (no-data gap)? I wonder if it makes sense to match the color (same in c and d)?
Figure 5&6: The clusters are coded two ways, once by colors and once by numbers. It would be easier to if this is limited to either one. Or also add the colors in the titles, behind numbers in figure 5, e.g. cluster 5 (red) and add numbers to colored dendogram in Figure 6.
Figure 6: I was wondering, if it would be sufficient to have this in a supplement. The additional information is minor, as the time series in Figure 5 already show degree of similarity.
L206: I suggest to add brief explanation: regions with overall positive trend are those located in Central Africa (including blue, yellow, dark green, pink).
L207ff: Here, the authors shift from a 7-cluster analysis to an 8-cluster analysis without a more detailed explanation. This should either be a new paragraph, to make that shift more clear. Alternatively, I am wondering if Figures 5-7 could be combined. For example, why is cluster 8 not also shown in Figure 5?
L207: if I understand it correctly, the sub-clusters in Figure 7 are also appearing in the cluster tree in Figure 6, as the authors emphasis on that here. However, in Figure 6 they are colored all light blue. I was wondering, if it makes sense to mark the purple cluster 8 also in Figure 6, to be more clear.
L209: … has even larger TWS amplitudes than … > … has a larger TWS amplitude than ...
L210-211: change the word “marked” to ”significant”,“distinct”, or “fast”
L214/Figure 9: The graph in Figure 9 does not look like the values are accumulated, but rather filtered with some kind of moving-window filter of certain width (or accumulated within a moving window). In case of only accumulating, you would have only values every n months, with n being the accumulation period. Please clarify.
L214-215: You compare accumulated precipitation with SST filtered TWS. The two time series are treated with different methods. Are they really comparable this way? Why do the authors not also apply an SST filter (using the same parameter as for TWS) to the precipitation data instead? This would also save them from estimating the correct filter-width for P.
Figure 8 might also be ok for a supplement, instead of the main manuscript?
L220-2029: I am wondering if this can be shortened, as P becomes less relevant given their concluding that E is missing to better compared to TWS. However, this conclusion is rather trivial from a hydrological perspective.
L218: Maybe add a sentence explaining the purpose of the violin plot. Does the change in width of the blue areas (violins) have any meaning?
L232-233: unclear formulations, please rephrase a bit simpler.
L233-234: unclear formulation, rephrase. “…longterm observation of ?”; also you do not put P-E in relation to TWS, but SPEI
Figure 9: add precipitation to the legend.
L243: do > does
L253: for the names > for their names
L256: I cannot see the 50% in Figure 10, the color bar is kind of vague. The top left Figure 10 colors seem saturated given the color bar. What are the maximum value in Figure 10 top row? It looks to me more like 30%, given the time series in Figure 10 bottom.
Figure 10: The red polygon shown in the upper three panels is neither labeled the legend, nor in the caption. I assume it is outline for cluster 7? Please add.
L261: space missing
Figure 11 caption: correct spelling of de-sesonalized
Figure 11: compares PEV and correlation for de-sesonalized SWS and TWS. It would be useful to show the deseasonalized time series somewhere, e.g. add to Figure 11 or Figure 10 bottom?
L285: the 50% occur only for years with very low TWS, but not for wetter years. Hence, this feels like an overstatement (also in the abstract). Maybe it would be more representative to also estimate the median or mean of the explained percentage over the years? Or it would be more transparent to discriminate between dry and wet years (see also comment for absract above)?
L289: Victoria Nile > Nictoria Nile River
L291-295: this information might be better suited already in Section 2.3 to provide more detail on the surface water bodies in the region and how they are managed. It would already help for understanding previous sections.
L311: Can you provide a reference to support this statement?
L235: govern > governed
L235-235: sentence unclear, reformulate
Figure 13: This is not compiled well to support the discussion in Section 7. Maybe presenting the time series in a single or stacked panels and/or in comparison to TWS and/or SWS time series would help the purpose more?
L363: reformulate sentence, a lake cannot lead, rather results for the lake.
L360-362: I disagree, SWS does not fully explain the steady increase of TWS, as shown in Figure 10, only partially. The value of this multi-year TWS/SWS rise was also not quantified in the manuscript, maybe it would help to add this?
L366: The connection between dam discharge and TWS is not clearly shown in the manuscript.
References
Werth, S., White, D., & Bliss, D. W. (2017). GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin. Journal of Geophysical Research: Solid Earth, 122(12), 10,459-10,477. https://doi.org/10.1002/2017JB014845
Citation: https://doi.org/10.5194/egusphere-2024-641-RC2 -
AC2: 'Reply on RC2', Eva Boergens, 01 Jul 2024
Dear Susanna,
Thank you very much for your valuable comments. This first rebuttal letter will only address the more significant concerns and changes to the study. Minor changes related to the text, figures, or language-related comments will not all be answered individually here. Still, we will consider them all in the revised version of the manuscript.
Please see our answers in the attached pdf.
With kind regards,
Eva Boergens (on behalf of the authors)
- AC5: 'Reply on AC2', Eva Boergens, 09 Aug 2024
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AC2: 'Reply on RC2', Eva Boergens, 01 Jul 2024
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RC3: 'Comment on egusphere-2024-641', Bramha Dutt Vishwakarma, 22 May 2024
Summary: The manuscript uses GRACE(-FO) along with Altimetry, precipitation, and Evaporation datasets to analyse the spatiotemporal behavior of the East African rift region. In terms of tools, STL and clustering algorithms were used first and then comparisons were made between several variables (lake storage, SPEI, and TWS) to draw conclusions.
