the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Sep 2025
Soil moisture droughts in Belgium during 2011–2020 were the worst in five decades
Abstract. In recent years, Belgium has experienced a sequence of intense droughts with wide-ranging impacts across multiple sectors. Determining whether these events are unprecedented or within natural variability requires indicators that properly diagnose drought. Root-zone soil moisture is a suitable indicator because it integrates meteorological forcings with land-surface processes. In Belgium, however, operational monitoring relies mainly on precipitation-based indices and lacks long-term in-situ soil-moisture observations, leaving uncertainty about whether these indices capture the persistence of root-zone drought. To address this gap, we reconstructed daily root-zone soil-moisture dynamics over Belgium for 1970–2020 using the mesoscale Hydrologic Model (mHM), placing recent droughts in historical context and evaluating the adequacy of precipitation-based indicators for representing drought conditions. Our analysis shows that droughts in 2011–2020 were unprecedented in both duration and severity over the past five decades. Between 2011 and 2020, the country experienced a cumulative three years of drought (non-consecutive), representing 30 % of the decade, more than double the cumulative duration in each decade from 1981–2010 and about 1.5 times that of 1971–1980. We further find that the Standardized Precipitation–Evapotranspiration Index (SPEI), currently used operationally as a proxy for agricultural droughts in Belgium, underestimates the persistence of root zone droughts because it does not explicitly account for land-surface memory. Thus, by including soil moisture monitoring in drought assessment, residual stresses on agriculture and subsurface water, which can persist long after meteorological conditions have normalized can still be detected. This gives decision-makers a more realistic understanding of droughts and how to respond proportionately.
Competing interests: At least one of the (co-)authors is a member of the editorial board of 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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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Status: closed
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CC1: 'Comment on egusphere-2025-4526', Heye Bogena, 26 Sep 2025
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AC1: 'Reply on CC1', Katoria Lesaalon Lekarkar, 06 Oct 2025
Dear Heye,
We thank you for pointing out omission of the proper citation of the ISMN data. We have now updated our manuscript with the proper acknowledgements of the individual data providers according to the instructions in the README file which accompanies the data upon download. Since some providers (e.g., BFG_Nw and ORACLE) do not have a DOI which we can use to cite the data, we have acknowledged them in the Acknowledgements section of the manuscript using the details provided in the README file.
Below is an extract of our updated manuscript referencing the ISMN data, with the citations included.Without long-term in situ soil moisture within Belgium to validate the soil moisture output of mHM, we expanded the model domain to cover parts of France, Germany and The Netherlands where soil moisture observations are available from the International Soil Moisture Network (ISMN) (Dorigo et al., 2021). From the ISMN, we used data from the following networks: COSMOS (Zreda et al., 2008), GROW (Xaver et al., 2020), TERENO (Bogena et al., 2018), BFG_Nw and ORACLE, all shown in Figure 2.
...Acknowledgements
We acknowledge the work of Jens Wilhelmi (BFG_Nw network) for providing data in support of the International Soil Moisture Network. We also acknowledge the work of Arnaud Blanchouin and the ORACLE team of the Institut national de recherche en sciences et technologies pour l”environment et l”agriculture, France in support of the ISMN.
Citation: https://doi.org/10.5194/egusphere-2025-4526-AC1
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AC1: 'Reply on CC1', Katoria Lesaalon Lekarkar, 06 Oct 2025
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RC1: 'Comment on egusphere-2025-4526', Anonymous Referee #1, 30 Oct 2025
The manuscript reconstructs multi-decadal root-zone soil moisture over Belgium with mHM and characterizes drought events using an SMI-based framework, comparing them with precipitation-based indicators (SPEI-1/3). The central finding—that 2011–2020 is the driest decade since at least 1971—is relevant for Belgian drought monitoring and well aligned with broader European trends.
However, the presentation is currently hard to follow due to (i) too many metrics without a clear hierarchy for ranking drought severity. (ii) Several methodological elements (MPR, KDE→SMI percentiles, Fisher-z, NSE definition, event splitting/merging rules) need one-line clarifications so readers can reproduce and interpret results.
1. The title can be read as if only 2011–2020 is analyzed, yet the study reconstructs 1970–2020 and concludes that 2011–2020 is the driest decade of the five. Please revise the title.
