the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jul 2026
Large-scale atmospheric drivers of recent hydroclimatic changes in the Horn of Africa: evidence from the Awash Basin terminal lake system, Ethiopia
Abstract. The Horn of Africa, and specifically Ethiopia's Awash Basin, is experiencing accelerating hydroclimatic change, yet the mechanisms driving multi-decadal shifts in its regional water balance remain poorly understood. The terminal Afambo–Gemeri–Abhe lake system, an endorheic water body at the outlet of the Awash Basin, acts as a natural integrator of its catchment's net water balance, providing a sensitive record of regional atmospheric dynamics. This study reconstructs its hydrological history over 1985–2024 by coupling the GR2M hydrological model with a lake water-balance model, constrained by satellite observations and in situ data. The model reproduces observed lake surface area dynamics and reliably extends the record into the data-scarce pre-1998 period allowing us to relate recent lake surface area changes to large scale circulation variability and trends.
The reconstructed history reveals two superimposed signals. The first is pronounced variability across interannual to interdecadal timescales, driven by the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Subtropical Indian Ocean Dipole (SIOD). The second is a non-oscillatory, multi-decadal expansion: the lake tripled in surface area from ∼150 km2 in the 1980s to ∼450 km2 by 2024, with growth accelerating markedly after 2020. This systematic expansion reflects a reorganization of the regional water balance driven by increased July–September rainfall and changes in potential evapotranspiration. We attribute these basin-scale changes to large-scale atmospheric circulation shifts, including a strengthening of the Somali Jet, the Tropical Easterly Jet, and the Indian monsoon circulation, and a northward shift of the Intertropical Convergence Zone and of the African Easterly Jet, likely linked to amplified regional warming over the Arabian Peninsula and the Tibetan Plateau. This modelling framework provides a transferable tool for evaluating past and future hydroclimatic variability, supporting more robust water resource assessments in data-scarce regions.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-3724', Anonymous Referee #1, 24 Aug 2026
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AC1: 'Reply on RC1', Juan P. Sierra, 05 Oct 2026
Dear Reviewer,
Thank you very much for your thoughtful and constructive comments, and for taking the time to review our manuscript. We agree that the presentation would benefit from a more focused synthesis of the results, with greater emphasis on the main messages and less detail that may distract from them. We have carefully considered your suggestions and have revised the figures and accompanying text accordingly.
Below, we provide a detailed response to each of your comments and describe the changes made to the manuscript. Since the public discussion is still ongoing, the revised version incorporating these changes will be made available as the second version of the manuscript at the end of the first review round.
- Reviewer comment: "Fig 1b,c could be deleted"
Response:
We agree that retaining all four panels in the main text may distract from the main message of the figure. We have therefore moved Figures 1b–c to the Supplementary Material, while retaining Figures 1d–e in the main text. Panels (d–e) are particularly relevant to our interpretation because they show that, during the March–May (MAM) and June–September (JJAS) seasons, precipitation can exceed potential evaporation across parts of the basin, indicating periods of positive surface water balance that contribute to the replenishment of surface and groundwater resources. In contrast, panels (b–c) illustrate the seasonal water deficit during October–November and December–February and are retained in the Supplementary Material to provide the complete seasonal context and facilitate comparison with the wetter seasons. Please refer to Figure 1 in the attached document to view the new Figure 1.
- Reviewer comment: "Fig. 2a,b the annual cycle 4 m3/s and time series 1 m3/s don't match"
Response:
Thank you for pointing this out. We believe that the apparent discrepancy results from a misunderstanding of the units used in Figure 2. The discharge values shown in both panels are expressed as monthly accumulated discharge (m³ month⁻¹), rather than discharge rates in m³ s⁻¹.
In panel (a), the values represent the mean monthly accumulated discharge over the 1990–2004 period. For example, the maximum observed value in August is approximately 4.77 *10**8 m³ month⁻¹. In panel (b), individual monthly discharge values are shown, and some months have substantially larger values than the climatological monthly mean. Therefore, the magnitude of the individual values in panel (b) is not expected to be identical to that of the annual cycle in panel (a).
For clarity, we have revised and edited the figure/caption to make the units more explicit and avoid confusion between discharge rates (m³ s⁻¹) and monthly accumulated discharge (m³ month⁻¹). We hope this clarification resolves the apparent inconsistency between the two panels.