General comments: the application of a clustering algorithm to identify regions with similar behavior is one of the most interesting part of the manuscript, but this is not fully explored. The article has numerous language and grammar errors (from spelling mistakes to redundant and incorrect sentence formations). Authors indicate that they have investigated human vs climate signals, but the analysis in that direction is also weak. They found a good agreement between Altimetry and GRACE & GRACE-FO in general and that remains the most convincing part. Here are some recommendations/concerns/suggestions:
- Line 5: here the study claims that it will characterize and analyze the interannual TWS variations over the East African rift region to provide a categorical classification: natural or human. Several important hydrological aspects that represent human and climate have been missed in the analysis: for example, groundwater is not accounted for in the whole analysis. The African Monsoon system has a huge impact on the decadal water resource availability in Eastern Africa, which has not been included in the discussion. Even the Monsson system is evolving with climate (see https://www.nature.com/articles/s43017-023-00397-x ). Nevertheless, in the conclusions section, the characterization and analysis is not clearly written: how much of interannual variation can be explained by precipitation (or P-E) and how much of it is due to human decisions on lake outflow. It is appreciated that lake release data is not available, but some quantitative insights based on remote sensing data would add a lot of value and increase the impact of this work on our current state of understanding.
- Line 8: “separate the TWS signal” -- > “decompose the TWS signal”
- Line 10: “study’s region” --> study region. This also raises the question if the study region chosen here is the same as East African Rift (EAR)? There are maps of the EAR that differ from the study region obtained via clustering. For example, the Lake Kariba and Lake Malawi (https://www.sciencedirect.com/science/article/pii/S1464343X05001251) are also part of the rift system but outside the study region here. If authors are choosing this name because it is already existing in literature, citing the source would help.
- Line 11: The sentence would read better if written as: We observe a decline in TWS un till 2006, followed by a steady increase till 2016, and a sharp increase in 2019 and 2020.
- Line 13: “ large lakes of the region explain large parts” --> “lakes explain large parts”
- Line 14: “alone contribute up to” --> “alone contributes up to”
- Line 14: “Satellite altimetry reveals the anthropogenically altered discharge downstream of the dam” : This sentence hurts the coherence of the text. This may be moved to the first or second line in the paragraph.
- It is already well known that lake water levels and the discharge from the Nile River are Anthropogenic. Authors have cited several papers that also find the same. Hence the last line of the abstract should contain a novel insight from this study.
- Line 21: delete: “cover equally surface and subsurface water storage compartments, i.e., they” (this info is redundant please remove)
- Line 24: Please rephrase. Either it has to be complementary data or delete “and invaluable complement to all other”.
- Line 25: “tiny” please use a more quantitative adjective such as (micrometer level).
- Line 25: please rephrase : two twin satellites: language wise it appears that there are 4 of them.
- Line 25: instead of “trailing each other” it should be “one following the other”.
- Line 26-27: “From collecting these .... derived” --> These intersatellite range measurements over a month are then processed to obtain monthly mean gravity field of the Earth.
- Line 27: “by computing and comparing .....investigated” --> Changes in the Earth’s gravity field are then represented in terms of mass changes near the Earth’s surface.
- Line 35: Rewrite as: Quantifying continental scale terrestrial water storage (TWS) variations has been possible only with GRACE.
- Line 46: “These region’s lakes ... ecosystems ” --> “These lakes have been named in the Global 200 eco ... ecosystems”
- Line 52: “monitoring, standardised indices got well established, namely the .. . For example” --> monitoring, well known indices such as SPI and SPEI have been used extensively.”
- Line 57: “storage variations are by now commonly .. “ --> Storage variations are now also monitored.. “
Similar changes are recommended for the rest of the manuscript. A thorough proof reading is essential. I will now only point out spelling mistakes for the manuscript after line 60.
20. Line 72: It is true that the region experienced drought and more water was released. However, then an independent Hydrologic engineer broke this news (https://archive.internationalrivers.org/resources/dams-draining-africa-s-lake-victoria-4117) and the treaty’s terms and conditions were enforced which led to a swift recovery. It would be nice to acknowledge that the dam water release was disproportionate and when ensured they were within agreed limits, conditions improved.
21. Line 77: The 2018 Rodell paper has termed the TWS increase as “probable natural variability”, however, there are studies that also investigate the severity and cause for the trend. In Vishwakarma et al., 2021 (https://iopscience.iop.org/article/10.1088/1748-9326/abd4a9/pdf) the trend observed over the region is found to be “extreme gain” in comparison to the long-term hydrological natural variability. Then by Zhong etal., 2023, these trends have further been attribute to precipitation driven and non-precipitation driven ( https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023WR035817). They found that the trends observed are mostly non-precipitation driven events. This puts the region into “anthropogenic” category, not the “natural variability”. Discussion must be added to improve the attribution, or the lack of it.
22. Line 93: Rephrase.
23. Figure 2: this map is not of the full East African Rift region, but a part of it. See the map in (https://www.sciencedirect.com/science/article/pii/S1464343X05001251).
