Leveraging 20 Years of Remote Sensing to Characterize Surface Phytoplankton Seasonality and Long-Term Trends in Lake Tanganyika

Toussaint, François; Alonso, Alice; Vanclooster, Marnik

doi:https://doi.org/10.5194/egusphere-2025-1326

Preprints

https://doi.org/10.5194/egusphere-2025-1326

Preprints

21 May 2025

| 21 May 2025

Leveraging 20 Years of Remote Sensing to Characterize Surface Phytoplankton Seasonality and Long-Term Trends in Lake Tanganyika

François Toussaint, Alice Alonso, and Marnik Vanclooster

Abstract. Lake Tanganyika, the world's second-largest freshwater lake by volume, is a vital resource for millions in East Africa, providing water, food, and economic opportunities while supporting exceptional biodiversity.

Chlorophyll-a concentration (Chl-a) is a key indicator of phytoplankton biomass and primary productivity, and thus a proxy for the health of aquatic ecosystems. In Lake Tanganyika, Chl-a is known to display strong spatiotemporal horizontal variability with an exceptionally low annual mean and wide ranges of concentrations compared to other tropical or temperate great lakes. This variability is influenced by the lake's hydrodynamic cycle driven by air temperature and wind seasonality. Phytoplankton biomass is suspected to be decreasing due to a strengthening of water column stratification induced by climate change. However, the particular spatiotemporal variability and trends in phytoplankton biomass have never been examined using a lake-wide, temporally continuous long-term record. This study bridges this gap by analyzing satellite remote sensing-derived Chl-a data from the ESA Climate Change Initiative Lakes dataset across the entire surface of Lake Tanganyika over a 20-year period. It offers insight into the Chl-a dynamics with an unprecedented timespan and spatial coverage.

The analysis reveals distinct seasonal patterns in Chl-a concentrations, with shallow regions (depth <170 m) maintaining high levels year-round, while deeper areas exhibit pronounced seasonality tightly linked to known wind patterns. To further explore these spatial differences in seasonal dynamics, the study identifies seven clusters of co-varying Chl-a concentrations, each displaying distinct seasonal behaviours that reflect the lake's hydrodynamic cycle. Long-term trends indicate a decline in Chl-a concentrations of -9 % per decade in deep regions, suggesting decreasing primary productivity. However, this overall decline is nuanced by monthly patterns. In deep regions, the low Chl-a concentrations, mainly observed between November and April, tend to decrease over time at rates between -5 to -15 % per decade when averaged over entire clusters. In contrast high Chl-a values recorded during the most productive months, from August to October, show increasing trends up to 25 %. Nearly all shallow areas, meanwhile, display year-round increases up to 35 % across the Chl-a distribution, with particularly sharp rises in extreme values.

The findings underscore the complexity of Lake Tanganyika's Chl-a dynamics. The observed trends may have significant consequences for the lake's trophic structure and the communities dependent on its resources. Further research is needed to elucidate the underlying drivers of these changes and to assess their broader ecological and socio-economic impacts.

Received: 20 Mar 2025 – Discussion started: 21 May 2025

Competing interests: Some authors (Marnik Vanclooster) are members of the editorial board of journal Hydrology and Earth System Sciences.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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François Toussaint, Alice Alonso, and Marnik Vanclooster

Status: final response (author comments only)