- Reviewer comment: “Fig 3 lake surface spread is interesting, but what about altimeter h eight, presumably corresponding?”
Response:
We agree that lake water-level information provides a useful complementary perspective on the evolution of the Afambo–Gemeri–Abhe lake system. In the revised manuscript, we therefore provide the corresponding lake-level information in the Supplementary Material, showing the observed radar-altimetry water levels together with the water levels simulated by the lake model. Please refer to Figure 2 in the attached document to view the new supplementary figure.
We chose to retain lake surface area as the main variable in Figure 3 because its primary purpose is to illustrate the spatial expansion of the lake system. Adding water level to the same figure would provide largely redundant information on the temporal evolution of the lake system, while making the figure more difficult to interpret. Nevertheless, the agreement between the surface-area evolution and the lake-level variations provides complementary evidence for the long-term changes in the system.
We have also clarified the role of satellite altimetry in the Supplementary Material. For Lake Abhe, HydroWeb water-surface elevation observations were combined with satellite-derived lake surface areas, SRTM topography, and in situ water-depth measurements from sediment coring sites to reconstruct the lake bathymetry. The resulting bathymetry was then used to derive the lake hypsometric relationships between water level, surface area, and water volume used in the lake model.
- Reviewer comment: “Fig 4 suggests that lakes are useful indicators of trends, rain and evap as oscillators, in (f) the pot.evap is dispersed, is there any way to say which is better?”
Response:
We agree that the larger spread among the potential evaporation estimates in Fig. 4f warrants clarification. The differences do not necessarily reflect inconsistencies in the observations, but rather the different formulations and meteorological inputs used to estimate potential evaporation. GLEAM v4.2a uses the Penman formulation and explicitly accounts for aerodynamic controls such as wind speed and vapour-pressure deficit, whereas WaterGAP3 uses a Priestley–Taylor formulation. PCR-GLOBWB estimates reference potential evaporation using either Hamon or FAO Penman–Monteith, depending on the forcing configuration, while CRU v4.09 is based on the Penman-Monteith formula with fixed wind information (climatology for the period 1961–1990). Therefore, differences among these products are expected and provide an indication of the uncertainty associated with the magnitude of potential evaporation over the Awash Basin.
We investigated whether one of these products could be considered more appropriate for the study region. There is, however, no single potential evaporation product that can be considered universally superior for the Awash Basin. A recent evaluation of 17 potential evaporation methods at five sites across Ethiopia showed substantial spatial differences in performance, with different methods performing best at different sites; notably, the Wendling method performed best at Metehara, in the Ethiopian Rift Valley, whereas Priestley–Taylor performed best at some other Ethiopian sites (Mekoya and Workneh 2024). GLEAM nevertheless provides a particularly useful basis for our analysis because it has been independently evaluated in Ethiopia. Chen et al. (2024) compared several precipitation and evapotranspiration datasets for closing the water balance over Ethiopia and found that the combination including GLEAM evapotranspiration provided the best-performing dataset combination for estimating surface runoff. Their results also showed a relatively small water-balance closure error of 46.5 mm yr⁻¹ (5.6%) at the national scale.
Given these results, we consider GLEAM a well-supported choice for representing evaporative demand in the present study, particularly given the scarcity of in-situ observations in Ethiopia. However, because these evaluations do not demonstrate that GLEAM is universally superior across the entire Awash Basin, and because observational constraints remain particularly strong in the lower Awash and Afar Depression, we retain the multi-product ensemble in Fig. 4f rather than treating GLEAM as a definitive reference.
Importantly, despite differences in absolute magnitude, the potential evaporation products show broadly consistent interannual variability. We therefore interpret the temporal variability in evaporative demand as more robust than its absolute magnitude. We have clarified this point in the revised manuscript and retain the multi-product comparison to explicitly represent the uncertainty associated with potential evaporation estimates.