24. Line 139: continues --> continuous.
25. Line 140 – 145: How does this method compares to that given by Wang et al., 2011 for converting lake height to storage? (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2011WR010534)
26. Line 149 –151: Please mention the interpolation technique used
27. Line 158: I am not sure what is meant by “multi-year interannual” Maybe I am wrong, but multi-year variations and interannual variations are synonyms.
28. Line 163: The guidelines for choosing STL parameters are indicative only. For regions such as EAR, where there are two wet seasons and Monsoon system exists (see climatology in Figure 1(d) in https://www.nature.com/articles/s43017-023-00397-x and Figure 5 in https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014350 ), you may also test the seasonal signal to be semiannual. Maybe more investigation is needed to show that these parameters are a good choice.
29. Figure 4: The caption appears to be incorrect. Original TWS time-series is red while the data gap is in black. Is blue and green also correctly matched?
30. Line 195: 700 km diameter is good enough to be resolved by GRACE (Vishwakarma et al., 2018: https://www.mdpi.com/2072-4292/10/6/852). Not sure why it was termed “not meaningful” here.
31. I believe the figure 5 has some interesting time-series. The Cluster analysis divided the region into entities that could also be explained via climatology and human intervention. This aspect was not explored further for Africa (maybe something in the future), but at least for EAR, there should be some discussion about what makes it unique in terms of climate and why this clustering makes sense.
32. Section 5: Monthly precipitation and TWS are not directly comparable because of the water budget equation, where P = ET + R + d(S)/dt. To make a fair comparison its recommended that the TWS time-series is differentiated and then compared to the weighted mean Precipitation data (see equation 2 and 3 in Lehmann et al., 2022). Also the concept of accumulated precipitation is not clearly explained. After reading the first paragraph of section 5 three times, I had three interpretations. For example, is the TWS compared with Annual averages of P? Or Is there a moving window of 12 months to compute accumulated P? or the P is accumulated for 12 months and then again for 12 months, which is then added to the last sum of 12 months? It is unclear. Figure 9 has a plot with accumulated P and TWS plotted, which helps me rule out the first and third option, but still not clear.
33. Figure 9 axis label says precipitation, but the caption says accumulated precipitation. Also, the units of precipitation should be (mm / [time]) and when accumulated (integrated) should become mm. Please revisit this aspect as well.
34. Figure 9: Why is the magnitude of TWS in excess of 4500 mm and there is no negative value. Is TWS also accumulated? The interannual TWS in figure 7 has a range [-200 +400].
35. Figure 9: Another important observation is that the SPEI (GPCC-based) shows no rise between 2008 to 2019, while there is a rise in TWS as well as SPEI (CRU-based). If there is a lack of trust in precipitation product then how reliable are the conclusions drawn based on them?
36. Line 228: The decision to choose 48 over 36 needs more thought. The parameters chosen for STL window size could be the guiding light.
37. Line 250: govern --> governed, despise --> despite
38. Since the signal leakage due to filtering is a problem that will reduce the quality of observations, is it possible to rather use a leakage-correction method for improving GRACE data (such as the forward modelling approach by Chen et al., 2015) instead of filtering the SWS data from altimetry and Lake area?
39. Figure 10: The SWS change slowly while TWS drastically between 2010 and 2017. Precipitation is also not increasing much as seen from Figure 9. The increased lake storage might also interact with the groundwater system. Hence a different rate of change could be attributed to groundwater-recharge (see Figure 5 in https://www.sciencedirect.com/science/article/pii/S0048969721044284 )
40. Line 89: In this chapter --> in this section.
41. Section 8, conclusions: Authors claim that there are clear linear trends over Northern India. If one uses STL there is a strong interannual variability over North-west India as well. As we increase the time length, interannual (decadal also) variations start to appear, which is why a longer time-series is needed for climate analysis. Line 310: courser --> coarser
42. Line 361: this became possible --> this was made possible
The manuscript is easy to read in parts and requires some effort from the readers in others. A thorough proofreading is required. This is quite an interesting problem and I wish the authors all the best. I hope these comments will be helpful.
Best wishes,
Bramha Dutt Vishwakarma
Citation: https://doi.org/10.5194/egusphere-2024-641-RC3 -
AC3: 'Reply on RC3', Eva Boergens, 01 Jul 2024
Dear Bramha,
Thank you very much for your valuable comments. This first rebuttal letter will only address the significant concerns and changes to the study. Minor text changes or language-related comments will not all be answered individually here, but we will consider them all in the revised version of the manuscript.
Please see our answers in the attached pdf.
With kind regards,
Eva Boergens (on behalf of the authors)
- AC6: 'Reply on AC3', Eva Boergens, 09 Aug 2024
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-641', Vagner Ferreira, 09 Apr 2024
The study “Interannual Variations of Terrestrial Water Storage in the East African Rift Region” addresses an interesting topic and provides some valuable insights. However, several issues need to be addressed before the manuscript can be considered for publication. I recommend major revisions based on the following main points and some other minor comments presented below.
Main points:
1. The authors state that “human intervention in the form of dam management at Lake Victoria substantially contributes to the TWS variability” (lines 15-16); however, they didn’t provide a clear estimation of the magnitude of this contribution. It would be interesting to see the relative contribution of natural variability and human interventions to the observed TWS fluctuations.
2. The study proposes a clustering approach to identify the East African Rift region as having similar interannual TWS dynamics. However, the justification for focusing on this specific region could be further improved by providing a stronger rationale for selecting this study area. The manuscript could highlight the East African Rift region's unique hydrological characteristics, ecological significance, or socio-economic importance.
