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A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Features on the Mongolian Plateau

Remote Sens. 2025, 17(21), 3647; https://doi.org/10.3390/rs17213647

by Lizhi Pan^1,2

, Juanle Wang^2,3,4,*

, Jing Han⁵, Kai Li^2,3

, Mengmeng Hong^1,2 and Yating Shao^1,2

Reviewer 1: Anonymous

Reviewer 2:

Kunyang Wang

Reviewer 3:

Anwar Eziz

Remote Sens. 2025, 17(21), 3647; https://doi.org/10.3390/rs17213647

Submission received: 12 September 2025 / Revised: 26 October 2025 / Accepted: 4 November 2025 / Published: 5 November 2025

(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The first comment I want to make concerns the use of acronyms. We are now in the age of acronyms, and some authors are competing to make their research articles more difficult to read by creating useless and complicated acronyms that no one can remember. Fortunately, many publishers in the scientific community have also begun to raise awareness of this problem.

A second acronym, MP, also appears in the highlights, with no clue as to its meaning. You have to get to the abstract to find out that it means Mongolian Plateau. Now, could someone please explain the need to complicate life for readers who want to read the scientific article?

To continue with the highlights, both those concerning the main findings and those concerning the main implications are not very informative and effective in providing clues to readers of either.

Abstract:

It does not summarize the manuscript well in all its parts. The objectives are missing.

Line 37. What does ‘flash drought’ mean? One can speak of flash floods, but how can a drought be flash?

Keywords: ‘multivariate composite drought index’ or ‘drought data product’ do not are keywords. Moreover, drought term occurs three times. Finally, title words shouldn’t be used as keywords.

Introduction

It is poorly structured and not very clear. The authors defined drought omitting to explain that there are different types of droughts (meteorological or climatological, agricultural, hydrological and socioeconomic drought) and correctly frame the type of drought they considered. Indeed, after defining drought as a period of below-average rainfall, it is sufficient to reach the next paragraph to encounter meteorological and agricultural droughts without even explaining how they are defined.

Moreover, the citation provided in defining drought is certainly neither original nor relevant to the purpose:

Yu, Q., Xu, C., Wu, H., Ke, Y., Zuo, X., Luo, W., Ren, H., Gu, Q., Wang, H., Ma, W., Knapp, A.K., Collins, S.L., Rudgers, J.A., 609 Luo, Y., Hautier, Y., Wang, C., Wang, Z., Jiang, Y., Han, G., Gao, Y., He, N., Zhu, J., Dong, S., Xin, X., Yu, G., Smith, M.D., Li, L., 610 Han, X., 2025. Contrasting drought sensitivity of Eurasian and North American grasslands. Nature 639, 114–118. 611 https://doi.org/10.1038/s41586-024-08478-7.

I have noticed that the authors cite many sources that are not original but cite authors who have cited relevant sources in their articles. This is not an appropriate way to cite existing literature.

Apart from all the confusing array of indices and related acronyms, it is unclear why this manuscript was submitted to Remote Sensing, given that, up to this point, it would seem more suited to a journal such as Water (for example).

Finally, validate ……, employing …… are not doing research. I also find the use of “discovering” in the title excessive and inappropriate. Usually, the features of a drought could be defined, characterized, etc., but discovering them does not seem appropriate to me.

Study

Its description is very poor as well as is the caption of Figure 1: Study area.

The elevation legend is too flattened and does not allow a proper reading of the elevations. It should have a classified colour scale to help readers understand the distribution of values. KM should be changed in km.

Data and processing

Suddenly, MODIS data appears without there having been the slightest indication of the use of remote sensing data. Of course, even these are poorly described without sufficient detail to allow readers to understand what the authors did and how they did it.

Methods: Drought indices

“Potential evapotranspiration” is limited only to Thornthwaite (1948) formulation. Is it the case or should be used reference evapotranspiration?

The indices used should be explained more clearly, with reasons given for their use in relation to other possible indices that were not selected.

Table 1. Was this index classification defined here or taken from other authors?

2.3.2. Spatial correlation coefficient

Does a 3-line subsection make sense? I don't think so. The same apply to the subsection ‘2.2.5. Coefficient of variation’.

Lines 232-275. The use of ‘Sen's slope estimator’ up to ‘Nash-Sutcliffe efficiency coefficient’ and ‘Hurst index’, should be contextualised and help the reader understand why they were chosen and how they are linked to the proposed methodological approach.

Results

Line 277. What does ‘STVDI Verification’ mean?

The description of the results appears difficult to follow due to a poor Materials and Methods section with insufficient details.

Table 2. Acronyms have to be explained in the caption.

Table 3. Km must be changed in km.

The same applies to the discussion as to the results: the Materials and Methods section needs to be improved.

Lines 522-523. There is a problem with the citation system.

Lines 553-565. The limitations should be better related to the main findings of the manuscript, avoiding the use of acronyms that only complicate the understanding of the text.

References

Please check the reference: McKee, T.B., Doesken, N.J., Kleist, J., n.d. THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME 627 SCALES

Author Response

Comments 1: The first comment I want to make concerns the use of acronyms. We are now in the age of acronyms, and some authors are competing to make their research articles more difficult to read by creating useless and complicated acronyms that no one can remember. Fortunately, many publishers in the scientific community have also begun to raise awareness of this problem.