CC1:
'Comment on egusphere-2025-1326', Mariano Bresciani, 04 Jun 2025

The manuscript is well written, easy to read, and makes an interesting point about the high spatial heterogeneity found in deep, very large lakes. Reading the abstract, introduction and methodology, it seems that the study is well structured. The methodology used appears appropriate for the study's objectives. However, the second part of the manuscript, including the results, discussion and conclusion, is less convincing.
The first remark concerns the use of the terms 'shallow coastal area', given that Lake Tanganyika is deep, even in the coastal zones. According to Moss et al. (2003), shallow lakes have a depth of less than 3 m. Secondly, the use of 'high values of Chl-a' is misleading. According to the OECD (1992), eutrophic lakes have a mean Chl-a concentration between 8 and 25 mg/m³. Therefore, the terminology must first be revised. In Lake Tanganyika, we can only speak of littoral/coastal versus pelagic areas, or of 'shallower areas' for comparison purposes, and of lower and higher concentrations of Chl-a, because Lake Tanganyika is an oligotrophic lake. See also later in the minor/specific comments.
Another point is the error associated with the concentration values. Maps showing the standard deviation, for example, are not displayed. There is a risk of speculating on data with a high degree of error because low Chl-a concentrations cast doubt on the reliability of the detection. This applies to both algorithms and spectrophotometric methods. This point is also valid for trend calculations.
From the abstract, it seemed that meteorological and climatic data (e.g. air temperature and wind) were used in the analysis to establish a relationship with Chl-a variability. However, this data was not used in the study, which seems to have been based solely on an assessment of Chl-a evolution.
Lake Surface Water Temperature (LSWT, another Lakes_cci variable) was also mentioned in the Materials and Methods section, but was not presented in the Results section to complement the spatial analysis of Lake Tanganyika. Another point that is missing is the fact that transparency, or Secchi disk depth, was not considered in the work, even though it is fundamental to phytoplankton blooms (see also minor comments later). Remote sensing data relate to surface waters and the euphotic zone, neither of which were discussed in the study. Light penetration is essential not only for phytoplankton, but also for Chl-a measurements using remote sensing techniques.
Another variable which is also available from Lakes_cci dataset is lake water level, that can be related with LSWT and Chl-a evolution in the long-time series. The importance of the levels are mentioned in the discussion but the data are also available and should have been integrated.
As a consequence of this lack in the analysis, the conclusions drawn are not fully appropriate or supported by the findings of this work. See later in the specific comment section.
A significant part of the discussion relates to deep and shallow lakes, but this is not appropriate given the initial remark raised. We could speak of the coastal and pelagic zones for comparison. Furthermore, some statements relate to wind direction and intensity, as well as air temperature, but no data are reported (probably from the literature). While I agree that ground-based wind data are not available, reanalysis data such as wind components (as well as air temperature) are available from the ERA5-Land dataset. Given the size of Lake Tanganyika, the spatial resolution of this data is almost acceptable.
Finally, in the discussion it is useful to mention also the limits of the dataset which can influence the results (e.g. data gap from 2012 to 2016, less availability of data in the rainfall season due to cloud cover).
Other comments:
I suggest adding some more recent citations to the first paragraphs of the introduction, for example lines 34–46.
Line 104: “Lake Tanganyika’s water is remarkably clear and nutrient levels at the surface are generally low.” Some reference data for secchi disk depth would be appreciated.Line 110 and later: “mg C m^-2” without point (mg C.m-2).
Line 115-135: Move to Intro or to support discussion?
Line 144: “Most lakes, including Tanganyika, show data gaps during the 2012 to 2016 period due to validation issues.” Incorrect, in the current available version (2.1), MODIS data for Tanganyika were not released. Delete this sentence or update accordingly.
Line 155: “95% missing values”. Is almost always and with a patchy distribution of the pixel? It seems a large “threshold”.
Line 220: “These coastal zones usually extend a few kilometres offshore and are relatively shallow, with depths generally not exceeding 250 m (Figure 1). They exhibit consistently high to very high median concentrations throughout the year, above 3 mg.m-3 in some areas.” I suggest to change the terminology for shallow areas and “high” median values, as shallow lakes are lakes with a depth < 3 m and eutrophic lakes have a mean Chl-a concentration in the range 8-25 mg m^-3. For this reason it would be better say something like this: “These coastal zones usually extend a few kilometres offshore and are shallower (<250 m) compared to the pelagic areas, and they exhibit higher Chl-a concentration above 3 mg m^-3 in some areas.”
Line 407: “Ground-based wind data are limited, and the reliability of historical wind speed records in the region remains a subject of debate (Eschenbach, 2004; O’Reilly et al., 2003).” Reanalysis data are available and suitable for this case study. I suggest including them in the work.
Lines:417-419: “This duality suggests that Lake Tanganyika exhibits characteristics of both a shallow lake becoming more eutrophic and a deep lake becoming more oligotrophic, adding to the complexity of its ecological functioning.” And Lines 428-430: “The contrasting trajectories suggest that Lake Tanganyika is simultaneously exhibiting characteristics of a deep, nutrient-limited system undergoing further oligotrophication and a shallow system experiencing intensified phytoplankton growth.
As in the previous comment, I don't think these statements are entirely accurate, because Tanganyika doesn't exhibit more "eutrophic" waters (at least not a higher concentration of 3 mg/m³ compared to open waters).
Lines 423-425: “The analysis confirms that shallow areas exhibit persistently high Chl-a concentrations year-round, whereas pelagic regions show strong seasonal variability, with peak productivity following wind-driven mixing events.” This is not really an outcome of this study, as wind was not included in the analysis. It is more a speculative sentence most likely based on literature research.

Citation: https://doi.org/10.5194/egusphere-2025-1326-CC1
- AC1: 'Reply on CC1', François Toussaint, 05 Oct 2025
  