3. Although the study compares TWS variations with precipitation, evapotranspiration, and surface water storage in the major lakes, the analysis of the underlying drivers remains somewhat superficial. The study could provide more information about the potential mechanisms that link these factors to TWS variability in the region (e.g., land use/land cover changes, soil moisture dynamics, groundwater recharge, and human water abstractions). A more comprehensive discussion of these drivers would beef up the interpretations and conclusions of the study.
4. The study cites some relevant literature; however, it could improve by discussing how the proposed study’s findings compare to or advance previous research on TWS variability in the East African Rift region. The paper would benefit from a more thorough synthesis of the existing knowledge and a clearer articulation of this study’s novel contributions.
5. The study lacks a thorough assessment of the uncertainties associated with the GRACE/GRACE-FO data, the precipitation and evapotranspiration datasets, and the surface water storage estimates. It would be interesting to see a more detailed description of the potential sources of error and their implications for the results. Also, the authors could elaborate more on the limitations, such as the coarse spatial resolution of GRACE data and the lack of ground-based validation data. These limitations could be explicitly acknowledged and discussed.
6. The current conclusion section is somewhat vague and does not fully address the broader implications of the findings for water resources management, ecosystem conservation, or climate change adaptation in the region (conditioned to the rationale for selecting the study area as per comment 2). The authors could elaborate on the potential applications of the study’s findings.
Minor comments:
7. Between lines 35-40, where it is “Niger Basin in West Africa,” it should be “Volta Basin in West Africa” in the context of the sentence.
8. Lines 134-137: The description of the water occurrence map processing is unclear. Please provide more details on how the 95% occurrence threshold was determined and how it affects the estimation of lake surface areas.
9. Lines 139-143: Please discuss the limitations of the surface water storage analysis based on a simplified relationship between lake level and area changes based on empirical cumulative distribution functions (ECDF). What could be the potential uncertainties it introduces in the storage estimates? For example, the monotonic and continuous relationship between lake level and area might not always be the case in reality. Lakes with complex bathymetry or irregular shorelines may exhibit non-monotonic or discontinuous relationships between level and area. However, the ECDF approach can handle outliers or anomalies in the input data more robustly than a linear regression used by Ferreira et al. (2018).
10. Lines 240-247: The discussion of the differences between the two SPEI datasets seems speculative. Please provide more evidence to support the claim that the divergence after 2008 is caused by differences in precipitation data rather than PET estimation methods.
11. Lines 290-295: The description of the Nalubaale Dam and its impact on Lake Victoria's water levels is incomplete. Please provide more information on the characteristics of the dam (e.g., operating rules) and downstream effects on the Victoria Nile and other water bodies. A study area section presenting the East African Rift Region would be useful.
12. Lines 314-315: Please provide a more rigorous assessment of the data quality and its impact on the correlation analysis.
13. Lines 367-368: That concluding statement seems too broad and not fully supported by the analysis. Please refine this conclusion and provide a more nuanced interpretation of the relative contributions of natural and anthropogenic factors to TWS variability.
14. Please revise the English since there are several issues (e.g., Line 5 shows “region region”, Line 92 shows “We analyses…”)Citation: https://doi.org/10.5194/egusphere-2024-641-RC1 -
AC1: 'Reply on RC1', Eva Boergens, 01 Jul 2024
Dear Vagner,
Thank you very much for your valuable comments. This first rebuttal letter will only address the more significant concerns and changes to the study. Please find them in the attached pdf.
Minor text changes, figures, or language-related comments will not all be answered individually here, but we will consider them in the revised version of the manuscript.
With kind regards,
Eva Boergens (on behalf of the authors)
- AC4: 'Reply on AC1', Eva Boergens, 09 Aug 2024
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AC1: 'Reply on RC1', Eva Boergens, 01 Jul 2024
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RC2: 'Comment on egusphere-2024-641', Susanna Werth, 15 May 2024
General Comments
The study presents an analysis of long-term variations in GRACE total water storage variations (TWS) over the last 22 years for Africa and compares the data to surface water storage (SWS) variations in major lakes derived from satellite altimetry in central Africa. The authors compare the datasets also with meteorological/drought data via time series analysis and statistical methods. They discuss the influence of human and climate on the variability in TWS and surface SWS in Central Africa. They also provide some novel insight into the TWS dataset through a cluster analysis for the continent Africa. The authors have conducted a good work. It provides detailed information on data and methods used and provides very interesting insight into the water storage variations in the study area around Lake Victoria in Africa. I do think, however, that a more structured organization of the manuscript; a quantification of uncertainty of the surface water storage estimates; and, following from that, a more comprehensive discussion and concise conclusion of the results would make the work clearer and more significant.
On organization of the work: In Section 3-7, the authors cover certain topics. For Section 3-5 they combine respective methods, results and discussions into one section. For Section 6, some of the relevant methods are explained in Section 2, then some more method description is added after results and discussion in this section. The authors often jump between results presentations along various figures and corresponding piece-wise discussion and conclusions. It makes it harder for the reader to discriminate objective facts from opinion or suggestions by the authors. Some of this becomes especially a problem in Section 5 and even more so Section 7, which are presented the least clear. A clearer structure should be introduced to the entire manuscript, for example, for example, either the methods, or the discussions should be split off in some way. Also, some of the figure organization need some improvements, for example some legends are incomplete. Introducing panel letters might help to address results in figures with more than two panels more clearly. Some figures might be better suited for a supplement. Further suggestions are given below.