Already in the highlights, the first word is an acronym that no one can understand: STVDI. You then have to get to the implications (third highlight) to finally find its meaning, which alone represents half of the highlight. A second acronym, MP, also appears in the highlights, with no clue as to its meaning. You have to get to the abstract to find out that it means Mongolian Plateau. Now, could someone please explain the need to complicate life for readers who want to read the scientific article? To continue with the highlights, both those concerning the main findings and those concerning the main implications are not very informative and effective in providing clues to readers of either.

Response 1: Thank you for your suggestion. It is our mistake for those acronyms without full meaning when it first time appear. Here, standard precipitation evapotranspiration - temperature - vegetation drought index (STVDI) and Mongolian Plateau (MP) are the most important words in this article and frequently appear in it. Abbreviations are used to avoid wordy writing. To make it easier for readers to read this article, explanations are provided where STVDI and MP first appear. In addition, we have tried to minimize the frequency of abbreviations appearing in highlights and the main manuscript.

Comments 2: To continue with the highlights, both those concerning the main findings and those concerning the main implications are not very informative and effective in providing clues to readers of either.

Response 2: Thank you for your comments. To increase the information volume and effectiveness of highlights and provide authors with more clues. We have once again summarized and refined the language, and made the following modifications to the highlights:

"What are the main findings?

The Mongolian Plateau is revealed to be under persistent threat from frequent flash droughts (2000-2021).
Standard precipitation evapotranspiration - temperature - vegetation drought index (STVDI) proves highly effective due to its rapid response to precipitation and soil moisture changes.

What is the implication of the main finding?

STVDI is established as a robust drought monitoring tool specifically for arid and semi-arid regions.
A new, fine-resolution (1-km) monthly drought dataset from 2000 to 2021 was provided to aid ecological and disaster management."

Followed this comment, we also checked and updated the writing of the main manuscript for easily and clearly understanding for readers.

Comments 3: Abstract: It does not summarize the manuscript well in all its parts. The objectives are missing.

Response 3: Thank you for your comment. We reconstruct the abstract according to the following content: (1) briefly introduce drought challenges in arid regions, (2) state the gap in existing indices' lag responses, (3) clearly list objectives, (4) summarize methods, (5) present key results, and (6) conclude with specific implications. The revised abstract is as follows:

“Drought represents one of the most critical environmental hazards in arid and semi-arid regions, exerting profound impacts on ecological security and sustainable development. Nevertheless, many existing drought indices exhibit delayed responses to precipitation and soil moisture anomalies, thereby constraining their ability to characterize the rapid onset and evolution of drought events. To address this limitation, we propose the Standardized Temperature–Vegetation Drought Index (STVDI), which integrates precipitation, evapotranspiration, temperature, and vegetation dynamics within a Euclidean space framework, and explicitly incorporates lag-response analysis. Taking the Mongolian Plateau (MP)—a key transition zone from taiga forest to desert steppe—as the study region, we constructed a 1 km resolution STVDI dataset spanning 2000–2021. Results reveal that over 88% of the MP is highly susceptible to flash droughts, with an average lag time of only 0.52 days, underscoring the index’s capacity for rapid drought detection. Spatial analysis indicates that drought severity peaks during March and April, with moderate drought concentrated in central Mongolia and severe droughts prevailing across southwestern Inner Mongolia. Although trend analysis suggests a slight long-term alleviation of drought intensity, nearly 70% of the MP is projected to experience further intensification in the future. This study delivers the first high-resolution, low-lag drought monitoring dataset for the MP and advances theoretical understanding of drought propagation and lag mechanisms in arid and semi-arid ecosystems.”

Comments 4: What does ‘flash drought’ mean? One can speak of flash floods, but how can a drought be flash?

Response 4: Thank you for your questions about flash drought. "Flash drought" refers to a rapidly intensifying drought where soil moisture depletes in a matter of weeks, driven more by spikes in atmospheric aridity (e.g., heatwaves, dry winds) than by long-term rainfall shortage. The key reason we highlight this phenomenon is that our findings reveal the Mongolian Plateau is highly susceptible to these sudden events. Furthermore, the design of our STVDI index—with its low lag and high sensitivity—is particularly effective for monitoring such rapid changes, which traditional slow-response indices might miss.

Comments 5: Keywords: ‘multivariate composite drought index’ or ‘drought data product’ do not are keywords. Moreover, drought term occurs three times. Finally, title words shouldn’t be used as keywords.

Response 5: Thank you for raising this important point. The revised keywords are as follows: “composite drought index; lag time; drought monitoring; Mongolian Plateau; remote sensing; soil moisture”

Comments 6: It is poorly structured and not very clear. The authors defined drought omitting to explain that there are different types of droughts (meteorological or climatological, agricultural, hydrological and socioeconomic drought) and correctly frame the type of drought they considered. Indeed, after defining drought as a period of below-average rainfall, it is sufficient to reach the next paragraph to encounter meteorological and agricultural droughts without even explaining how they are defined.

Response 6: Thank you for your comments. In the introduction, we have added the explaining and differences of various drought types and strengthened the overall logical relationship. “The term encompasses distinct phenomena with different onset and impacts: meteorological drought signals precipitation deficits, agricultural drought reflects soil moisture shortages affecting crops, hydrological drought manifests as reduced water resources with a time lag, and socioeconomic drought arises when water shortages affect human activities. Among these, meteorological and agricultural droughts are most relevant in arid and semi-arid regions and form the focus of this study.”