References:
Mekoya, A., Workneh, M. Comparison of potential evapotranspiration methods in Ethiopia. Discov Atmos 2, 1 (2024). https://doi.org/10.1007/s44292-024-00003-9
Chen, Xuelong and Workneh, Habtamu Tsegaye and Ma, Yaoming and Tegegne, Eyale Bayable and Kassahun, Asnake (2024) Quantifying Runoff and Total River Discharge in Ethiopia Based on Water Balance Using Satellite and Reanalysis Datasets. Available at SSRN: https://ssrn.com/abstract=5010515 or http://dx.doi.org/10.2139/ssrn.5010515
- Reviewer comment: “Fig 6 clearly PDO influence, not ENSO - IOD,”
Response:
We thank the reviewer for raising this point. We have reconsidered the interpretation of Figure 6 and agree that the PDO signal is particularly clear in Figure 6b. However, we consider that the two panels provide complementary information on different aspects of the large-scale climate variability associated with Lake Abhe and the Awash Basin, and that the ENSO and SIOD signals should not be excluded.
In particular, Figure 6a does not represent a correlation with a PDO, ENSO, or IOD index. It shows the spatial correlation between JAS anomalies of 500-hPa vertical velocity (ω500) averaged over the Awash Basin and JAS ω500 anomalies throughout the tropical belt. The purpose of this analysis is therefore to identify large-scale atmospheric circulation patterns associated with anomalous vertical motion over the Awash Basin, rather than to reproduce the canonical SST signature of a particular climate mode.
The significant negative correlations over the central and eastern equatorial Pacific, together with opposite-sign correlations over the western Pacific/Maritime Continent, indicate an east–west contrast in tropical vertical motion consistent with variations in the Walker circulation. We recognize that the Pacific pattern in Figure 6a does not correspond spatially to the canonical El Niño SST pattern. However, this is not necessarily expected because the variable analysed is 500-hPa vertical velocity rather than SST. ENSO-related atmospheric circulation anomalies can have a spatial structure that differs from that of the associated SST anomalies. Wang (2002), for example, explicitly examined ENSO-related 500-hPa vertical velocity and showed distinct anomalous ascending and descending branches over the eastern and western tropical Pacific, together with changes in the Walker and Hadley circulation cells. The study also demonstrated that these atmospheric circulation anomalies evolve differently through the different phases of El Niño.
Although the negative correlations over the tropical Pacific in Figure 6a are not confined to the equatorial region in the same way as the canonical Niño-3 SST anomaly, we therefore consider the pattern to be consistent with an ENSO-related atmospheric circulation response rather than evidence against an ENSO influence. In particular, the use of JAS 500-hPa vertical velocity emphasizes the atmospheric circulation response to tropical Pacific variability rather than the location of the underlying SST anomalies.
For Figure 6b, we agree with the reviewer that the PDO is the most clearly expressed Pacific signal in the spatial correlation with Lake Abhe surface area. We have therefore revised the text to explicitly acknowledge the prominent role of PDO variability rather than interpreting the Pacific pattern primarily as an ENSO signature. At the same time, Figure 6b also exhibits a dipolar structure over the southern Indian Ocean, with negative correlations in the southwestern tropical Indian Ocean and positive correlations farther to the southeast. This pattern is consistent with variability associated with the Subtropical Indian Ocean Dipole (SIOD) and is distinct from the conventional tropical Indian Ocean Dipole (IOD).
To further distinguish the relative contributions of these climate modes, we complement the spatial correlation analysis in Figure 6 with the time-series analysis presented in Figure 7. Figure 7 compares the detrended annual Lake Abhe level anomalies with the PDO, El Niño, and SIOD indices, providing a more direct assessment of their temporal covariability.
References:
Wang, C., 2002: Atmospheric Circulation Cells Associated with the El Niño–Southern Oscillation. J. Climate, 15, 399–419, https://doi.org/10.1175/1520-0442(2002)015<0399:ACCAWT>2.0.CO;2.
- Reviewer comment: “Fig 7 suggests the lake time series has 2 degrees of freedom, eg. one cycle”
Response:
We thank the reviewer for raising this important point. We agree that the detrended Lake Abhe surface-area series exhibits substantial low-frequency persistence, with only a limited number of broad excursions over the study period. Consequently, the nominal number of annual observations does not necessarily represent the number of independent degrees of freedom, and correlation significance tests based on the assumption of independent samples may overestimate the statistical significance of the relationships shown in Fig. 7.