2. Provide a simple, explicit severity ranking protocol. At present the Results toggle between TDM, SMI≤τ area, exposure months, peak area, duration, SPEI-1/3, etc., without a decision rule. Readers cannot tell which event is “most severe.” Please state a clear hierarchy.
Add a Table listing the top events with: TDM, peak area (%), duration, exceptional-class exposure.
Annotate TDM values directly in Fig. 5 and state in the caption which tie-breaker decided final ranks when two events are similar.
3. Lines 363-365, is 2016–2017 or 1975–1977 drought bigger? And based on which indicators?
Lines 411-414, based on drought persistence, 2011-2020 is the biggest one.
4. Lines 365 and 367, when you talk about area percentage, provide the figure reference.
5. Lines 368–372 discuss 2022–2023, but your decadal analysis ends in 2020. Please remove or move to Discussion/SI with an explicit caveat.
Also line 341 says Fig. 5 covers 1970–2023, but the figure appears to show 1970–2020—please make the figure and caption consistent with the text.
6. Add a short description about each subsection at the start of Results. Two–three sentences will prevent readers from getting lost.
7. Lines 196-199, explain why resolutions differ.
8. Define NSE on first use (Results 3.1.2). Give the range and interpretation (≈1 perfect; ≈0 equals mean-flow benchmark; <0 worse than mean).
9. Lines 470-475, restate the minimum overlap area rule used to merge adjacent months into one multi-temporal event. Or how do you define duration. This clarifies whether a brief wet interlude (e.g., March–April 2017) splits or does not split an event.
10. MPR, what is the full name?
11. Terminology clarity. The manuscript uses three different concepts that contain the word “calibration”:
(i) a 5-year warm-up: is it 1965–1969, if yes, why exclude 1970 as a calibration year for drought analysis?
(ii) excluding 1970 as a calibration year for drought analysis when forming decades (hence using 1971–1980),
(iii) streamflow parameter calibration (2000–2023) vs validation (1970–1999).
Please clarify these three meanings to avoid confusion.
Citation: https://doi.org/10.5194/egusphere-2025-4526-RC1 -
AC2: 'Reply on RC1', Katoria Lesaalon Lekarkar, 30 Oct 2025
Dear reviewer,
Thank you for your constructive feedback and suggestions on the manuscript.
We will start addressing them within the shortest time possible and share our responses to each of the queries raised.
With best wishes,
Katoria Lekarkar, on behalf of all authorsCitation: https://doi.org/10.5194/egusphere-2025-4526-AC2 -
AC4: 'Reply on RC1', Katoria Lesaalon Lekarkar, 16 Dec 2025
Dear,
Thank you for the time and effort devoted to reviewing our manuscript.
The comments were constructive and will no doubt improve the clarity and quality of the manuscript.
We have responded to all comments in the attached document.
With best wishes,
Katoria Lekarkar, on behalf of all the authors.
-
AC2: 'Reply on RC1', Katoria Lesaalon Lekarkar, 30 Oct 2025
-
RC2: 'Comment on egusphere-2025-4526', Anonymous Referee #2, 07 Dec 2025
Overall assessment
This study analyzes the root-zone soil moisture dynamics in Belgium from 1970 to 2020, focusing on the severity and persistence of droughts during the 2011-2020 period. It highlights the unprecedented nature of these droughts and evaluates the limitations of precipitation-based drought indices (such as SPEI) in drought assessments. The paper proposes using root-zone soil moisture as a more effective drought monitoring indicator and underscores the increasing frequency and persistence of drought events in the context of climate change. The work is valuable and substantial, but its scientific novelty is limited, and some methodological and analytical aspects need further clarification or strengthening.
Specifically, the manuscript currently lacks critical quantitative evidence to support two core claims: (1) that root-zone soil moisture provides added operational value over precipitation-based indices (SPEI), and (2) that the chosen reconstruction approach (mHM) is preferable to widely used soil-moisture products (e.g., ERA5-Land). I recommend the authors (a) perform an event-based contingency analysis comparing mHM-RZSM and SPEI drought events (onset, termination, duration, severity) and report hit/miss/false-alarm statistics, and (b) provide a direct inter-comparison with at least one widely used soil-moisture product to quantify time-series agreement and event-detection differences. Detailed comments follow.