On uncertainty of the results: estimates of TWS from GRACE as well as SWS from altimetry and subsequent modeling includes several sources of uncertainty, e.g. measurement errors, parameter uncertainty. These uncertainties should be discussed. But since the authors are quantifying percentage of explained signal variance, a quantification is also suggested, especially for SWS. The conclusions need to be put into perspective of the uncertainties (see also next section).
On the conclusions: The study's conclusion on the nature of the driver of TWS variations, i.e. whether it is either human or climate during certain temporal periods, is not fully supported by the results and analysis provided. First, this statement is mainly directly addressed in Section 7, where water levels of various lakes and river level are compared, and the impact of dam management is highlighted. There is no direct comparison with SWS and TWS variations provided. Second, a correlation of TWS to drought indicators is not an explanation or proof of climate dominance, as stated in the conclusion (L357-358), because human water use (e.g. of surface or groundwater) itself is typically also heavily influenced by drought conditions and might therefore similarly impact TWS. In addition, in the rest of the manuscript, the authors only analyze the SWS portion of TWS variation but no soil moisture or groundwater, hence, a large portion of TWS variation remains unexplained, and therefore a conclusion on human or climate dominance in TWS remains very speculative. I am also wondering if such a conclusion is even relevant to emphasize on the importance of the work, but rather may take away from the actual interesting quantitative and qualitative findings of the work on the importance of SWS in the region. This could be more highlighted by slightly altering the discussion of the findings.
In addition, the authors do not comprehensively quantify and discuss why TWS may be rising overall in the Central Africa/Lake Victoria region over the last two decades (they did so only for specific sections of the TWS time series or in relation to P and SWS). It was shown that precipitation plays an important role. However, the P increase (or change in ET) does not indicate if and where the water is stored. (Here, the authors could also make the role of the hydrological processes - flux versus storage - more clear in the work.) Then, is the overall TWS increase mostly due to the accumulation of water in the lakes/reservoirs, or may other storages also play a role? The results the authors show, do suggest that quite some of the increase sources from the lakes. However, since up to 50% of annual variations occur only during very specific times, e.g., dry years (further comments below), and the size of the linear trend is quite different (Figure 10, additional numeric quantification of this overall increase might be helpful to compare SWS and TWS) a large part of the interannual increase is still unexplained by SWS. However, the correlation between the time series (TWS and SWS in Figure 10, bottom) is striking and the overall rise over the last decades very congruent, just the amplitudes are not matching. So, the question is, does the uncertainty of the SWS amplitudes (from sensors and model parameter) (or from TWS) play a role here? Or are maybe other storage components besides SWS equally important for explaining TWS rise in the region? Just as an example (no need to cite), Werth et al. (2017) have suggested groundwater storage increase may play a role for the storage increase in the Niger basin, and the argument was supported by reports of increasing groundwater levels in the region. Since the cluster for Niger and Lake Victoria have some similarity, maybe groundwater might be relevant in your study area as well. Such or similar thoughts could be included in the discussion and conclusions of the work.
In addition, a few clarifications on the methods and discussions are requested in specific comments further below.
Specific Comments
Abstract: The authors state that the study’s main objective “determine whether natural variability or human interventions caused these changes” in TWS variations. However, based on the presented results, the authors can only discuss this for SWS, not for TWS, since they do not analyze other storage components (see comment above).
Introduction: Clarify why were specifically the interannual variations analyzed and not (also) the seasonal variations?
L91: SPEI is typically labeled a drought index. On the data website they define it as follows: “The SPEI is a multiscalar drought index based on climatic data.”
L130: Approach to estimate water area bases on optical data. How would the uncertainty of the water occurrence probability due to weather conditions affect the final SWS estimate of the study? Also, this drawback of visual light imagery has been solved by other studies that rely on radar data to detect surface water occurrence, with the advantage that they are not weather-dependent. The authors could include in the discussion, why they have not referred to such data instead, or how application of radar instead of visible light remote sensing images might enhance the accuracy of the method.
L137: cululative > culmulative
L145: Add a statement to further spell out what your assumption on the lake profile shape for the volume estimation is, e.g. how steep is the pyramid wall inclined?
L30ff/L147: Please clarify, if all lakes in the region were included? Or to what percentage are smaller lakes neglected?
Equation 2) How representative is such a profile for the lakes? This approach probably has some uncertainty because the lake wall angle is likely heterogeneity inclined, for example, shallower near the shore. Can this introduce a significant error to the total surface water storage estimate? And how large is the uncertainty? It would help to provide a reasonable range of uncertainty for this.
L151: I appreciate that the authors spatially filter the surface water data to mimic the sensitivity of the GRACE observations to water mass changes. The author’s did not, however, clearly state if the applied gaussian filter width of 350 km is comparable to that applied during the GRACE data processing as conducted for the COST-G dataset. A different filter width can significantly alter the amplitude in storage variations. Since the GRACE dataset used is a unified from various datasets, this might be a bit more complex to evaluate. However, a discussion of it is missing. Optionally, this could be included as another source of uncertainty in the surface water storage time series.
151: Please indicate how the filtering was conducted, e.g. in the spatial or frequency (spherical harmonic) domain.