We will therefore reassess the significance of the correlations between Lake Abhe surface area and the PDO, El Niño 4, and SIOD indices by accounting for serial autocorrelation and estimating the effective number of degrees of freedom, following the approach of Zwiers and von Storch (1995). The corresponding correlation significance levels will then be recalculated using the effective sample size rather than the nominal length of the time series.
More importantly, we agree that the limited number of independent low-frequency fluctuations constrains the strength of any statistical inference that can be drawn from these correlations. We will therefore revise the text to interpret the relationships shown in Fig. 7 more cautiously. Rather than presenting the correlations as definitive evidence of a causal influence of these modes of climate variability on Lake Abhe, we will describe them as temporal covariability that is consistent with the proposed large-scale atmospheric mechanisms and mechanism previously reported in the literature, while acknowledging the limited effective degrees of freedom of the relatively short record.
References:
Zwiers, F. W., & Von Storch, H. (1995). Taking serial correlation into account in tests of the mean. Journal of Climate, 8(2), 336-351.
- Reviewer comment: “Fig 8 onward - delete climatology, too much information is diluting the message”
Response:
We thank the reviewer for this suggestion. We agree that the figures from Fig. 8 onward contain a substantial amount of information and that the presentation should be streamlined to focus more clearly on the main atmospheric mechanisms.
We nevertheless consider the climatological circulation to be useful for interpreting the trends, as it provides the reference state against which the changes in moisture transport and vertical motion can be evaluated. Rather than removing the climatology entirely, we have therefore substantially simplified Fig. 8 to retain this information in a more concise form. Please refer to Figure 3 in the attached document to see the new Figure 8.
Specifically, we have reduced Fig. 8 from four panels to two. The climatological vertically integrated moisture transport is now shown as vectors, while the 500-hPa vertical velocity climatology is displayed as the background field on the same map. We have removed the climatological moisture-convergence field, which was less essential for the main message. The same approach is applied to the trend fields: the trends in vertically integrated moisture transport are shown as vectors, while the trends in 500-hPa vertical velocity are displayed as the background field. This reduces the amount of information presented while retaining the atmospheric circulation features necessary to interpret the spatial pattern of the trends.
We believe this revised presentation addresses the reviewer’s concern about excessive information while preserving the climatological reference needed to interpret the changes in atmospheric circulation and moisture transport.
- Reviewer comment: “Fig 9 wind arrows are too close horizontally, need to spread (sub-sample horizontally) and magnify vertical motion, shouldn't a Walker cell be analyzed E-W (not tilted?), and extend the analysis further east to Indian Ocean”
Response:
We thank the reviewer for this helpful suggestion. We agree that the wind vectors in Fig. 9 are too closely spaced horizontally and that the vertical motion is not sufficiently visible in the current presentation. We have therefore revised the figure by horizontally sub-sampling the wind vectors to improve their readability and by increasing the scaling of the vertical-motion component.
Regarding the orientation of the cross-section, we initially chose a tilted E–W section because our objective was to characterize the regional atmospheric circulation and moisture-transport pathways affecting the Awash Basin. The cross-section was defined to follow the main direction of the low-level moisture-transport streamlines between 1000 and 700 hPa and to pass over the Awash Basin (please refer to Figure 4 in the attached document). Thus, its original orientation was intended to capture the dominant regional circulation associated with moisture transport toward the basin, rather than to represent an idealized zonal cross-section of the equatorial Walker circulation.
Following the reviewer's suggestion, we have revised the analysis in the new version of the manuscript using a west–east-oriented cross-section and extended it eastward over the Indian Ocean. This configuration allows us to better assess the continuity of the atmospheric circulation and moisture-transport patterns between the Indian Ocean and the Awash Basin, while providing a more appropriate framework for examining the zonal circulation associated with the Walker-type circulation. The corresponding figure (please refer to Figure 5 in the attached document to see the new Figure 9) and discussion have been updated accordingly.
- Reviewer comment: “Fig 10 trend of 200 wind useful, the rest are ambiguous and could be deleted”
Response:
We thank the reviewer for this suggestion and agree that Fig. 10 can be substantially simplified. In particular, we agree that the geopotential-height trends shown in Fig. 10b do not provide a sufficiently clear or robust signal to support pressure changes discussed in the text related in the text regarding trends in the Somali Jet. We will therefore remove the panels 10a–b.