  Thank you for your very interesting remarks, which will help improve this article.
  First, regarding the use of the terms “shallow” and “deep”, they can indeed be misleading when comparing with typical lake depth classification. Instead of simply distinguishing between coastal and pelagic regions, the decision was made to classify zones based on depth rather than distance from the shore. This approach was adopted because certain nearshore areas in Lake Tanganyika are characterized by considerable depths and exhibit seasonal patterns and trends similar to those of offshore deep regions. Therefore, using the terms “coastal” and “pelagic” based solely on proximity to shore could be misleading. To reduce ambiguity, referring to the zones as “shallower” and “deeper” may offer a more accurate and consistent description. This change will be implemented in the whole manuscript, as in line 220 for example.
  The use of “high” and “low” for describing the values of Chl-a can also be misleading in the context of Lake Tanganyika, which is oligotrophic. To better reflect the relative nature of chlorophyll-a (Chl-a) concentrations within the lake, the terms “high” and “low” will therefore be changed to “higher” and “lower” thourghout the text. The use of the terms “oligotrophic”, “oligotrophication” and “eutrophic”, as in lines 417-419 and 428-430, will be revised and changed to “lower levels”, “lowering levels” and “higher levels” of Chl-a.
  Regarding the mention of air temperature and wind seasonality as drivers of Lake Tanganyika’s hydrodynamic cycle, they are first mentioned in the abstract in a paragraph summarizing established knowledge on Chl-a variability. The influence of these variables on the lake’s hydrodynamics is well documented in previous studies (Naithani et al., 2011; Naithani & Deleersnijder, 2004; Plisnier et al., 2023; Sterckx et al., 2023). In the discussion, the wind and air temperature data referenced are drawn from the literature, and the lack of direct data is explicitly acknowledged. While both variables are available from the ERA5-Land dataset, and while it would be highly relevant to study the influence of these variables on Chl-a using such data, this lies beyond the scope of the present study (this is a subject of another study). Consequently, current statements linking Chl-a variability to wind and air temperature are intended as interpretations without causal confirmation. The conclusion and perspectives of the revised paper will mention that the use of reanalysis products to investigate the influence of climatic variables on ecosystem dynamics - and more specifically phytoplankton concentrations, represents a valuable direction for future research.
  A change will also be made in lines 423–425. The sentence will be revised to indicate that peak Chl-a concentrations occurs during periods of strong winds, rather than specifically following wind-driven mixing events, as the precise timing of such events cannot be determined without the use of appropriate wind datasets.
  Lake Surface Water Temperature, indeed available in the Lakes_cci dataset alongside Chl-a data, represents an additional variable that could be exploited. However, it was not used in this study for two main reasons. First, lake temperature exhibits much lower spatiotemporal variability than Chl-a and its seasonal pattern closely follows the well documented wind seasonality. Secondly, the decision was made to focus solely on Chl-a in order to place emphasis on this variable. Another variablealso available from the Lakes_cci dataset is lake water level, which is identified in the introduction as a major threat for local populations. However, the relationship between lake level and Chl-a concentrations at the lake scale remains unclear and was not investigated within our study. It does not aim to study in detail the drivers of these dynamics, which is the subject of an additional forthcoming study. Further research would be needed to elucidate potential mechanisms underlying this interaction.
  As mentioned in this review, transparency is indeed fundamental to phytoplankton blooms and to the remote sensing estimation of phytoplankton concentrations. Available Secchi disk depth will be included to support comment on water clarity in Line 104. Given that remote sensing data pertain to surface waters, the text consistently refers to surface Chl-a when presenting the results. However, a clarification will be added in the opening paragraphs of the Results section to emphasize that the findings reflect surface conditions and that the influence of phytoplankton concentrations on the Chl-a signal decreases with depth.
  Regarding data availability, it does indeed influence the results, particularly regarding the statistical significance of observed trends. We will therefore include a comment in the discussion acknowledging the limitations of the dataset. Alongside this, the uncertainty inherent in Chl-a estimates—though not shown in the main figures to maintain clarity—is a critical factor in interpreting both absolute values and trend analyses. The Lakes_cci dataset provides uncertainty layers for Chl-a, with lines 142–143 indicating that 90% of the data have a fractional uncertainty between 39 and 60%. To enhance transparency and allow for a more nuanced assessment of the results’ robustness, we will explicitly address the impact of uncertainty on interpretation in the discussion and include supplementary maps depicting the monthly mean fractional uncertainty across Lake Tanganyika.
  
  Other comments:
  Lines 34-46: Recent citations will be added to the first paragraphs of the introduction to strengthen it.
  Regarding lines 115 to 135 and the suggestion to move them to the introduction or to support the discussion, we respectfully disagree. In the introduction, we provide a general context about Lake Tanganyika so that readers understand the significance of this ecosystem as well as the relevance of using remote sensing data to estimate phytoplankton concentrations. Section 2.1, "Study site," then presents a more detailed description of the lake’s physical and biological characteristics, explaining how these have changed over recent decades and their impact on the ecosystem.
  
  It would be possible to merge these parts into a single introduction section. However, it does not seem coherent to separate lines 115–135 from the Study site section, as this would lead readers to first encounter detailed information on the lake’s evolving physical and biological features in the introduction, only to return to more general context in the Study site section. Such an arrangement would disrupt the logical flow and clarity of the presentation. We suggest to leave this section unchanged.
  Line 144: This sentence will be changed to state that MODIS data (from 2012-2016) was not released for most lakes in the dataset, including Lake Tanganyika.
  Line 155: The DINEOF methodology requires a minimum of available pixels of 5% for interpolation. Images with low data availability typically show patches of available data, not scattered pixels all around the lake.
  
  Naithani, J., & Deleersnijder, E. (2004). Are there internal Kelvin waves in Lake Tanganyika? Geophysical Research Letters, 31(6). https://doi.org/10.1029/2003gl019156
  Naithani, J., Plisnier, P. D., & Deleersnijder, E. (2011). Possible effects of global climate change on the ecosystem of Lake Tanganyika. Hydrobiologia, 671(1), 147–163. https://doi.org/10.1007/s10750-011-0713-5
  Plisnier, P., Cocquyt, C., Cornet, Y., Poncelet, N., Nshombo, M., Ntakimazi, G., Nahimana, D., Makasa, L., & MacIntyre, S. (2023). Phytoplankton blooms and fish kills in Lake Tanganyika related to upwelling and the limnological cycle. Journal of Great Lakes Research, 49(6). https://doi.org/10.1016/j.jglr.2023.102247
  Sterckx, K., Delandmeter, P., Lambrechts, J., Deleersnijder, E., Verburg, P., & Thiery, W. (2023). The impact of seasonal variability and climate change on lake Tanganyika’s hydrodynamics. Environmental Fluid Mechanics, 23(1), 103–123. https://doi.org/10.1007/s10652-022-09908-8
  