L154: the term “simple” is vague here. I assume you are referring an assumption for stationarity of the temporal components in the time series, as stated further below in L159? Different approaches available (e.g. fourier based, or others) are not more or less simple, but instead they are potentially better applicable to climate processes. Also, the non-stationarity of climate signals is not only present in seasonal components but also in the inter-annual/trend components, hence, why STL is better applicable for both. Please rephrase to make this clearer.
L160ff: how does the smoothing parameter affect the signal decomposition? What was the criteria for choosing them. I understand this is a trial and error approach, and requires some empirical decision making. However, it would be good to try to write down what you were aiming for, when choosing the parameter.
L156/Section 3: Please indicate if the STL is loss-free or not.
L171/Figure 4: If I understand this correctly, the black time series (original in a) is corresponding to the blue long-term signal in b (no-data gap)? I wonder if it makes sense to match the color (same in c and d)?
Figure 5&6: The clusters are coded two ways, once by colors and once by numbers. It would be easier to if this is limited to either one. Or also add the colors in the titles, behind numbers in figure 5, e.g. cluster 5 (red) and add numbers to colored dendogram in Figure 6.
Figure 6: I was wondering, if it would be sufficient to have this in a supplement. The additional information is minor, as the time series in Figure 5 already show degree of similarity.
L206: I suggest to add brief explanation: regions with overall positive trend are those located in Central Africa (including blue, yellow, dark green, pink).
L207ff: Here, the authors shift from a 7-cluster analysis to an 8-cluster analysis without a more detailed explanation. This should either be a new paragraph, to make that shift more clear. Alternatively, I am wondering if Figures 5-7 could be combined. For example, why is cluster 8 not also shown in Figure 5?
L207: if I understand it correctly, the sub-clusters in Figure 7 are also appearing in the cluster tree in Figure 6, as the authors emphasis on that here. However, in Figure 6 they are colored all light blue. I was wondering, if it makes sense to mark the purple cluster 8 also in Figure 6, to be more clear.
L209: … has even larger TWS amplitudes than … > … has a larger TWS amplitude than ...
L210-211: change the word “marked” to ”significant”,“distinct”, or “fast”
L214/Figure 9: The graph in Figure 9 does not look like the values are accumulated, but rather filtered with some kind of moving-window filter of certain width (or accumulated within a moving window). In case of only accumulating, you would have only values every n months, with n being the accumulation period. Please clarify.
L214-215: You compare accumulated precipitation with SST filtered TWS. The two time series are treated with different methods. Are they really comparable this way? Why do the authors not also apply an SST filter (using the same parameter as for TWS) to the precipitation data instead? This would also save them from estimating the correct filter-width for P.
Figure 8 might also be ok for a supplement, instead of the main manuscript?
L220-2029: I am wondering if this can be shortened, as P becomes less relevant given their concluding that E is missing to better compared to TWS. However, this conclusion is rather trivial from a hydrological perspective.
L218: Maybe add a sentence explaining the purpose of the violin plot. Does the change in width of the blue areas (violins) have any meaning?
L232-233: unclear formulations, please rephrase a bit simpler.
L233-234: unclear formulation, rephrase. “…longterm observation of ?”; also you do not put P-E in relation to TWS, but SPEI
Figure 9: add precipitation to the legend.
L243: do > does
L253: for the names > for their names
L256: I cannot see the 50% in Figure 10, the color bar is kind of vague. The top left Figure 10 colors seem saturated given the color bar. What are the maximum value in Figure 10 top row? It looks to me more like 30%, given the time series in Figure 10 bottom.
Figure 10: The red polygon shown in the upper three panels is neither labeled the legend, nor in the caption. I assume it is outline for cluster 7? Please add.
L261: space missing
Figure 11 caption: correct spelling of de-sesonalized
Figure 11: compares PEV and correlation for de-sesonalized SWS and TWS. It would be useful to show the deseasonalized time series somewhere, e.g. add to Figure 11 or Figure 10 bottom?
L285: the 50% occur only for years with very low TWS, but not for wetter years. Hence, this feels like an overstatement (also in the abstract). Maybe it would be more representative to also estimate the median or mean of the explained percentage over the years? Or it would be more transparent to discriminate between dry and wet years (see also comment for absract above)?
L289: Victoria Nile > Nictoria Nile River
L291-295: this information might be better suited already in Section 2.3 to provide more detail on the surface water bodies in the region and how they are managed. It would already help for understanding previous sections.
L311: Can you provide a reference to support this statement?
L235: govern > governed
L235-235: sentence unclear, reformulate
Figure 13: This is not compiled well to support the discussion in Section 7. Maybe presenting the time series in a single or stacked panels and/or in comparison to TWS and/or SWS time series would help the purpose more?
L363: reformulate sentence, a lake cannot lead, rather results for the lake.
L360-362: I disagree, SWS does not fully explain the steady increase of TWS, as shown in Figure 10, only partially. The value of this multi-year TWS/SWS rise was also not quantified in the manuscript, maybe it would help to add this?
L366: The connection between dam discharge and TWS is not clearly shown in the manuscript.