Major comments:
1. Please clarify the definition of the root-zone layer used in this study (0–0.5 m). Why was this depth chosen, and how representative is it across different vegetation types and land-cover conditions in Belgium? Considering the variability of underlying surfaces could help assess the robustness of the results.
2. The Introduction successfully establishes the severity of drought in Belgium and correctly identifies the scientific gap regarding the limitations of precipitation-based indices. However, the section's structure and balance require revision to maximize clarity and scientific impact.
- The lengthy, detail-heavy descriptions of the 2011, 2018–2019, and 2022 droughts (Lines 46–74) read like an event chronicle. This narrative must be significantly condensed to focus only on the key messages that motivate the need for a long-term assessment, ensuring the scientific gap is presented more prominently.
- Conceptual Distinctions: The discussion on drought monitoring (Lines 79–103) should be enhanced by more explicitly distinguishing meteorological/ hydrological drought from agricultural drought. This includes clarifying why traditional indices like SPEI are limited and emphasizing the superior conceptual role of root-zone soil moisture (RZSM) for capturing plant water stress.
- The final paragraph (Lines 104–116) prematurely introduces technical specifics (e.g., mHM model, offline forcings, SMI derivation via percentile ranking). These "how-to" details should be relocated entirely to the dedicated Methods section to ensure the Introduction maintains a focus on the study's high-level objectives and overarching approach.
3. The authors provide a compelling and well-referenced justification for prioritizing the Pearson correlation coefficient to assess the temporal agreement of standardized soil moisture anomalies. However, two critical components are missing for a complete validation of the model's skill in the context of this drought study.
- To fully characterize the model's performance beyond just temporal consistency, the authors should consider reporting an appropriate error metric, such as the Unbiased Root Mean Square Error (ubRMSE). The ubRMSE is ideally suited for this validation context, as it quantifies the error component related to the model's random fluctuations and timing errors, while excluding the systematic absolute bias that is deliberately factored out by the standardization approach.
- Crucially, given that the study's goal is drought analysis, the evaluation should include an explicit assessment of the model's ability to accurately represent drought conditions as observed by the in situ data. For instance, comparing the model's and in situ data's ability to correctly classify dry/drought days based on an established threshold (e.g., the 20th percentile), assessing the correlation or error between the model-derived soil moisture index (SMI) and an index derived from the in-situ data.
4. The detailed presentation of streamflow simulation performance (Section 3.1.2) is robust, but its inclusion as a standalone subsection immediately following the core Soil Moisture evaluation (3.1.1) is structurally misleading and requires clarification.
Based on the Methods section (Lines 249–257), the streamflow analysis serves primarily as an internal calibration and performance check of the hydrological modeling framework, not a direct validation of the primary variable of interest (soil moisture). Therefore, the detailed streamflow analysis (Section 3.1.2) should be relocated and significantly condensed. This content belongs logically in a dedicated subsection within the Methods (e.g., Model Calibration) to briefly demonstrate the adequacy of the modeling framework, rather than occupying a prominent position in the main Results section. If the authors insist on keeping the streamflow results prominent, they must establish a clear logical link showing how the successful streamflow calibration improves or validates the soil moisture simulations.
5. While Section 3.2 effectively uses the Total Drought Magnitude (TDM) index to characterize the evolution of drought events and identifies a compelling increase in frequency and severity post-2011, the claims of "three distinct drought regimes" and a "significant shift" are primarily descriptive, relying heavily on the narrative of the top ten events (Figure 5). I think this conclusion lacks robust, decade-spanning quantitative evidence.
The author should consider providing a concise table or figure reporting key summary statistics for each complete decade (e.g., 1971–1980, 1981–1990, etc.), such as the total cumulative TDM or the mean annual number of drought days. This comprehensive quantitative evidence is essential to lend statistical rigor to the core finding of the "significant shift."
6. The central finding that SMI exhibits stronger persistence and SPEI underestimates deficits (L448, L455) is crucial but currently relies on qualitative descriptions (e.g., "more persistent," "underestimate"). It is better to supplement the discussion with quantified persistence metrics.