In addition, we have made significant revisions to the overall introduction to make it clearer and more reasonable. More details can be seen in the revision manuscript.

Comments 7: Moreover, the citation provided in defining drought is certainly neither original nor relevant to the purpose:

I have noticed that the authors cite many sources that are not original but cite authors who have cited relevant sources in their articles. This is not an appropriate way to cite existing literature.

Response 7: Thank you for your kind comments. The citation provided in defining drought was revised to a more direct reference:

“Nairizi, S. Drought and water scarcity. International Commission on Irrigation and Drainage: New Delhi, India, 2017.”

Other similar inappropriate references that appeared throughout the text were checked and revised to ensure that such situations would not occur again.

Comments 8: Finally, validate ……, employing …… are not doing research. I also find the use of “discovering” in the title excessive and inappropriate. Usually, the features of a drought could be defined, characterized, etc., but discovering them does not seem appropriate to me.

Response 8: Thank you for the suggestions. We have replaced the technical terms such as "validating" and "employing" in the original text with more research-oriented expressions like "evaluate" and "analyze". The relevant revisions in the introduction section are as follows.

"The objectives of this study are therefore to: (1) evaluate the performance and responsiveness of the proposed validate the effectiveness of STVDI for drought monitoring on the MP; (2) analyze the spatiotemporal distribution and seasonal variations of drought on the MP from 2000 to 2021 at 1 km resolution; (3) examine drought propagation and lag characteristics based on STVDI; and (4) evaluate long-term drought trends and persistence using statistical approaches including the Hurst index."

In order to make the title be more concise and specific to highlight the key innovation, we have changed the title to: “A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Features on the Mongolian Plateau.”

Comments 9: Study: Its description is very poor as well as is the caption of Figure 1: Study area.

Response 9: Thank you for your comment. We have reorganized the description of the Mongolian Plateau. To enrich the content, the revised content is as follows:

"The Mongolian Plateau (87°43′–126°04′E, 37°22′–53°20′N) is an inland plateau in Asia. The total area of the study region is ~2,747,116 km2, comprising 1,183,000 km2 of Inner Mongolia and 1,564,116 km2 of Mongolia (Figure 1). Mountains dominate the northwest, hills occupy central and western regions, and the Gobi Desert spans the southwest, with an average elevation of 1,580 m. Vegetation shows a north–south gradient from forest and forest-steppe to typical steppe, desert steppe, and Gobi Desert. Soil types vary accordingly, including chestnut and dark brown soils in steppe areas, meadow soils in forest-steppe zones, and sandy or saline soils in deserts, influencing water retention and drought propagation. The climate transitions from subhumid zones in the north and east to semi-arid, arid, and hyper-arid zones in the southwest. Annual precipitation ranges from 250 to 450 mm, decreasing north to south and east to west, while high evapotranspiration in arid zones exacerbates drought severity. Land use patterns further shape vulnerability: northern areas support forestry and cropland, central and eastern regions are dominated by grazing, and overgrazing in steppe and desert-steppe zones reduces vegetation resilience. The Gobi region is sparsely populated and ecologically fragile, with sandy soils and limited water heightening drought risks. The interactions among topography, soil types, vegetation cover, climate, and land use drive the spatial heterogeneity of drought occurrence and propagation across the Mongolian Plateau."

We have changed the caption of Figure 1 to "Map of the Mongolian Plateau".

Comments 10: The elevation legend is too flattened and does not allow a proper reading of the elevations. It should have a classified colour scale to help readers understand the distribution of values. KM should be changed in km.

Response 10: Thank you for the suggestion on Figure 1, it is helpful. We have modified the legend and units (km) as required. To enhance the readability of elevation values, the color ramp was updated to a green-to-yellow-to-red gradient, and the corresponding intermediate elevation values were clearly labeled in the legend. Figure 1 is shown as follows.

Comments 11: Suddenly, MODIS data appears without there having been the slightest indication of the use of remote sensing data. Of course, even these are poorly described without sufficient detail to allow readers to understand what the authors did and how they did it.

Response 11: Thank you for your comments. We have added more details in the introduction to describe the remote sensing data (MODIS data) used in the calculation of drought index. The following description has been specifically added:

"Modeling is typically performed using remote sensing data, especially Moderate-resolution Imaging Spectroradiometer (MODIS) data [17]. With the rapid development of large areas remote sensing monitoring, MODIS data is widely used in drought monitoring due to its advantages of high temporal resolution (daily), wide coverage (global cover), and easy accessibility (free access)."

In the method, we have also added more details to the section on MODIS data, including how we used these data. The modified complete MODIS data section is as follows:

"The MOD11A2 product (global 1-km 8-day land surface temperature, LST), MOD13Q1 product (global 250m 16-day NDVI), and MOD16A2 (global 500m 8-day potential evapotranspiration, PET) were obtained from NASA Earth Observation System Data Gateway. To maintain the same spatiotemporal resolution, the PET (500 m) and NDVI (250 m) were resampled to 1,000 m through bicubic interpolation, and the 16-day NDVI, 8-day LST, and PET values were averaged to obtain their monthly products. Using Google Earth Engine, batch computations were performed on these MODIS products to derive the monthly LST, NDVI, and PET. These monthly products were then used to calculate the Temperature Condition Index (TCI) using LST, the Vegetation Condition Index (VCI) using NDVI, and the Standardized Precipitation Evapotranspiration Index (SPEI) using PET."