References
Werth, S., White, D., & Bliss, D. W. (2017). GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin. Journal of Geophysical Research: Solid Earth, 122(12), 10,459-10,477. https://doi.org/10.1002/2017JB014845
Citation: https://doi.org/10.5194/egusphere-2024-641-RC2 -
AC2: 'Reply on RC2', Eva Boergens, 01 Jul 2024
Dear Susanna,
Thank you very much for your valuable comments. This first rebuttal letter will only address the more significant concerns and changes to the study. Minor changes related to the text, figures, or language-related comments will not all be answered individually here. Still, we will consider them all in the revised version of the manuscript.
Please see our answers in the attached pdf.
With kind regards,
Eva Boergens (on behalf of the authors)
- AC5: 'Reply on AC2', Eva Boergens, 09 Aug 2024
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AC2: 'Reply on RC2', Eva Boergens, 01 Jul 2024
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RC3: 'Comment on egusphere-2024-641', Bramha Dutt Vishwakarma, 22 May 2024
Summary: The manuscript uses GRACE(-FO) along with Altimetry, precipitation, and Evaporation datasets to analyse the spatiotemporal behavior of the East African rift region. In terms of tools, STL and clustering algorithms were used first and then comparisons were made between several variables (lake storage, SPEI, and TWS) to draw conclusions.
General comments: the application of a clustering algorithm to identify regions with similar behavior is one of the most interesting part of the manuscript, but this is not fully explored. The article has numerous language and grammar errors (from spelling mistakes to redundant and incorrect sentence formations). Authors indicate that they have investigated human vs climate signals, but the analysis in that direction is also weak. They found a good agreement between Altimetry and GRACE & GRACE-FO in general and that remains the most convincing part. Here are some recommendations/concerns/suggestions:
- Line 5: here the study claims that it will characterize and analyze the interannual TWS variations over the East African rift region to provide a categorical classification: natural or human. Several important hydrological aspects that represent human and climate have been missed in the analysis: for example, groundwater is not accounted for in the whole analysis. The African Monsoon system has a huge impact on the decadal water resource availability in Eastern Africa, which has not been included in the discussion. Even the Monsson system is evolving with climate (see https://www.nature.com/articles/s43017-023-00397-x ). Nevertheless, in the conclusions section, the characterization and analysis is not clearly written: how much of interannual variation can be explained by precipitation (or P-E) and how much of it is due to human decisions on lake outflow. It is appreciated that lake release data is not available, but some quantitative insights based on remote sensing data would add a lot of value and increase the impact of this work on our current state of understanding.
- Line 8: “separate the TWS signal” -- > “decompose the TWS signal”
- Line 10: “study’s region” --> study region. This also raises the question if the study region chosen here is the same as East African Rift (EAR)? There are maps of the EAR that differ from the study region obtained via clustering. For example, the Lake Kariba and Lake Malawi (https://www.sciencedirect.com/science/article/pii/S1464343X05001251) are also part of the rift system but outside the study region here. If authors are choosing this name because it is already existing in literature, citing the source would help.
- Line 11: The sentence would read better if written as: We observe a decline in TWS un till 2006, followed by a steady increase till 2016, and a sharp increase in 2019 and 2020.
- Line 13: “ large lakes of the region explain large parts” --> “lakes explain large parts”
- Line 14: “alone contribute up to” --> “alone contributes up to”
- Line 14: “Satellite altimetry reveals the anthropogenically altered discharge downstream of the dam” : This sentence hurts the coherence of the text. This may be moved to the first or second line in the paragraph.
- It is already well known that lake water levels and the discharge from the Nile River are Anthropogenic. Authors have cited several papers that also find the same. Hence the last line of the abstract should contain a novel insight from this study.
- Line 21: delete: “cover equally surface and subsurface water storage compartments, i.e., they” (this info is redundant please remove)
- Line 24: Please rephrase. Either it has to be complementary data or delete “and invaluable complement to all other”.
- Line 25: “tiny” please use a more quantitative adjective such as (micrometer level).
- Line 25: please rephrase : two twin satellites: language wise it appears that there are 4 of them.
- Line 25: instead of “trailing each other” it should be “one following the other”.
- Line 26-27: “From collecting these .... derived” --> These intersatellite range measurements over a month are then processed to obtain monthly mean gravity field of the Earth.
- Line 27: “by computing and comparing .....investigated” --> Changes in the Earth’s gravity field are then represented in terms of mass changes near the Earth’s surface.
- Line 35: Rewrite as: Quantifying continental scale terrestrial water storage (TWS) variations has been possible only with GRACE.
- Line 46: “These region’s lakes ... ecosystems ” --> “These lakes have been named in the Global 200 eco ... ecosystems”
- Line 52: “monitoring, standardised indices got well established, namely the .. . For example” --> monitoring, well known indices such as SPI and SPEI have been used extensively.”
- Line 57: “storage variations are by now commonly .. “ --> Storage variations are now also monitored.. “
Similar changes are recommended for the rest of the manuscript. A thorough proof reading is essential. I will now only point out spelling mistakes for the manuscript after line 60.
20. Line 72: It is true that the region experienced drought and more water was released. However, then an independent Hydrologic engineer broke this news (https://archive.internationalrivers.org/resources/dams-draining-africa-s-lake-victoria-4117) and the treaty’s terms and conditions were enforced which led to a swift recovery. It would be nice to acknowledge that the dam water release was disproportionate and when ensured they were within agreed limits, conditions improved.