For example, for the three major events analyzed (1975–1977, 2016–2017, 2018–2019), the author should consider reporting the following metrics: a) the Maximum Duration (in months) of the moderate drought for SMI, SPEI-1, and SPEI-3, the Total Number of Months under moderate drought for each index during the event period. These data are essential for empirically validating the conclusion that SMI has higher inertia than the precipitation-based indices.
7. The Discussion section could be strengthened by elaborating on the broader implications of this work. How can the findings be applied to larger regions, or integrated into operational drought monitoring and management strategies?
Minor comments:
- The content in Section 2.2.1 regarding the lack of long-term in situ soil moisture data in Belgium and the subsequent expansion of the model domain, is more appropriate for Section 2.2.3. The Input data should focus primarily on the datasets used to drive the model, such as meteorological data, soil texture, and land cover data.
- Standardize the capitalization of section titles, as the current usage is inconsistent.
- Please standardize the terminology and use “in situ” consistently throughout the manuscript, at present some occurrences are written as “in-situ”.
- While the term "Drought Persistence" is used in the text, the definition is actually that of Cumulative Drought Exposure/Duration. In hydrology, "Persistence" often implies the autocorrelation or memory of a drought event.
- The Conclusion should focus primarily on synthesizing the study’s key findings, the main scientific contributions, and the practical implications. Condensing this section and removing discussion-type content would substantially improve clarity. Furthermore, while the section describes the worsening drought conditions and the value of soil-moisture-based indices, it does not concisely highlight the unique contributions of the study.
- Please standardize the terminology and use “root-zone” consistently throughout the manuscript, at present some occurrences are written as “root zone” or “rootzone”.
- It is recommended to present the other drought indices, which are currently discussed in the Discussion section, within the Methods section.
Special comments:
1. Line 36: Please correct “agricultural” to “agricultural”
2. Line 160: Please remove the extraneous semicolon “;”
3. Please clarify “temperature” in the meteorological forcing refers to air temperature or something else?
4. Line 425 and Line 429: Please correct “Figure ??(a)” and “Figure ??(b)”
5. Line 501 correct “cosysytems”to “ecosystems”
Citation: https://doi.org/10.5194/egusphere-2025-4526-RC2 -
AC3: 'Reply on RC2', Katoria Lesaalon Lekarkar, 16 Dec 2025
Dear,
We appreciate your effort and time to review our manuscript.
The suggestions you provided will greatly improve the manuscript. We have addressed all the comments raised in the attached document.
With best wishes,
Katoria Lekarkar, on behalf of all the co-authors
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2025-4526', Heye Bogena, 26 Sep 2025
In this paper, data from the International Soil Moisture Network (ISMN) were used; however, the Terms and Conditions were not followed. Specifically, the rule on acknowledgement and citation was disregarded, which states:
"Whenever data distributed by the ISMN are used for publication, the data's origin (i.e., the original data provider and the ISMN) must be acknowledged and referenced. A reference to both the ISMN and all networks providing data for the study in question shall be given."
Citation: https://doi.org/10.5194/egusphere-2025-4526-CC1 -
AC1: 'Reply on CC1', Katoria Lesaalon Lekarkar, 06 Oct 2025
Dear Heye,
We thank you for pointing out omission of the proper citation of the ISMN data. We have now updated our manuscript with the proper acknowledgements of the individual data providers according to the instructions in the README file which accompanies the data upon download. Since some providers (e.g., BFG_Nw and ORACLE) do not have a DOI which we can use to cite the data, we have acknowledged them in the Acknowledgements section of the manuscript using the details provided in the README file.
Below is an extract of our updated manuscript referencing the ISMN data, with the citations included.Without long-term in situ soil moisture within Belgium to validate the soil moisture output of mHM, we expanded the model domain to cover parts of France, Germany and The Netherlands where soil moisture observations are available from the International Soil Moisture Network (ISMN) (Dorigo et al., 2021). From the ISMN, we used data from the following networks: COSMOS (Zreda et al., 2008), GROW (Xaver et al., 2020), TERENO (Bogena et al., 2018), BFG_Nw and ORACLE, all shown in Figure 2.
...Acknowledgements
We acknowledge the work of Jens Wilhelmi (BFG_Nw network) for providing data in support of the International Soil Moisture Network. We also acknowledge the work of Arnaud Blanchouin and the ORACLE team of the Institut national de recherche en sciences et technologies pour l”environment et l”agriculture, France in support of the ISMN.