Comments 12: “Potential evapotranspiration” is limited only to Thornthwaite (1948) formulation. Is it the case or should be used reference evapotranspiration?

Response 12: Thank you for comment. In this study, we use potential Evapotranspiration to calculate the Standardized Precipitation Evapotranspiration Index, rather than the Thornthwaite (1948) formulation. The Thornthwaite (1948) formula was one of the early methods for estimating potential evapotranspiration, but it was not the only method. Due to the fact that the result space resolution calculated by Thornthwaite (1948) formula is too low and does not fit the current climate change background, we have replaced the Thornthwaite (1948) formula with MODIS data (Potential evapotranspiration).

Comments 13: The indices used should be explained more clearly, with reasons given for their use in relation to other possible indices that were not selected.

Response 13: Thank you for your suggestions. In this study, we selected SPEI, VCI, and TCI as the core components of the STVDI due to their complementary capacities in capturing meteorological and agricultural drought conditions. SPEI includes both precipitation and potential evapotranspiration, making it more sensitive to climatic water balance than SPI, which only considers precipitation. VCI reflects vegetation health anomalies by normalizing NDVI, offering spatial comparability across regions and time periods. TCI captures surface temperature stress on vegetation, which is especially relevant in identifying heat-induced drought effects or compound drought–heatwave events. The three have complementary advantages in the multi-dimensional monitoring of drought. More details can be seen in the updated Section "2.3.1. Drought Indices".

Comments 14: Table 1. Was this index classification defined here or taken from other authors?

Response 14: Thank you for your comments. The index classification in Table 1 is defined by a national standard used in this region (GB/T 20481–2017). We have added this citation (Zhang, L., Jiao, W., Zhang, H., Huang, C., & Tong, Q. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote sensing of environment. 2017, 190, 96-106.) in 2.3.1.

Comments 15: 2.3.2. Spatial correlation coefficient Does a 3-line subsection make sense? I don't think so. The same apply to the subsection “2.3.5. Coefficient of variation“.

Response 15: Thank you for comment. In order to make sense, we provided more detailed descriptions of these two methods:

“2.3.2. Spatial correlation coefficient

The spatial correlation analysis method within the Geographic Information System was employed to calculate the correlation coefficient (r), systematically analyzing and comparing the relationships between various drought indices and both precipitation and SM. The correlation coefficient ? (usually referring to the Pearson correlation coefficient) is a statistic that measures the strength and direction of the linear relationship between two variables. The calculation formula is as follows:

and are respectively the ? th observations of the two variables X and ?. and are respectively the averages of the variables ? and Y. The value range of r is from -1 to 1, where: ?=1 indicates perfect positive correlation; r= -1: Indicates a complete negative correlation; r=0: Indicates no linear correlation.”

“2.3.5. Coefficient of variation

The coefficient of variation (CV) is commonly used to quantify the relative degree of dispersion of a variable by normalizing the standard deviation to the mean value. It provides a dimensionless measure that enables comparisons of variability among datasets with different units or magnitudes.

and are the mean and standard deviation of the STVDI. Generally, a smaller CV value indicates less fluctuation and greater temporal stability, vice versa. Specifically, when STVDI_CV < 0.3, the temporal variation of STVDI is considered low and stable; 0.3–0.5 suggests moderate variability, and values greater than 0.5 indicate high instability. This method quantitatively validates the stability of drought indices across time and regions.”

Comments 16: Lines 232-275. The use of ‘Sen's slope estimator’ up to ‘Nash-Sutcliffe efficiency coefficient’ and ‘Hurst index’, should be contextualised and help the reader understand why they were chosen and how they are linked to the proposed methodological approach.

Response 16: Thank you for your comments. We provide conceptual explanations and the applicability of all methods to enhance clarity and support methodological rigor in section "2.3 Methods". Sen's slope estimator robustly analyzes drought intensity trends, as it is distribution-free and effectively identifies long-term monotonic changes. Nash–Sutcliffe Efficiency (NSE) serves as a key metric for model consistency evaluation, as it compares deviations between observed and predicted values and effectively quantifies the degree to which the STVDI aligns with reference indicators such as soil moisture or precipitation-based indices. The Hurst exponent provides a measure of the long-term memory or persistence in the STVDI time series and is instrumental in diagnosing whether regional drought dynamics are likely to continue, stabilize, or reverse in the future, offering theoretical insight into potential drought risk evolution. The revised version can be found in Section 2.3.2 - 2.3.7.

Comments 17: Results Line 277. What does ‘STVDI Verification’ mean?

Response 17: Thank you for your questions about 'STVDI Verification'. We have changed the title to "Evaluation of the STVDI" of this section (Line 385). In our research, STVDI is a newly constructed index. The qualitative to quantitative verification of it is to ensure its effectiveness and reliability in drought monitoring. Specifically, we conducted a quantitative comparison between STVDI and other indices, verifying its reliability and superiority.

Comments 18: The description of the results appears difficult to follow due to a poor Materials and Methods section with insufficient details.