  Citation: https://doi.org/10.5194/egusphere-2025-1326-AC1
CC2:
'Comment on egusphere-2025-1326', J-P. Descy, 17 Jun 2025

Review of egusphere-2025-1326

The work uses teledetection estimation of chla concentration in Lake Tanganyika to examine log-term (~20 y) and seasonal changes of phytoplankton biomass in Lake Tanganyika. Like in previous work based on a similar approach, it shows the spatial heterogeneity of phytoplankton biomass over this large lake, further insisting on differences between the pelagic zone and some coastal areas or the southern and northern ends of the lake.

I agree with the authors that, given this great spatial heterogeneity, field-based chlorophyll measurements at fixed sites for long-term monitoring cannot capture all changes that occurred and will occur in such a large lake, as a result of climate change and other anthropogenic influences. This aligns very well with conclusions drawn from Descy et al (2005):
“Interannual variability in weather pattern and lake hydrodynamics can be high, and only monitoring of phytoplankton composition, biomass and production over long periods of time, and at several sites to account for spatial variation, allow proper assessment of relative effects of short- and long term environmental variability on this community”.

In this context, teledetection is a great tool to examine variation of lake surface Chla, lake surface temperature (LST) and even light penetration at large spatio-temporal scale, and this paper does the job quite well for chla. Particularly interesting is the information presented in figure 6, which shows the very different seasonal patterns depending on the lake region, with contrasting behaviour between the northern part of the lake (strongly stratified during the rainy season) and the more dynamic southern part (due to lower temperature gradient and more frequent mixing).

All this is fine, however we miss a few important things here, in particular the link with the complex hydrodynamics in lake Tanganyika, on which important work has been done in the past few years (e.g. Delandmeter et al., 2018; Sterckx et al., 2023). Establishing this link might help to point out the cause for some observations, as the “blooms” (I believe the term should be used with more caution in the ms.) that occur the coastal areas. They might be the results of coastal nutrient input from upwelling caused by oscillations such as Kelvin waves, the existence of which being likely, according to Antenucci (2005).
They might also consists of periphyton growing on substrates in the littoral zones, brought into suspension by turbulence from wave action and transported offshore. And of course the littoral zone may be subjected to local nutrient inputs, from rivers and from villages on the shore. Actually, several processes can explain that chlorophyll a is often higher in the coastal zone than in the pelagic zone, although there is no compelling evidence from field data that inshore waters have sytematically higher chla than offshore waters : data can be found in Descy et al. (2006). Of course these data came from sampling fixed pelagic and coastal sites, which may not reflect the situation at larger spatio-temporal scale.

What I also miss is a proper validation of the chla concentrations determined from satellite data. Despite a good general agreement with the published chla concentration in lake Tanganyika, both from field measurements and estimates from satellite data, this needs to be done to prove that their derived satellite data are realistic and in the right range. Here the authors seem to have missed a calibration (to my knowledge the first one for this lake) between whole-lake field concentrations (from north-south cruises) and the estimates from the application of the existing algorithms at that time (Horion et al. , 2010, in RSE). Using these published data, the authors could have assessed the agreement with their own data.

Other comments

L10 “Chlorophyll-a concentration (Chl-a) is a key indicator of phytoplankton biomass and primary productivity, and thus a proxy for the health of aquatic ecosystems”. Chlorophyll a is a proxy of phytoplankton biomass and not of productivity; and it is not a proxy of ecosystem health. Ecosystem health is a broader concept, which encompasses not only trophic status, but other criteria and indicators, including foodweb structure, resilience, biodiversity ...
Productivity can be assessed from estimates of primary and secondary production over a sufficiently long period of time, e.g. a year or several years. For example a typical unit of primary productivity is g C.m^-2.y^-1. Chla is just a concentration, which can be expressed as g C.m^-3or g C.m^-2if integrated over the water column, using a C:Chla conversion factor.
Elsewhere in the ms., there is a confusion between chla concentration (phytoplankton biomass) and primary production. The authors should be aware that primary production depends not only on chlorophyll a concentration but also on light penetration and on photosynthetic capacity of the phytoplankton assemblage. They should also know that phytoplankton biomass at a given time/site results from the balance between production and losses (incl respiration, grazing, sedimentation...). So for example, it is quite possible that chla in the pelagic zone is lower than in the coastal zone because losses by grazing and sedimentation are lower in the coastal zone, assuming a same production rate in both zones.
LO 28 and elsewhere : avoid to use the term “shallow” which is used for lakes of only a few m deep. Lake Tanganyika is a deep lake, with a littoral zone (defined by light penetration down to a depth which allow the development of macrophytes and periphyton) of limited extension, sometimes only a few dozen of m. Using “coastal” or “inshore”, as opposed to pelagic and offshore may be more appropriate.

L42 and following : see comment above. Chla is an indicator of trophic status, among other indicators such as Secchi depth and macronutrient concentration. Lake ecosystem health is something different as mentioned above.