Citation: https://doi.org/10.5194/egusphere-2025-4526-AC1
-
AC1: 'Reply on CC1', Katoria Lesaalon Lekarkar, 06 Oct 2025
-
RC1: 'Comment on egusphere-2025-4526', Anonymous Referee #1, 30 Oct 2025
The manuscript reconstructs multi-decadal root-zone soil moisture over Belgium with mHM and characterizes drought events using an SMI-based framework, comparing them with precipitation-based indicators (SPEI-1/3). The central finding—that 2011–2020 is the driest decade since at least 1971—is relevant for Belgian drought monitoring and well aligned with broader European trends.
However, the presentation is currently hard to follow due to (i) too many metrics without a clear hierarchy for ranking drought severity. (ii) Several methodological elements (MPR, KDE→SMI percentiles, Fisher-z, NSE definition, event splitting/merging rules) need one-line clarifications so readers can reproduce and interpret results.
1. The title can be read as if only 2011–2020 is analyzed, yet the study reconstructs 1970–2020 and concludes that 2011–2020 is the driest decade of the five. Please revise the title.
2. Provide a simple, explicit severity ranking protocol. At present the Results toggle between TDM, SMI≤τ area, exposure months, peak area, duration, SPEI-1/3, etc., without a decision rule. Readers cannot tell which event is “most severe.” Please state a clear hierarchy.
Add a Table listing the top events with: TDM, peak area (%), duration, exceptional-class exposure.
Annotate TDM values directly in Fig. 5 and state in the caption which tie-breaker decided final ranks when two events are similar.
3. Lines 363-365, is 2016–2017 or 1975–1977 drought bigger? And based on which indicators?
Lines 411-414, based on drought persistence, 2011-2020 is the biggest one.
4. Lines 365 and 367, when you talk about area percentage, provide the figure reference.
5. Lines 368–372 discuss 2022–2023, but your decadal analysis ends in 2020. Please remove or move to Discussion/SI with an explicit caveat.
Also line 341 says Fig. 5 covers 1970–2023, but the figure appears to show 1970–2020—please make the figure and caption consistent with the text.
6. Add a short description about each subsection at the start of Results. Two–three sentences will prevent readers from getting lost.
7. Lines 196-199, explain why resolutions differ.
8. Define NSE on first use (Results 3.1.2). Give the range and interpretation (≈1 perfect; ≈0 equals mean-flow benchmark; <0 worse than mean).
9. Lines 470-475, restate the minimum overlap area rule used to merge adjacent months into one multi-temporal event. Or how do you define duration. This clarifies whether a brief wet interlude (e.g., March–April 2017) splits or does not split an event.
10. MPR, what is the full name?
11. Terminology clarity. The manuscript uses three different concepts that contain the word “calibration”:
(i) a 5-year warm-up: is it 1965–1969, if yes, why exclude 1970 as a calibration year for drought analysis?
(ii) excluding 1970 as a calibration year for drought analysis when forming decades (hence using 1971–1980),
(iii) streamflow parameter calibration (2000–2023) vs validation (1970–1999).
Please clarify these three meanings to avoid confusion.
Citation: https://doi.org/10.5194/egusphere-2025-4526-RC1 -
AC2: 'Reply on RC1', Katoria Lesaalon Lekarkar, 30 Oct 2025
Dear reviewer,
Thank you for your constructive feedback and suggestions on the manuscript.
We will start addressing them within the shortest time possible and share our responses to each of the queries raised.
With best wishes,
Katoria Lekarkar, on behalf of all authorsCitation: https://doi.org/10.5194/egusphere-2025-4526-AC2 -
AC4: 'Reply on RC1', Katoria Lesaalon Lekarkar, 16 Dec 2025
Dear,
Thank you for the time and effort devoted to reviewing our manuscript.
The comments were constructive and will no doubt improve the clarity and quality of the manuscript.
We have responded to all comments in the attached document.
With best wishes,
Katoria Lekarkar, on behalf of all the authors.