We also agree that the wind trends shown in Fig. 10b provide information that is largely consistent with the changes in vertically integrated moisture transport presented in Fig. 8 and therefore do not add sufficient independent information to justify retaining these panels.
We will retain the Tropical Easterly Jet panels (Fig. 10c–d), as they provide a distinct characterization of the upper-tropospheric circulation and its changes at 200 hPa.
For the African Easterly Jet, however, we consider that the climatology and trends shown in Fig. 10e–f provide relevant and sufficiently clear information for the interpretation of the manuscript. In particular, the significant trends in 700-hPa winds and temperature document changes in the structure and intensity of the AEJ that are directly relevant to the atmospheric mechanisms investigated in the subsequent analysis (Fig. 13). Figure 10e–f therefore provides the regional-scale context for the more detailed analysis presented in Fig. 13 and is not intended to duplicate it.
Accordingly, the revised Fig. 10 will be reduced from six to four panels (please refer to Figure 6 in the attached document to see the new Figure 10), retaining only the TEJ and AEJ climatologies and trends. We believe that this revision addresses the reviewer’s concern about ambiguous and redundant information while preserving the circulation features that are directly relevant to the main atmospheric mechanisms discussed in the manuscript.
- Reviewer comment: “Fig 11 seems off-the-point”
Response:
We thank the reviewer for this comment. We agree that the original version of Fig. 11 did not make sufficiently clear how the warming over the Arabian Peninsula and Tibetan Plateau was related to the circulation changes discussed in the manuscript.
The purpose of this analysis was to provide physical context for the strengthening trends identified in the Somali Jet and Tropical Easterly Jet by documenting the pronounced JAS warming over the Arabian Peninsula and Tibetan Plateau. To better establish this connection, we have complemented the temperature-trend analysis with an analysis of the relationship between JAS 500-hPa vertical motion over the Awash Basin and 2-m air temperature. In this analysis, vertical velocity was multiplied by −1 so that positive values represent ascending motion. The resulting correlation pattern shows significant positive correlations between temperature over the Arabian Peninsula and Tibetan Plateau and ascending motion over the Awash region, providing a more direct indication of the link between regional warming and the enhanced convection over the basin.
In response to the reviewer's comment, we have therefore moved this analysis from the main manuscript to the Supplementary Material and revised the corresponding supplementary figure to include the new correlation analysis (please refer to Figure 7 in the attached document to see the new supplementary figure). The supplementary figure now combines the spatial distribution of JAS temperature trends with the temperature–vertical-motion correlations, providing a clearer physical basis for considering the Arabian Peninsula and Tibetan Plateau in the interpretation of the circulation changes. The main manuscript has been streamlined accordingly and now contains only a concise discussion of this relationship, with a reference to the supplementary figure.
- Reviewer comment: “Fig 12 N-S section should extend northward to catch the Red Sea airflow, and wind arrows need to spread as noted above.”
Response:
We thank the reviewer for this helpful suggestion. We agree that extending the N–S cross-section northward will allow us to better capture the atmospheric circulation and low-level airflow associated with the Red Sea and its potential contribution to the regional moisture transport.
We will therefore extend the northern boundary of the cross-section to include the Red Sea region. In addition, following the reviewer’s previous suggestion for Fig. 9, we will sub-sample the wind vectors horizontally to increase the spacing between arrows and improve their readability, and we will increase the scaling of the vertical-motion component to make the vertical circulation more clearly visible. Please refer to Figure 8 in the attached document to see the new Figure 12.
- Reviewer comment: “Fig 14 fine, but mid-troposphere and diabatic heating occur much higher than 700, more like 400-500 hPa.”
We agree that the mid-tropospheric circulation and diabatic heating relevant to the processes discussed in Fig. 14 are better represented at higher levels than 700 hPa. We will therefore revise the figure by shifting the analysis to the 400–500 hPa levels, following the reviewer’s suggestion, and will adjust the accompanying discussion accordingly.
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AC1: 'Reply on RC1', Juan P. Sierra, 05 Oct 2026
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RC2: 'Comment on egusphere-2026-3724', Anonymous Referee #2, 26 Aug 2026
Review of “Large-scale atmospheric drivers of recent hydroclimatic changes in the Horn of Africa: evidence from the Awash Basin terminal lake system, Ethiopia” by Sierra et al.