21. Line 77: The 2018 Rodell paper has termed the TWS increase as “probable natural variability”, however, there are studies that also investigate the severity and cause for the trend. In Vishwakarma et al., 2021 (https://iopscience.iop.org/article/10.1088/1748-9326/abd4a9/pdf) the trend observed over the region is found to be “extreme gain” in comparison to the long-term hydrological natural variability. Then by Zhong etal., 2023, these trends have further been attribute to precipitation driven and non-precipitation driven ( https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023WR035817). They found that the trends observed are mostly non-precipitation driven events. This puts the region into “anthropogenic” category, not the “natural variability”. Discussion must be added to improve the attribution, or the lack of it.
22. Line 93: Rephrase.
23. Figure 2: this map is not of the full East African Rift region, but a part of it. See the map in (https://www.sciencedirect.com/science/article/pii/S1464343X05001251).
24. Line 139: continues --> continuous.
25. Line 140 – 145: How does this method compares to that given by Wang et al., 2011 for converting lake height to storage? (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2011WR010534)
26. Line 149 –151: Please mention the interpolation technique used
27. Line 158: I am not sure what is meant by “multi-year interannual” Maybe I am wrong, but multi-year variations and interannual variations are synonyms.
28. Line 163: The guidelines for choosing STL parameters are indicative only. For regions such as EAR, where there are two wet seasons and Monsoon system exists (see climatology in Figure 1(d) in https://www.nature.com/articles/s43017-023-00397-x and Figure 5 in https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014350 ), you may also test the seasonal signal to be semiannual. Maybe more investigation is needed to show that these parameters are a good choice.
29. Figure 4: The caption appears to be incorrect. Original TWS time-series is red while the data gap is in black. Is blue and green also correctly matched?
30. Line 195: 700 km diameter is good enough to be resolved by GRACE (Vishwakarma et al., 2018: https://www.mdpi.com/2072-4292/10/6/852). Not sure why it was termed “not meaningful” here.
31. I believe the figure 5 has some interesting time-series. The Cluster analysis divided the region into entities that could also be explained via climatology and human intervention. This aspect was not explored further for Africa (maybe something in the future), but at least for EAR, there should be some discussion about what makes it unique in terms of climate and why this clustering makes sense.
32. Section 5: Monthly precipitation and TWS are not directly comparable because of the water budget equation, where P = ET + R + d(S)/dt. To make a fair comparison its recommended that the TWS time-series is differentiated and then compared to the weighted mean Precipitation data (see equation 2 and 3 in Lehmann et al., 2022). Also the concept of accumulated precipitation is not clearly explained. After reading the first paragraph of section 5 three times, I had three interpretations. For example, is the TWS compared with Annual averages of P? Or Is there a moving window of 12 months to compute accumulated P? or the P is accumulated for 12 months and then again for 12 months, which is then added to the last sum of 12 months? It is unclear. Figure 9 has a plot with accumulated P and TWS plotted, which helps me rule out the first and third option, but still not clear.
33. Figure 9 axis label says precipitation, but the caption says accumulated precipitation. Also, the units of precipitation should be (mm / [time]) and when accumulated (integrated) should become mm. Please revisit this aspect as well.
34. Figure 9: Why is the magnitude of TWS in excess of 4500 mm and there is no negative value. Is TWS also accumulated? The interannual TWS in figure 7 has a range [-200 +400].
35. Figure 9: Another important observation is that the SPEI (GPCC-based) shows no rise between 2008 to 2019, while there is a rise in TWS as well as SPEI (CRU-based). If there is a lack of trust in precipitation product then how reliable are the conclusions drawn based on them?
36. Line 228: The decision to choose 48 over 36 needs more thought. The parameters chosen for STL window size could be the guiding light.
37. Line 250: govern --> governed, despise --> despite
38. Since the signal leakage due to filtering is a problem that will reduce the quality of observations, is it possible to rather use a leakage-correction method for improving GRACE data (such as the forward modelling approach by Chen et al., 2015) instead of filtering the SWS data from altimetry and Lake area?
39. Figure 10: The SWS change slowly while TWS drastically between 2010 and 2017. Precipitation is also not increasing much as seen from Figure 9. The increased lake storage might also interact with the groundwater system. Hence a different rate of change could be attributed to groundwater-recharge (see Figure 5 in https://www.sciencedirect.com/science/article/pii/S0048969721044284 )
40. Line 89: In this chapter --> in this section.
41. Section 8, conclusions: Authors claim that there are clear linear trends over Northern India. If one uses STL there is a strong interannual variability over North-west India as well. As we increase the time length, interannual (decadal also) variations start to appear, which is why a longer time-series is needed for climate analysis. Line 310: courser --> coarser
42. Line 361: this became possible --> this was made possible
The manuscript is easy to read in parts and requires some effort from the readers in others. A thorough proofreading is required. This is quite an interesting problem and I wish the authors all the best. I hope these comments will be helpful.
Best wishes,
Bramha Dutt Vishwakarma
Citation: https://doi.org/10.5194/egusphere-2024-641-RC3 -
AC3: 'Reply on RC3', Eva Boergens, 01 Jul 2024
Dear Bramha,
Thank you very much for your valuable comments. This first rebuttal letter will only address the significant concerns and changes to the study. Minor text changes or language-related comments will not all be answered individually here, but we will consider them all in the revised version of the manuscript.
Please see our answers in the attached pdf.
With kind regards,
Eva Boergens (on behalf of the authors)
- AC6: 'Reply on AC3', Eva Boergens, 09 Aug 2024
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