-
AC2: 'Reply on RC1', Katoria Lesaalon Lekarkar, 30 Oct 2025
-
RC2: 'Comment on egusphere-2025-4526', Anonymous Referee #2, 07 Dec 2025
Overall assessment
This study analyzes the root-zone soil moisture dynamics in Belgium from 1970 to 2020, focusing on the severity and persistence of droughts during the 2011-2020 period. It highlights the unprecedented nature of these droughts and evaluates the limitations of precipitation-based drought indices (such as SPEI) in drought assessments. The paper proposes using root-zone soil moisture as a more effective drought monitoring indicator and underscores the increasing frequency and persistence of drought events in the context of climate change. The work is valuable and substantial, but its scientific novelty is limited, and some methodological and analytical aspects need further clarification or strengthening.
Specifically, the manuscript currently lacks critical quantitative evidence to support two core claims: (1) that root-zone soil moisture provides added operational value over precipitation-based indices (SPEI), and (2) that the chosen reconstruction approach (mHM) is preferable to widely used soil-moisture products (e.g., ERA5-Land). I recommend the authors (a) perform an event-based contingency analysis comparing mHM-RZSM and SPEI drought events (onset, termination, duration, severity) and report hit/miss/false-alarm statistics, and (b) provide a direct inter-comparison with at least one widely used soil-moisture product to quantify time-series agreement and event-detection differences. Detailed comments follow.
Major comments:
1. Please clarify the definition of the root-zone layer used in this study (0–0.5 m). Why was this depth chosen, and how representative is it across different vegetation types and land-cover conditions in Belgium? Considering the variability of underlying surfaces could help assess the robustness of the results.
2. The Introduction successfully establishes the severity of drought in Belgium and correctly identifies the scientific gap regarding the limitations of precipitation-based indices. However, the section's structure and balance require revision to maximize clarity and scientific impact.
- The lengthy, detail-heavy descriptions of the 2011, 2018–2019, and 2022 droughts (Lines 46–74) read like an event chronicle. This narrative must be significantly condensed to focus only on the key messages that motivate the need for a long-term assessment, ensuring the scientific gap is presented more prominently.
- Conceptual Distinctions: The discussion on drought monitoring (Lines 79–103) should be enhanced by more explicitly distinguishing meteorological/ hydrological drought from agricultural drought. This includes clarifying why traditional indices like SPEI are limited and emphasizing the superior conceptual role of root-zone soil moisture (RZSM) for capturing plant water stress.
- The final paragraph (Lines 104–116) prematurely introduces technical specifics (e.g., mHM model, offline forcings, SMI derivation via percentile ranking). These "how-to" details should be relocated entirely to the dedicated Methods section to ensure the Introduction maintains a focus on the study's high-level objectives and overarching approach.
3. The authors provide a compelling and well-referenced justification for prioritizing the Pearson correlation coefficient to assess the temporal agreement of standardized soil moisture anomalies. However, two critical components are missing for a complete validation of the model's skill in the context of this drought study.
- To fully characterize the model's performance beyond just temporal consistency, the authors should consider reporting an appropriate error metric, such as the Unbiased Root Mean Square Error (ubRMSE). The ubRMSE is ideally suited for this validation context, as it quantifies the error component related to the model's random fluctuations and timing errors, while excluding the systematic absolute bias that is deliberately factored out by the standardization approach.
- Crucially, given that the study's goal is drought analysis, the evaluation should include an explicit assessment of the model's ability to accurately represent drought conditions as observed by the in situ data. For instance, comparing the model's and in situ data's ability to correctly classify dry/drought days based on an established threshold (e.g., the 20th percentile), assessing the correlation or error between the model-derived soil moisture index (SMI) and an index derived from the in-situ data.
4. The detailed presentation of streamflow simulation performance (Section 3.1.2) is robust, but its inclusion as a standalone subsection immediately following the core Soil Moisture evaluation (3.1.1) is structurally misleading and requires clarification.
Based on the Methods section (Lines 249–257), the streamflow analysis serves primarily as an internal calibration and performance check of the hydrological modeling framework, not a direct validation of the primary variable of interest (soil moisture). Therefore, the detailed streamflow analysis (Section 3.1.2) should be relocated and significantly condensed. This content belongs logically in a dedicated subsection within the Methods (e.g., Model Calibration) to briefly demonstrate the adequacy of the modeling framework, rather than occupying a prominent position in the main Results section. If the authors insist on keeping the streamflow results prominent, they must establish a clear logical link showing how the successful streamflow calibration improves or validates the soil moisture simulations.