General comments
The manuscript “Large-scale atmospheric drivers of recent hydroclimatic changes in the Horn of Africa: evidence from the Awash Basin terminal lake system, Ethiopia” by Sierra et al., addresses a timely and relevant topic concerning hydroclimatic variability and lake-level changes in the Awash Basin. One of its main strengths is the combination of multiple datasets, methodological approaches, and perspectives, which allows the system to be examined from several complementary angles. I found this broad approach potentially valuable and think the study could provide useful insights into the climatic and hydrological controls on recent changes in the basin.
At present, however, I have several substantial concerns regarding both the methodological basis of the analysis and the way the results are presented. Most importantly, the hydrological model plays a central role in the paper and underpins several of its main conclusions, yet its formulation, calibration, validation, parameter selection, sensitivity, and performance are described only very briefly. This makes it difficult to assess how much confidence can be placed in the model results, particularly given the apparent change in model performance around the large lake-level rise in 2020. Related assumptions concerning evaporation, groundwater and transmission losses, precipitation biases, and the treatment of the atmospheric water budget also require stronger justification and, where possible, explicit evaluation of their associated uncertainties and residuals.
More generally, the manuscript currently contains a very large number of datasets, analyses, and relatively short results subsections. While this diversity is one of the study's strengths, it also makes the main scientific narrative difficult to follow. In several places, results are presented only briefly before the text moves directly into interpretation and comparison with previous studies, making it difficult to distinguish clearly between what the analyses show and how the authors interpret those findings. This is particularly noticeable in the later parts of Sect. 3 and across Figs. 8–14. I therefore encourage the authors to revise the presentation: more clearly distinguish results from discussion even if the two remain within a combined section, and consider moving some of the supporting analyses and figures to the Supplementary Material. Doing so would help bring the central findings and the evidence supporting them much more clearly to the foreground.
Overall, I think the manuscript makes a very interesting contribution, but substantial revision is needed before the robustness of its main conclusions can be properly assessed.
Major comments
- 2.2.2: This section lacks details, and assumes both that readers are familiar with the model, and that Figure 2 is enough to trust the model. I disagree with these two: Can you elaborate on the model? What are the two parameters? How were they chosen? Was the model calibrated (besides the factor 2 for the high flows)? Validated? How was the factor 2 for the high flows decided? Were there any sensitivity tests for the model parameters and for the correction of the high flows? How were the other precipitation datasets used in the model? What are the metrics used for the calibration and validation and how do you define a success of the model? What are the calibration objectives (match total volumes, high flows, specific peaks, etc.)? How is the model performance over the gauges for which it was not calibrated? Does this matter for your study? L177: What do you mean by “bias-correction”? How come there is still a positive bias after it?
- L192–197: Can you think of a reason why GLEAM should perform OK for the other lakes but not for Lake Abhe? Are there other PET estimates for the region in contrast with GLEAM? In that case, why should we trust it in other places? Can the model mismatch come from a different factor rather than evaporation only?
- Groundwater: This is mentioned as a variable in Eq. 1 but later said to be neglected. Can you reinforce the fact it is negligible? How about the sandy river bottoms in the lower basin next to Lake Abhe? This factor can be super important in arid, sandy regions (Knighton and Nanson, 1994; Morin et al., 2009).
- 3.1: There seems to be a very big discrepancy between the model performance before the big 2020 rise and after it. This odd behaviour needs to be clarified by both describing the model validation process, and finding the reasons for this discrepancy.
- One of the strengths of this manuscript is the combination of multiple datasets, methodological approaches, and perspectives, which allows the problem to be examined from several complementary angles. At the same time, however, the large number of datasets, methods, and relatively short results subsections makes the overall flow of the paper difficult to follow. In particular, Figs. 8–12 are covered within only a few sentences, and it is often unclear where the presentation of results ends and the discussion begins. Please make a clearer distinction between the two, even if Results and Discussion remain combined. The reader should be able to distinguish clearly between what was found and the authors’ interpretation of what these findings mean, their possible causes, and how they relate to previous studies. I also suggest considering whether all of Figs. 8–14 are needed in the main text, particularly as many of their main messages are ultimately synthesised in Fig. 14. Some of these figures could perhaps be moved to the Supplementary Material/Appendix, while relevant background material from the opening paragraphs of these subsections could instead be incorporated into the Introduction. This is particularly relevant for Sect. 3.3, where the results themselves are described only very briefly before the text moves into an extended discussion. It may therefore be clearer to treat much of this section explicitly as Discussion, while summarising the key results more concisely and presenting only the main findings in one or two figures.