L50 Remote sensing provides a measurement of surface chlorophyll a (with no indication of distribution of phytoplankton biomass in a stratified water column); among the references cited, Horion et al. 2010, who made a first estimate of chla in Lake Tanganyika from remote sensing, is missing

L64 I suggest to replace “upwelling” by “availability” or some equivalent term, as increase of stratification does not necessarily reduce upwelling.

L 96 upwelling does not increase nutrient cycling (which depends on biological processes) but brings nutrients from deep water to the euphotic zone

L 104 “Lake Tanganyika’s water is remarkably clear and nutrient levels at the surface are generally low” References missing here.
L 105 “Phytoplankton concentration show great spatiotemporal variability depending on the nutrient availability in the euphotic zone”; As mentioned above, phytoplankton biomass depends on several processes, and it is phytoplankton growth (not biomass) which depends on nutrient availability.

L 336-337 “Most previous studies did not use remote sensing data and focused on phytoplankton concentrations near the coasts” : this is incorrect

L 347 “that a key factor differencing these two types of areas is depth, rather than simply their distance from the shore. Lake
Tanganyika could be seen as having two distinct ecosystem areas, each with its own characteristic phytoplankton
concentration levels and seasonality”. Yes, coastal waters have different food webs and vastly different biodiversity, but when it comes to phytoplankton biomass and composition, pelagic and coastal zones are very similar.

L 365 “Lake Tanganyika appears to exhibit characteristics of both lake types, depending on the depth” : if this means that the coastal zone is shallow and eutrophic whereas the pelagic zone is oligotrophic, the statement is incorrect. Lake Tanganyika is oligotrophic and the range of chla in the coastal zones remains in the oligotrophic range.

L390 I would suggest to use instead a C:Chla ratio determined for this lake, from POC – Chla regressions.

L407 Modelling climate impact on the lake hydrodynamics may help for the prediction of the outcome of different scenarios, as suggested by Sterckx et al. (2023)

L 417 “This duality suggests that Lake Tanganyika exhibits characteristics of both a shallow lake becoming more eutrophic and a deep lake becoming more oligotrophic” . See comment on L 365

“These shifts could have cascading effects throughout the lake’s trophic network, potentially altering food web dynamics and ecosystem stability” . This would rather be a bottom-up effect, as cascading suggests a top-down effect. Plus they don’t know much about the food web and its dynamics ... I suggest to be more careful, as this is an ancient lake, which has seen many changes over time, perhaps of greater importance than the current anthropogenic alterations.

Citation: https://doi.org/10.5194/egusphere-2025-1326-CC2
- AC2:
  'Reply on CC2', François Toussaint, 05 Oct 2025
  