Response 18: Thank you for your comment. Based on the revised method section, we made necessary modifications to the "Results" section to ensure that the method, the objective, and the results are all in correspondence. Specifically, we presented clearer regional patterns of STVDI performance based on observed values; reported specific lag day values and their spatial distributions; added statistical significance tests (e.g., p-values) to support key numerical findings; structured the long-term trend results (Sen’s slope and Hurst index) as descriptive outputs. Please refer to the revised version for the modifications in Sections 3.1.1, 3.1.3, and 3.2.2.

Comments 19: Table 2. Acronyms have to be explained in the caption.

Response 19: Thank you for comment. We have added annotations to Table 2 to explain these acronyms. The specific modifications are as follows.

“Notes: Correlation coefficient (CC), Coefficient of variation (CV), Nash-Sutcliffe efficiency coefficient (NSE)”

Comments 20: Table 3. Km must be changed in km.

Response 20: Thank you. We have modified the km unit here and checked the full text to ensure that such problems will not occur again.

Comments 21: The same applies to the discussion as to the results: the Materials and Methods section needs to be improved.

Response 21: Thank you for your comment. We have significantly improved the discussion with reference to the data and methodological framework outlined in the Materials and Methods section. Specifically: ①We expanded the analysis of regional differences in drought lag to explain how local climatic and ecological factors influence the responsiveness of the STVDI. ②We clarified the distinction between susceptibility to flash droughts and actual drought occurrence, thereby refining the interpretation of the near-zero lag results. ③We thoroughly discussed the uncertainties in precipitation downscaling and the limitations of MODIS datasets, particularly regarding NDVI saturation and LST retrieval errors. ④Additionally, we addressed the potential overfitting risk of the Euclidean modeling method, emphasizing the steps taken to mitigate it, including normalization and cross-validation. More details can be seen in "2.3. Methods" and "4. Discussion".

Comments 22: Lines 522-523. There is a problem with the citation system.

Response 22: Sorry for this mistake. We have removed the incorrect reference here.

Comments 23: Lines 553-565. The limitations should be better related to the main findings of the manuscript, avoiding the use of acronyms that only complicate the understanding of the text.

Response 23: Thank you for your suggestions. The limitations have been modified as required. We elaborated on the uncertainties in precipitation downscaling and the limitations of the MODIS product that affect the accuracy of STVDI. Additionally, we discussed the potential overfitting of the Euclidean model (lines 708-727).

Comments 24: References Please check the reference: McKee, T.B., Doesken, N.J., Kleist, J., n.d. THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME 627 SCALES

Response 24: Thank you for pointing out the missing information of the references cited here. We have already made the corrections and additions.

“McKee, T. B., Doesken, N. J., & Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology. 1993, Vol. 17, No. 22, pp. 179-183.”

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This paper's topic is of practical significance. It attempts to combine meteorological indices with remote sensing indicators and proposes the STVDI for drought monitoring. However, overall, the manuscript suffers from significant shortcomings in the following areas:

Lack of Innovation: The construction of the STVDI is highly similar to existing three-dimensional Euclidean distance drought indices, such as the TVPDI, TVMDI, and TSWDI, differing only in the combination of variables, lacking any substantial breakthrough.

Methodological Issues:

The study's hysteresis effect results clearly contradict the generally accepted precipitation hysteresis pattern, suggesting problems with the methodological design or data processing.

The drought thresholds lack statistical or empirical support.

The description of data downscaling and error handling is inadequate, impacting reproducibility.

Insufficient Support for the Results and Conclusions:

There is a lack of validation with independent observational data (e.g., soil moisture or crop yield).

The comparative analysis lacks quantitative statistical methods, making it difficult to demonstrate the STVDI's superiority over existing indices.

The conclusions overemphasize the STVDI's superiority and potential for flash drought warnings, but lack sufficient empirical support.

Insufficient Discussion:

The unusual results (almost no lag response) are not adequately explained.

Insufficient comparison with existing international studies fails to highlight the paper's unique contribution.

The discussion of application prospects and feasibility is superficial.

Overall Recommendation: This study has some regional application value, but lacks methodological reliability and innovation. Substantial revisions are required, particularly re-examination of lag effects, additional observational validation, and systematic comparison with existing indicators.

Author Response

Comments 1: Lack of Innovation: The construction of the STVDI is highly similar to existing three-dimensional Euclidean distance drought indices, such as the TVPDI, TVMDI, and TSWDI, differing only in the combination of variables, lacking any substantial breakthrough.

Response 1: Thank you for comment about the innovation. Our primary innovation lies in the low time-lag performance of STVDI in capturing drought-related characteristics, as well as its capacity for timely response to flash drought. The title has been revised to "A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Characteristics on the Mongolian Plateau" to highlight the innovative aspect. During our discussion, we added the analysis of regional differences and causes of the low lag of STVDI (lines 625-645), and clarified the potential susceptibility of STVDI to flash drought (lines 679-699). The complete revised content can be found in "4. Discussion ". Besides this, we also revised the Abstract, Introduction and Conclusion sections to enhance the innovation of this study.

Comments 2: The study's hysteresis effect results clearly contradict the generally accepted precipitation hysteresis pattern, suggesting problems with the methodological design or data processing.