5. While Section 3.2 effectively uses the Total Drought Magnitude (TDM) index to characterize the evolution of drought events and identifies a compelling increase in frequency and severity post-2011, the claims of "three distinct drought regimes" and a "significant shift" are primarily descriptive, relying heavily on the narrative of the top ten events (Figure 5). I think this conclusion lacks robust, decade-spanning quantitative evidence.
The author should consider providing a concise table or figure reporting key summary statistics for each complete decade (e.g., 1971–1980, 1981–1990, etc.), such as the total cumulative TDM or the mean annual number of drought days. This comprehensive quantitative evidence is essential to lend statistical rigor to the core finding of the "significant shift."
6. The central finding that SMI exhibits stronger persistence and SPEI underestimates deficits (L448, L455) is crucial but currently relies on qualitative descriptions (e.g., "more persistent," "underestimate"). It is better to supplement the discussion with quantified persistence metrics.
For example, for the three major events analyzed (1975–1977, 2016–2017, 2018–2019), the author should consider reporting the following metrics: a) the Maximum Duration (in months) of the moderate drought for SMI, SPEI-1, and SPEI-3, the Total Number of Months under moderate drought for each index during the event period. These data are essential for empirically validating the conclusion that SMI has higher inertia than the precipitation-based indices.
7. The Discussion section could be strengthened by elaborating on the broader implications of this work. How can the findings be applied to larger regions, or integrated into operational drought monitoring and management strategies?
Minor comments:
- The content in Section 2.2.1 regarding the lack of long-term in situ soil moisture data in Belgium and the subsequent expansion of the model domain, is more appropriate for Section 2.2.3. The Input data should focus primarily on the datasets used to drive the model, such as meteorological data, soil texture, and land cover data.
- Standardize the capitalization of section titles, as the current usage is inconsistent.
- Please standardize the terminology and use “in situ” consistently throughout the manuscript, at present some occurrences are written as “in-situ”.
- While the term "Drought Persistence" is used in the text, the definition is actually that of Cumulative Drought Exposure/Duration. In hydrology, "Persistence" often implies the autocorrelation or memory of a drought event.
- The Conclusion should focus primarily on synthesizing the study’s key findings, the main scientific contributions, and the practical implications. Condensing this section and removing discussion-type content would substantially improve clarity. Furthermore, while the section describes the worsening drought conditions and the value of soil-moisture-based indices, it does not concisely highlight the unique contributions of the study.
- Please standardize the terminology and use “root-zone” consistently throughout the manuscript, at present some occurrences are written as “root zone” or “rootzone”.
- It is recommended to present the other drought indices, which are currently discussed in the Discussion section, within the Methods section.
Special comments:
1. Line 36: Please correct “agricultural” to “agricultural”
2. Line 160: Please remove the extraneous semicolon “;”
3. Please clarify “temperature” in the meteorological forcing refers to air temperature or something else?
4. Line 425 and Line 429: Please correct “Figure ??(a)” and “Figure ??(b)”
5. Line 501 correct “cosysytems”to “ecosystems”
Citation: https://doi.org/10.5194/egusphere-2025-4526-RC2 -
AC3: 'Reply on RC2', Katoria Lesaalon Lekarkar, 16 Dec 2025
Dear,
We appreciate your effort and time to review our manuscript.
The suggestions you provided will greatly improve the manuscript. We have addressed all the comments raised in the attached document.
With best wishes,
Katoria Lekarkar, on behalf of all the co-authors
Peer review completion
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Katoria Lekarkar
Oldrich Rakovec
Rohini Kumar
Stefaan Dondeyne
Ann van Griensven
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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In this paper, data from the International Soil Moisture Network (ISMN) were used; however, the Terms and Conditions were not followed. Specifically, the rule on acknowledgement and citation was disregarded, which states:
"Whenever data distributed by the ISMN are used for publication, the data's origin (i.e., the original data provider and the ISMN) must be acknowledged and referenced. A reference to both the ISMN and all networks providing data for the study in question shall be given."