Specific comments
L26: “have” should be “may have / may impose” or be supported by a reference.
L32: What do you mean by “recurrent droughts”? That both the short and long rains are low, or that there are two consecutive drought years?
L33: 80% of the annual mean – is this for the entire country or the inner parts? These numbers are in contrast to those shown by Palmer et al. (2023). I think the clarification is done in L64, where the same numbers are stated for only the Awash basin. Please reconcile these two.
L97: One of the things that Dinku et al. (2018) mention is the bias CHIRPS data have over dry (and in some cases mountainous) regions. Moreover, Dinku et al. (2011) specifically describe the problem of below cloud rainfall evaporation in dry regions. Finally, GLEAM uses MSWEP precipitation as input. This means that by using other precipitation datasets you also inherit some error in the ET coming from the precipitation product used in GLEAM. Please address these issues.
L99: “TRMM 3B42 V7” – do you mean IMERG V7?
L118–120: Are there temperature discrepancies when using CRU data for mean T and ERA5 for T? Why do separate these into two datasets?
Sect. 2.1.7: Please explain: (1) How the Landsat-based extension (2021–2025) works; (2) what’s the reliability of the data from Zhao et al. (2022), which are based on GSWD, to the region; (3) what is the period of the HydroWeb span, and how reliable it is. If it does not, what did you do for the previous period? (4) what HydroWeb product was used?
Sect. 2.2.1: This section seems to resolve one of the problems solved by Zhao et al. (2022), which is one of the input sources. Isn’t it? Please explain.
Appendix: Do a and b correspond to x and y in the A1–A5 eqs.? Please make this clearer. Also, these equations assume no transmission losses. Can this be justified? How about irrigation abstractions along the rivers?
Fig. S4 and hypsometric curves: The figure is not clear enough – what are the Altimetry radar contours? Can you state their elevations? Elevation contours seem unphysical; consider either smoothing them, or using a different approach, e.g., the one from Armon et al. (2020). Can you validate the hypsometry using external data (e.g., ICESat-2)?
Sect. 2.3.2: Why don’t you use GLEAM to assess ET here? Is this consistent with your use in the hydrological model?
Why not using surface pressure to top of the model (or if it’s easier, 200 hPa)? I’m asking because in large parts of the region the surface is well above the 1000 hPa line. Do you take the lowermost layer or what? Additionally, given that the datasets are not equal, I would not expect a water balance closure. Can you quantify the residual using the different datasets?
Sect. 2.3.3: Given your precipitation-based ITCZ identification, please consider replacing the term ITCZ with a more appropriate term. Please see discussions in: Nicholson (2009; 2018).
L263–264: How can you tell this is the driver? If that’s the case, why isn’t it the same across the other parts of the time series?
L280–282: I’m not sure I can see this. Excluding the period of 1985–1990, they seem to be roughly static. Can you explain how you got to this conclusion? The same holds for the conclusion in 285–287. Please quantify/explain.
L289–291: Are these trends similar to gauge-based trends? How about other dataset? This question rises whenever a long-term satellite product is used, because sensors and satellites are being replaced, and it is hard to assess the validity of these trends.
L299: Can you explain what is this warming? Isn’t it also the human-induced warming?
L316–327: I suggest avoiding the interpretation of monthly mean 500-hPa vertical velocity as a direct proxy for convective activity. At ERA5 resolution, and particularly after monthly averaging, w500 primarily represents large-scale mean ascent/subsidence rather than convective updraughts. Indeed, the stated motivation for monthly averaging: to suppress short-lived convective variability, appears inconsistent with using the resulting field to characterise convection itself. I suggest describing this variable as an indicator of large-scale vertical motion or the large-scale dynamical environment associated with convection, unless its relationship with an independent measure of convective activity is demonstrated.
Fig. 6: Please add the basin shape to this figure. With regards to Fig. 6b, I think detrending is necessary, as we expect trends (in both datasets) to have an impact on this result.