Response 2: Thank you for your comment. In our results, over 85% of the region shows near-zero lag between precipitation and the STVDI response. This indeed contrasts with traditional drought indices that typically show a positive lag of 1–2 months, especially in vegetated systems influenced by soil moisture dynamics. The near-zero lag response is attributed to the integration of fast-reacting climate and remote sensing indicators in the STVDI, combined with the dryland conditions of the study area, characterized by shallow soils and limited moisture retention. In response, we have revised the Discussion section to clarify the ecological mechanisms potentially driving fast lag responses, and to distinguish between susceptibility to flash drought and actual lag behavior. We have further incorporated a discussion on the regional variability in lag response. We also revised the Results section to interpret these findings more cautiously and refined the Methods section to explain how lag was calculated. See the "Methods" and "Discussion" sections for these modifications.

Comments 3: The drought thresholds lack statistical or empirical support.

Response 3: Thank you for your suggestions. The index classification in Table 1 is based on the national standard used in this regions (GB/T 32136–2015; GB/T 20481–2017). To verify the accuracy of the classification, we supplemented a comparative experiment with historical drought events.

To validate the drought level thresholds of the proposed STVDI, we compared the STVDI-based classification results against the Global SPEI Database (version 2.08) over the Mongolian Plateau during the drought-prone year 2007, which has well-documented severe drought events. We conducted a pixel-by-pixel comparison (randomly selecting 7,000-pixel points) and constructed a five-category confusion matrix (Table 2). The validation results show an overall classification accuracy of 78.6%, demonstrating a substantial agreement between the STVDI and SPEI-derived drought intensity levels. This supports the reliability of the drought threshold values derived from national standards and confirms STVDI’s effectiveness in capturing real drought severity patterns.

Table 2 Error matrix of drought index (STVDI) in Mongolian Plateau in 2007

		Evaluation results (STVDI)
	Drought levels	No drought	Mild	Moderate	Severe	Extreme
Global SPEI Database	No drought	1823	91	28	9	1
	Mild	109	1027	153	91	5
	Moderate	42	104	796	196	26
	Severe	9	36	215	894	164
	Extreme	2	3	43	168	965

In addition, we have also added the verification of historical drought events on the drought thresholds (lines 543-575) in section 3.2.2.

Comments 4: The description of data downscaling and error handling is inadequate, impacting reproducibility.

Response 4: Thank you for your comment. We have provided supplementary explanations for the downscaling method and error handling in "2.2.2. Meteorological data ". The specific modification contents are as follows.

"The downscaling process used the proportional method with high-resolution auxiliary data (elevation, slope, and aspect) to distribute precipitation while maintaining precipitation totals within each 0.5° grid. This method achieved a correlation of 0.85 with ground observations (RMSE=12.3 mm/month). The downscaling uncertainty was assessed using cross-validation, with relative errors typically within ±15% for monthly values. The monthly downscaled Pre data with a resolution of 1000 m were obtained through this rigorously validated method."

Comments 5: Insufficient Support for the Results and Conclusions: There is a lack of validation with independent observational data (e.g., soil moisture or crop yield).

Response 5: Thank you for your comments. The meteorological station data in Mongolia is severely lacking and difficult to obtain. We introduced historical drought events to verify the accuracy of the drought (STVDI) classification. We found that the severe drought events mainly occurred in 2003, 2005, 2007, and 2017. STVDI can well reflect the occurrence of these severe drought events (Figure 1). We have also enhanced this description in the "Results" section as follows.

"The severe drought events primarily occurred in 2003, 2005, 2007, and 2017 [35,57], and the STVDI effectively captures the occurrence of these events."

Figure 1 The interannual variation of STVDI and the comparison with historical drought events during the period from 2000 to 2021.

Comments 6: The comparative analysis lacks quantitative statistical methods, making it difficult to demonstrate the STVDI's superiority over existing indices.

Response 6: Thank you for your kind comments. In Table 2 (line 496), we have detailed the performance indicators of STVDI and other drought indices at different time scales, so that readers can have an intuitive understanding of the performance of STVDI in drought monitoring. We also conducted statistical analysis on these indicators to assess whether the performance of STVDI at different time scales was significantly better than that of the existing drought index. Specifically, we conducted statistical comparisons of STVDI, SPEI and VHI, including correlation coefficient (CC), coefficient of variation (CV), Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The independent historical drought data verification in response 5 can also support the superiority of STVDI.

Comments 7: The conclusions overemphasize the STVDI's superiority and potential for flash drought warnings, but lack sufficient empirical support.

Response 7: Thank you for your comment. In our discussion, we have currently distinguished the susceptibility of flash drought from its actual occurrence. We have updated the references to support the conclusion section. The modifications are as follows: "Regarding rapid-onset droughts, our analysis indicates that over 88% of the MP exhibits near-zero lag between STVDI and both soil moisture and precipitation, reflecting highly synchronized drought development. It is important to clarify that this finding represents potential susceptibility to rapid-onset (flash) drought events rather than actual occurrences. Flash droughts are typically defined based on specific criteria such as rapid soil moisture decline or vegetation stress over a short period (e.g., 5–30 days) [58,59]. Thus, while the near-zero lag highlights regions where drought signals can develop quickly, additional event-based validation would be required to confirm actual flash drought events. The observed average lag time of 0.52 days further emphasizes the rapid propagation of drought signals in these susceptible regions." More details can be seen in "4. discussion".

Comments 8: The unusual results (almost no lag response) are not adequately explained.