  Thank you for your helfpful feedback, which will contribute to improving this paper.
  First, we acknowledge the relevance of the comments regarding the link between the hydrodynamics of the lake and surface Chl-a concentrations. Although a link was made in the manuscript between the hydrodynamic cycle described in Plisnier et al. (2023) and seasonal variation in surface Chl-a (lines 367–384), the discussion will indeed benefit from referencing recent studies that specifically unravel the lake’s complex hydrodynamics and their evolution under climate change, such as Delandmeter et al. (2018) and Sterckx et al. (2023). Modifications in the manuscript will be made accordingly.
  As the reviewer further points out, phytoplankton blooms can result from a range of distinct processes including coastal phenomena, such as nutrient enrichment from localized upwellings, potentially triggered by internal waves or oscillations like Kelvin waves, as suggested by Antenucci (2005). Other mechanisms are the resuspension of periphyton growing in the littoral zone and being detached by wave-induced turbulence and drifting offshore and local nutrient inputs from rivers and shoreline settlements. This has been pointed out in our results in the area near the Malagarasi River mouth for example.
  Satellite observations of Chl-a plumes drifting offshore over multiple days support the idea that some phytoplankton accumulations originate near the coasts and are advected by nearshore circulation. Disentangling these various processes using satellite-derived surface Chl-a alone, however, remains challenging. One influence is particularly evident though, and notably localized: that of rivers, as illustrated by the Chl-a hotspot observed near the mouth of the Malagarasi River, for example.
  The manuscript will be revised to better convey the idea that pelagic and coastal regions are not isolated compartments but rather form a dynamic continuum shaped by physical processes such as currents, mixing, and wave action and that mechanisms can transport biomass and nutrients across zones.
  Regarding the findings of Descy et al. (2006), we agree that their field data did not systematically show higher Chl-a concentrations in coastal compared to offshore waters. Our results are consistent with this as we do not observe a systematic difference either for all coastal regions. However, we were able to distinguish between different regions with different Chl-a seasonality and trend patterns. Some coastal and shallower regions exhibit persistently higher Chl-a concentrations and the strongest 20-year increasing trends. In contrast, offshore and deeper coastal regions show more pronounced seasonal patterns in Chl-a concentrations and generally negative 20-years trends. These spatial differences do not contradict Descy et al. (2006), but rather complement their site-based observations.
  Regarding the validation satellite-derived Chl-a concentrations, we agree that it is a key point. However, it is important to note that the Chl-a product used in this study, ESA’s Lakes_cci dataset, includes extensive algorithm development and validation steps across a large range of lakes and trophic states from around the world. Moreover, the spatial and temporal patterns we observe in Lake Tanganyika are consistent with previous studies based on both in situ and other satellite products which supports the reliability of the Chl-a estimates. While we acknowledge that direct in situ validation for this lake would strengthen the argument, such data are not available for the full time period covered in this study. We therefore rely on the consistency with prior studies and on the quality of the Lakes_cci product itself.
  Other comments:
  L10, 42, 105: We agree that chlorophyll-a concentration is a proxy for phytoplankton biomass, but not directly for primary productivity or ecosystem health as a whole. We will revise the manuscript accordingly to remove references that mistakenly associate Chl-a with primary production or overall ecosystem health. Instead, we will consistently refer to Chl-a as an indicator of phytoplankton biomass and, where appropriate, trophic status.
  L50: While the references cited here provide general groundworks for the use of optical remote sensing to estimate phytoplankton biomass, the study by Horion et al. (2010) will be added as citation as well as it specifically applied those techniques on Lake Tanganyika by developing an algorithm tailored to its optical properties.
  L28: We acknowledge the reviewer’s point regarding the potentially misleading use of the terms “shallow” or “deep” in this study. In the revised manuscript, we propose to distinguish between “shallower coastal” and “deeper pelagic and coastal’ regions rather than relying solely on distance-to-shore distinctions. This terminology helps delineate regions that showed similar patterns in both the seasonality and trends in surface Chl-a.
  L64, 96: We agree with the comment regarding line 64 and will replace “upwelling” by “availability”. Line 96 will also be reformulated to state that upwellings bring nutrients from deep waters to the euphotic zone.
  L104: We agree that a reference is needed and suggest citing the reference book on Lake Tanganyika by Coulter (1991).
  L336-337: Regarding the reviewer statement about lines 336-337, we agree to change the phrasing to “Most previous studies relied on sampling methodologies to characterize phytoplankton concentrations”.
  L: 347: Our results did indicate that some coastal areas, particularly those that are relatively shallow, do show distinct patterns of surface phytoplankton concentrations. These areas show year-round higher concentrations and less pronounced seasonal variations compared to deeper regions of the lake. We acknowledge that the dataset used does not allow to draw conclusions regarding phytoplankton community composition which our study did not attempt to characterize.
  L 417: We agree with the reviewer’s comment on line 365. The use of the terms “oligotrophic” and “eutrophic” should be revised as Chl-a levels in Lake Tanganyika, even in extreme events do not necessarily make it eutrophic.
  L407: We agree with the relevance of this point and will include a sentence in the revised manuscript to highlight the importance of modelling climate-induced hydrodynamic changes, as suggested by Sterckx et al. (2023).
  L431: We agree with the reviewer’s suggestion and will revise the sentence accordingly to reflect a more cautious interpretation.
  Antenucci, J. P. (2005). Comment on “Are there internal Kelvin waves in Lake Tanganyika” by Jaya Naithani and Eric Deleersnijder. Geophysical Research Letters, 32(22), 1–2. https://doi.org/10.1029/2005GL024403
  Coulter, G. W. (1991). Lake Tanganyika and its life. Oxford University Press.
  Delandmeter, P., Lambrechts, J., Legat, V., Vallaeys, V., Naithani, J., Thiery, W., & Deleersnijder, E. (2018). A fully consistent and conservative vertically adaptive coordinate system for SLIM 3D v0.4 with an application to the thermocline oscillations of Lake Tanganyika. Geoscientific Model Development, 11(3), 1161–1179. https://doi.org/10.5194/gmd-11-1161-2018
  Descy, J.-P., André, L., Vyverman, W., & Deleersnijder, E. (2006). Climate variability as recorded in Lake Tanganyika (CLIMLAKE). http://www.belspo.be
  Horion, S., Bergamino, N., Stenuite, S., Descy, J. P., Plisnier, P. D., Loiselle, S. A., & Cornet, Y. (2010). Optimized extraction of daily bio-optical time series derived from MODIS/Aqua imagery for Lake Tanganyika, Africa. Remote Sensing of Environment, 114(4), 781–791. https://doi.org/10.1016/j.rse.2009.11.012
  Plisnier, P., Cocquyt, C., Cornet, Y., Poncelet, N., Nshombo, M., Ntakimazi, G., Nahimana, D., Makasa, L., & MacIntyre, S. (2023). Phytoplankton blooms and fish kills in Lake Tanganyika related to upwelling and the limnological cycle. Journal of Great Lakes Research, 49(6). https://doi.org/10.1016/j.jglr.2023.102247
  Sterckx, K., Delandmeter, P., Lambrechts, J., Deleersnijder, E., Verburg, P., & Thiery, W. (2023). The impact of seasonal variability and climate change on lake Tanganyika’s hydrodynamics. Environmental Fluid Mechanics, 23(1), 103–123. https://doi.org/10.1007/s10652-022-09908-8
  
  Citation: https://doi.org/10.5194/egusphere-2025-1326-AC2
  - CC3: 'Reply on AC2', J-P. Descy, 06 Oct 2025
    
    The authors have taken into account satisfactorily my comments and remarks, and proposed changes to their text accordingly.
    In addition, I would recommend citing and commenting the following paper, which goes further in estimating primary production from remote sensing data, on a similarly long period of time:
    
    Sayers, M., Bosse, K., Fahnenstiel, G., Shuchman, R., 2020. Carbon fixation trends in eleven of the world’s largest lakes: 2003–2018. Water (Switzerland) 12, 1–16. https://doi.org/10.3390/w12123500
    
    Citation: https://doi.org/10.5194/egusphere-2025-1326-CC3
RC1:
'Comment on egusphere-2025-1326', Anonymous Referee #1, 22 Jul 2025
Abstract: This variability is influenced by the lake's hydrodynamic cycle driven by air temperature and wind seasonality (line 13). Line 371-384, there is no wind or water temperature data in the manuscript.