Fig. 7: Why Lake Abhe only? Why not showing the observed lake time series too?
L356: How do you define the “boreal summer” is it the same as your long-rains definition?
L412: What is the meaning of “enhancing kinetic energy”? Shouldn’t the interaction with topography reduce the speed and kinetic energy of the flow?
Fig. 12e: Can you please add the 0-precipitation trend line? If all values are positive, you could put it in the lowermost part of the axis, but otherwise it’s hard to distinguish negative from positive trends.
L434: How can you tell that this increase in specific humidity comes from increased orographic precipitation? Can it also be there because of advection?
Sect. 3.4.2: The first two paragraph seem to be related to Sect. 3.4.1. Can you please put them together?
All figures presenting maps from -25 to 25: Consider reducing the latitudinal span of these figures. Perhaps 40W to 90E or something like this.
Fig. 13: Can you add the Sahara and Sahel rectangles related to Fig. 13b to one of the maps in this figure? Can you add political borders to panel c and d or zoom out a bit so its easier to read these maps? And the river catchment as well?
L484: I’m not sure I understand what the tool is. Is the problem stated common throughout the globe for pre-1998 data?
Technical corrections
L19: “region” should probably be “regions”.
Supplementary Material references are all corrupted. Please correct this.
L354: “Sect. X” should be corrected.
Fig. 8: Please make sure all colour axes are symmetric with respect to 0. Please make sure to do the same for all the corresponding figures.
L377: “how long term trends” seems to be missing “the” or “these”.
Fig. 9c: Are you sure about the *10^3 units? Should it be 10^-3?
L435: Missing space after “12e).”
L456: Missing reference (“?”).
References
Armon, M., Dente, E., Shmilovitz, Y., Mushkin, A., Cohen, T. J., Morin, E., and Enzel, Y.: Determining Bathymetry of Shallow and Ephemeral Desert Lakes Using Satellite Imagery and Altimetry, Geophys. Res. Lett., 47, 1–9, https://doi.org/10.1029/2020GL087367, 2020.
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Citation: https://doi.org/10.5194/egusphere-2026-3724-RC2 - RC3: 'Comment on egusphere-2026-3724', Anonymous Referee #3, 07 Oct 2026
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EC1: 'Comment on egusphere-2026-3724', Shira Raveh-Rubin, 15 Oct 2026
Dear Juan Pablo Sierra and coauthors,
As you can see, we have received in-depth reviews of your manuscript. All reviewers agree that the work needs more focus and evidence-based text on selected topics. I encourage you to submit a substantially revised manuscript in line with the reviewers' suggestions, and I look forward to then reconsider your work for publication in WCD.
Best regards,
Shira
Citation: https://doi.org/10.5194/egusphere-2026-3724-EC1
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The paper presents an interesting work, but needs some guidance to improve the presentation. There is too much going on, so a need to consolidate. This reviewer will focus on the results via the figures in sequence. Fig 1b,c could be deleted, Fig 2a,b the annual cycle 4m3/s and time series 1m3/s don't match, Fig 3 lake surface spread is interesting, but what about altimeter height, presumably corresponding? Fig 4 suggests that lakes are useful indicators of trends, rain and evap as oscillators, in (f) the pot.evap is dispersed, is there any way to say which is better? Fig 5 fine, Fig 6 clearly PDO influence, not ENSO - IOD, Fig 7 suggests the lake time series has 2 degrees of freedom, eg. one cycle, Fig 8 onward - delete climatology, too much information is diluting the message, Fig 9 wind arrows are too close horizontally, need to spread (sub-sample horizontally) and magnify vertical motion, shouldn't a Walker cell be analyzed E-W (not tilted?), and extend the analysis further east to Indian Ocean, Fig 10 trend of 200 wind useful, the rest are ambiguous and could be deleted, Fig 11 seems off-the-point, Fig 12 N-S section should extend northward to catch the Red Sea airflow, and wind arrows need to spread as noted above, Fig 13 fine, Fig 14 fine, but mid-troposphere and diabatic heating occur much higher than 700, more like 400-500 hPa.
With these changes in the results, naturally the supporting text can be focused and distractions can be eliminated to have a clear message. A useful reference might be: doi.org/10.3390/cli13100215