Response 8: Thank you for comment. We offer the following clarifications and possible explanations. First, the STVDI combines both meteorological and remote sensing components (SPEI, VCI, TCI). Unlike traditional indices that rely solely on precipitation accumulation, STVDI directly incorporates temperature and vegetation responses, particularly via TCI and VCI, which are known to respond rapidly to environmental stressors in arid/semi-arid ecosystems. Second, the Mongolian Plateau represents a typical dryland system with shallow soils, sparse vegetation, rapid evapotranspiration, and low moisture buffering capacity. These conditions can lead to faster hydro-ecological responses following rainfall events, resulting in shorter lag periods. Third, our analysis is conducted at a high spatial resolution (1 km) and monthly timescale. High-frequency responses may be more detectable at finer resolutions, especially when using remote sensing indices that reflect vegetation or surface thermal state, which typically react more quickly than deeper soil or hydrological systems. Despite the plausible ecological and methodological explanations, we acknowledge that such minimal lag results could partly reflect artifacts due to variable scaling, noise, or over-sensitivity in data. Therefore, in the revised manuscript, we added a more comprehensive discussion on the ecological plausibility and data-driven limitations of these results; emphasized regional heterogeneity in lag response patterns, avoiding overgeneralization; clearly distinguished between lag in vegetation stress signals versus lag in hydrological processes. The details above mentioned can be found in "4. Discussion".

Comments 9: Insufficient comparison with existing international studies fails to highlight the paper's unique contribution.

Response 9: Thank you for this insightful comment. In the discussion and introduction section, we have added comparisons with related research, the contributions of this study, and authoritative cutting-edge research results. We highlight key limitations in previous works such as time-lag issues, coarse spatial resolution, and the inability to capture flash droughts. The STVDI proposed in this study addresses these gaps through multi-source data fusion, low-lag performance, fine spatial resolution, and an unique Euclidean space–based drought framework with forward-looking trend analysis. These points have been clarified to better communicate the scientific novelty and application value of STVDI The revision in section XXX are as follows.

"In recent years, numerous studies have developed drought indices using single or dual-variable approaches based on SPI, SPEI, NDVI, soil moisture (SM), TCI, or their combinations [37,38]. While models such as SPEI-VCI and Vegetation Health Index (VHI) have been widely used, many suffer from limitations including time-lagged responses to precipitation anomalies, low spatial resolution [39], and limited effectiveness in detecting flash droughts [40], especially in arid regions like the Mongolian Plateau.

To address this gap, the present study introduces the Standardized Temperature–Vegetation Drought Index (STVDI), developed within a three-dimensional Euclidean space constructed from SPEI, TCI, and VCI. This formulation comprehensively integrates precipitation, evapotranspiration, temperature, and vegetation conditions while accounting for lag responses, thereby combining the strengths of meteorological and agricultural drought indices. The central hypothesis is that combining SPEI, TCI, and VCI in a Euclidean framework reduces lag effects and improves the capacity for near real-time drought detection compared to existing indices. The objectives of this study are therefore to: (1) validate the effectiveness of STVDI for drought monitoring on the MP; (2) analyze the spatiotemporal distribution and seasonal variations of drought on the MP from 2000 to 2021 at 1 km resolution; (3) examine drought propagation and lag characteristics based on STVDI; and (4) evaluate long-term drought trends and persistence using statistical approaches including the Hurst index. This study seeks to provide both a practical drought monitoring method and new theoretical insights into drought lag mechanisms in arid and semi-arid ecosystems."

Comments 10: The discussion of application prospects and feasibility is superficial.

Response 10: Thank you for your suggestions. During our discussion (section 4), we added more details on application prospects and feasibility. The specific contents are as follows. "The 1-km monthly STVDI dataset developed in this study could be utilized for regional drought risk assessment, rangeland and water resource management, as well as ecological protection efforts. The STVDI framework is fully dependent on globally available, open-access datasets (e.g., MODIS, CRU), which ensures high reproducibility and transferability to other regions. It can be integrated into regional or national drought monitoring systems (e.g., China’s National Disaster Reduction Center, Mongolia’s Environmental Monitoring Networks), with deployment supported by Google Earth Engine to enable cost-efficient, automated processing. While this study focused on the Mongolian Plateau, the STVDI methodology can be generalized to other regions with similar semi-arid climatic conditions, such as Central Asia and African savannas."

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Review Report on "Discovering Drought Features on the Mongolian Plateau Using a Novel Low-Lag Response Composite Drought Index"

General Comments

This manuscript presents a novel composite drought index (STVDI) that integrates SPEI, TCI, and VCI within a Euclidean space to monitor drought with low lag response on the Mongolian Plateau. The study's strengths include the development of a high-resolution (1 km) monthly drought dataset spanning 2000–2021, comprehensive spatiotemporal analysis, and a focus on lag mechanisms—a relevant aspect for flash drought monitoring. However, critical weaknesses undermine its scientific rigor. The methodological framework lacks clarity in the transformation of SPEI to a 0–1 scale (Figure 2), which is essential for the Euclidean distance calculation, and the validation of STVDI against precipitation and soil moisture relies on correlation analyses without addressing potential confounding factors (e.g., spatial resolution mismatches in data). Additionally, the classification of drought levels (Table 1) is not sufficiently justified against established standards, and the discussion overstates implications for flash droughts without direct evidence from event-based analysis. With that being said, I recommend major revision before going to publication. For the specific comments, please refer to the following:

Specific Comments

2.1 Suggestion for the Title

The current title accurately reflects the study's focus on drought features and the novel index but could be more concise and specific to highlight the key innovation. The term "Discovering" is somewhat vague, and "Low-Lag Response" might be better phrased to emphasize the temporal aspect. Consider: "A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Features on the Mongolian Plateau." This alternative maintains accuracy while enhancing appeal and clarity.