Dataset: The author only used ESA Climate Change Initiative Lakes dataset, which contains Chl-a from 2002 to 2022. It is important to investigate the spatial and temporal changes in Phytoplankton for such a large lake. However, in-situ measurement is also very important to validate the satellite data. As the authors have mention in the introduction, several studies focused on the specific region of the lake often nearshore. Although time span for these studies is limited, they should be used to validate the satellite result, the seasonality and the inter-annual variability. If satellite results can be validated by in-situ measurement, the result can be more convincing.

The authors give a detailed study about changes in Chl-a in Lake Tanganyika, about its seasonality, inter-annual variability etc. What is the implication for other studies?

Result section: There are two 3.2 sections. Maybe should reorganize the order of the two sections. The first 3.2 section should be moved backward and the second 3.2 section should be moved forward.

The language should be further improved. Some examples are given as below:

Line 50, delete an indicator of?
Line, 88, says ‘warm, drier’ for May to August? Then in Line 94 says ‘cooler drier season’ for May to August, which is true?
Line 119, during the dry season, near the shore?
Citation: https://doi.org/10.5194/egusphere-2025-1326-RC1
- AC3: 'Reply on RC1', François Toussaint, 05 Oct 2025
  
  Thank you for your comments.
  Abstract: Regarding the mention of wind and water temperature in the abstract, this was intended to highlight their role as drivers of Lake Tanganyika’s hydrodynamic cycle, and therefore of phytoplankton biomass. Further mentions in the discussion are intended as interpretation without conclusive causal link.
  Dataset / validation with in-situ data: We agree that linking satellite-derived Chl-a estimates with in situ measurements is important. In the revised manuscript, we will add a section in which the satellite-derived seasonality is compared to the seasonality described in previous works based on in situ measurements. This will help place our results in the context of existing field-based studies.
  Implication for other studies: Our study provides a comprehensive view of the seasonality of surface phytoplankton biomass and its changes across Lake Tanganyika over a 20-year period. By establishing a robust picture of surface phytoplankton dynamics, it offers a relevant basis for future studies on the ecology of Lake Tanganyika and fisheries-related research, including those examining the links between phytoplankton productivity, food-we processes, and fisheries yields.
  Results section numbering: We thank the reviewer for noticing the duplicated section numbers. In the final manuscript, these sections will be reorganized and renumbered accordingly.
  Language: We acknowledge the language issues raised and will revise the manuscript for clarity and style, including correcting the examples highlighted by the reviewer.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1326-AC3
RC2:
'Comment on egusphere-2025-1326', Anonymous Referee #2, 08 Oct 2025
Section 2.1 Study Site:

The current scale of the Lake Tanganyika bathymetry map is insufficient to clearly depict variations in water depth and underwater slope from the shoreline toward the central zone. This is particularly relevant for key depth intervals referenced by the author, including the coastal shallow area (<250 m, lines 220–221), regions with depths less than 170 m (line 20), and the layer where "phytoplankton biomass is located between depths from 0 to 40 m, with maxima commonly found between 0–20 m” (line 106). To improve clarity, it is recommended that several representative east–west cross-sectional depth profiles be included.
Chapter 2: Materials and Methods:

A comprehensive review suggests that water depth is a primary factor influencing chlorophyll-a (Chl-a), though other variables have not been extensively analyzed. Logically, the analysis should begin by dividing the lake into distinct depth zones, followed by partitioning pixels into grid units. Subsequent steps should involve time-series analysis of these spatial units—covering daily, seasonal, and interannual Chl-a variability—and finally, identification of seven regions with co-varying Chl-a concentrations and their distributional shifts. For instance, after Section 2.2 (Dataset), a new section titled "2.3 Water Depth Zoning and Grid Pixel Unit Division” could be introduced, illustrating unit grids and in-situ Chl-a sampling points on the bathymetric map. This would be followed by Section 2.4 (“Data Application and Interpolation Method”), detailing the interpolation of daily time-step data per pixel. Subsequent sections could cover "2.5 Analysis Methods for Daily, Seasonal, and Interannual Variability of Chlorophyll-a” and “2.6 Partition and Shifts in Chl-a Distribution”. It is recommended that this chapter explicitly outline the research framework and methodologies.
Section 2.2 Dataset:

Does Lake Tanganyika have available data for wind speed, surface water temperature, water turbidity, and in-situ Chl-a? If in-situ Chl-a data exist, have the remote sensing retrieval results been validated against them? Furthermore, the statement that "For Chl-a, 90% of the dataset had an associated fractional uncertainty between 39 and 60%” (lines 42–43) raises concerns regarding data reliability and requires further clarification.
Section 2.3 Data Interpolation Method:

From the context, the missing chlorophyll-a data include some days with completely missing data or incomplete spatial coverage of the lake, which clearly does not include data from the 2012 to 2016 period. However, what method is used to handle the data for lake areas that are not covered? It is suggested that before data interpolation, the chlorophyll data derived from remote sensing interpretation should also be validated against in-situ measurement data.
Sections 2.4, 2.5, and 2.6:

It is suggested that Sections 2.4, 2.5, and 2.6 be consolidated into a single section titled “Analysis Methods for Seasonal and Interannual Variations of Chlorophyll-a”, accompanied by corresponding formulas for calculating seasonal and interannual variability.
Section 2.7 Shifts in Chl-a Distribution: Quantile-Based Trend Analysis:

This section title is better phrased as “Partition and Shifts in Chl-a Distribution”.
Section 3.1 Seasonality of Chl-a Concentrations:

The author links Chl-a seasonality to water depth, yet Figure 1 is too small to clearly illustrate how depth and slope variations relate to Chl-a concentration. Key coastal shallow areas, such as the Gulf of Burton (line 227), are not marked—consistent with the first comment. While Figure 2 offers a general view of seasonal trends, it is recommended that the April and October/November panels be enlarged to highlight differences between coastal shallow zones and deep-water regions, as well as north–south contrasts. Alternatively, incorporating cross-sectional profiles could better illustrate regional Chl-a dynamics.
Section 3.2 Spatial Clustering for Improved Characterization of Chl-a Variability:

The analysis describes interannual and seasonal Chl-a variations across seven clusters. Several issues arise:
The clustering may obscure intra-cluster variability among pixel units, even if differences are small.

Legend colors in Figure 4 do not match the time-series curves.

In Figure 4, the seasonal variation curves of chlorophyll-a in Clusters 3–6 exhibit a single peak in October, whereas the 75th and 90th percentile daily time series for Clusters 1 and 2 show two distinct peaks—occurring in September and November, respectively. This pattern suggests frequent phytoplankton blooms in the latter clusters, with a decline in October. However, the underlying causes of this differential pattern are not explored. By contrast, the author suggests in the discussion (Lines 367–370) that a three-season framework for Chl-a dynamics emerges from remote sensing observations in pelagic regions.

Section 3.2Overall Patterns of Change in Surface Chl-a:9.The Serial Number of 3.2 is duplicated with the previous section

This section analyzes decadal trends in annual Chl-a across depth zones (10 m intervals), identifying 170 m as a threshold between positive and negative trends. However, no map compares this 170 m isobath with Chl-a trends. Additionally, the meaning of the p-values in Figure 5c is not explained.
Section 3.3Monthly Dynamics of Surface Chl-a Trends:

Only relative trend changes are presented; absolute trend magnitudes and associated p-values are lacking.
Section 3.4Shifts in Chl-a Distribution: Quantile-Based Trend Analysis:

Why is the Quantile-Based Trend Analysis from the 25th to 90th percentiles presented as a line graph rather than a bar chart for deep water and shallow water zones? Moreover, both deep water and shallow water zones also obscure the differences among pixels within the same zones.
Chapter 4 Discussion:

The discussion lacks sufficient supporting data—such as warming trends, wind speed/direction, and hydrodynamic cycles—and the accompanying analysis is inadequate.
Lines 357–360: The author mentions that apart from their shallow depths, these areas do not share similar spatial contexts: some are located near the estuaries of major rivers, others lie close to urban centers, and some are situated in neither of these settings. A common explanation for the highest Chl-a levels in these areas remains elusive. From the entire text, aren’t the areas with high chlorophyll-a values—including estuarine zones and urban center areas—all located in shallow water regions? Where exactly are the exceptional areas that are not in shallow water? What causes this?
Lines 368–370: When mentioning the seasonal variation characteristics of chlorophyll-a in the deep water area, the author stated: “While Plisnier’s hydrodynamic cycle of Lake Tanganyika is subdivided into four distinct periods—two trade wind seasons and two intermonsoon seasons—a three-season framework for Chl-a dynamics emerges from remote sensing observations, as seen in Figure 4.” However, Figure 4 indicates that except for the 7th shallow water cluster, the seasonal variations of chlorophyll-a in the other several clusters do not show results consistent with a three-season framework.
Lines 385–397: The text discusses the issue of declining primary productivity and fish yields caused by rising temperatures. However, Figure 5 reflects the long-term changes in chlorophyll a. So, what are the influencing factors of primary productivity and fish yields, and do they show a positive correlation with chlorophyll a? Moreover, the article does not cite enough data on the warming temperature trend to support this inf
Citation: https://doi.org/10.5194/egusphere-2025-1326-RC2

François Toussaint, Alice Alonso, and Marnik Vanclooster

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Short summary

We analysed 20 years of satellite data to study changes in surface phytoplankton concentration in Lake Tanganyika. Results reveal strong seasonal patterns linked to known hydrodynamics, with decreasing levels in deep areas and increases in shallow regions. These changes could alter the lake’s ecosystem and threaten the resources that millions of people rely on.


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