2.2 Suggestion for the Keywords

The provided keywords cover core concepts but lack specificity and include redundancy. "Multivariate composite drought index" is similar to "drought monitoring," and "lag response" could be more precise. Recommend a focused set: "composite drought index; lag time; drought monitoring; Mongolian Plateau; remote sensing; soil moisture." This set improves searchability and aligns with the journal's scope.

2.3 Suggestion for the Abstract

The abstract outlines the problem, objectives, and key findings but lacks a clear statement of the research gap and overstates conclusions. For instance, it claims STVDI enables "nearly real-time monitoring" without defining what constitutes real-time in this context. Suggest restructuring to: (1) briefly introduce drought challenges in arid regions, (2) state the gap in existing indices' lag responses, (3) clearly list objectives, (4) summarize methods (e.g., Euclidean space integration), (5) present key results (e.g., low lag times, spatial patterns), and (6) conclude with specific implications. Also, ensure all acronyms (e.g., STVDI) are defined upon first use.

2.4 Suggestion for the Introduction

The introduction effectively contextualizes drought impacts and reviews existing indices but lacks a logical flow to the specific research gap. While it mentions the lag response issue, it does not explicitly justify why the Euclidean space approach is novel or how it addresses this gap. The hypotheses are implied but not stated; for example, the assumption that combining SPEI, TCI, and VCI reduces lag should be made explicit. Additionally, the objectives listed on page 3 are clear, but the manuscript later introduces analysis of future trends (e.g., Hurst index), which is not mentioned in the introduction. Revise to: (1) strengthen the link between the limitations of existing indices and the need for STVDI, (2) explicitly state hypotheses, and (3) ensure all objectives covered in the paper are included upfront.

2.5 Suggestion for the Materials and Methods

This section requires significant revision to ensure reproducibility and justify choices. Specifically:

Study Area Description (Page 3): Add more ecological and climatic details, such as dominant soil types or land use patterns, which influence drought propagation.
Data Processing (Page 4): The downscaling of precipitation data using the "proportional method" is not adequately described or cited. Provide equations or references to validate this approach. Similarly, justify the use of nearest-neighbor resampling for soil moisture data, as it may introduce errors.
STVDI Formulation (Pages 5–6): The transformation of SPEI to a 0–1 scale for the Euclidean space is not explained. SPEI typically ranges from -∞ to +∞, so how was it normalized? Without this, the vector magnitude (equation 9) may be mathematically flawed. Clarify this step and justify the choice of Euclidean distance over other metrics (e.g., Mahalanobis distance).
Classification Scheme (Table 1): The drought levels are based on national standards but lack validation against ground truth or historical events. Include a confusion matrix or error analysis to support the thresholds.
Validation Methods (Pages 6–7): The cross-correlation function (CCF) is applied, but the interpretation of lag days (e.g., negative values in Table 2) is confusing. Define lag direction clearly (e.g., positive lag means STVDI responds after precipitation) and justify the ±3-month window. Also, ensure that the Nash-Sutcliffe efficiency (NSE) is used appropriately, as it is typically for model output comparison, not index validation.

2.6 Suggestion for the Results

The results are comprehensive but often presented without direct linkage to objectives. For example:

Figure 3 and 4: Show correlations with other indices but do not explain why STVDI performs better in certain regions. Add spatial context (e.g., why low correlation in northeast?).
Table 2 and Figure 9: Report lag days but lack statistical significance tests. Include p-values or confidence intervals to validate claims.
Drought Trends (Page 15): The Sen’s slope and Hurst index analysis is well-presented, but the combination for future trends (Table 4) is not clearly explained. Define the categories (e.g., "+ -" meaning) in the text or caption. Ensure all figures have high-resolution maps with clear legends and scales, and refer to them in the text to guide the reader.

2.7 Suggestion for the Discussion

The discussion effectively interprets results in the context of existing literature but overlooks key limitations and counter-intuitive findings. For instance:

Lag Response (Page 17): The claim that STVDI has "minimal lag" is based on average values, but Figure 9 shows regional variations (e.g., positive lags in some areas). Explain these discrepancies—could they be due to data quality or ecological factors?
Flash Droughts (Page 18): The assertion that over 88% of areas exhibit "flash drought" susceptibility is overstated without defining flash drought criteria or linking to specific events. Differentiate between susceptibility and actual occurrence.
Limitations (Page 18): Acknowledged but superficial. Expand on how precipitation downscaling uncertainties or MODIS product limitations (e.g., cloud cover) might affect STVDI. Also, discuss the potential overfitting of the Euclidean model.

2.8 Suggestion for the Conclusion

The conclusion summarizes findings but overstates implications, such as "widespread susceptibility to flash droughts" and "valuable resource for policy-making." Ensure claims are tempered by the study's scope—e.g., the dataset is useful but requires validation against independent data. Specify recommendations for future work, such as integrating higher-resolution data or applying STVDI to other regions.

2.9 Suggestion for the References

References are mostly relevant and up-to-date, but formatting inconsistencies exist (e.g., some URLs are included, while others are not). Adhere strictly to MDPI style: ensure all journal names are italicized, and URLs are removed for digital object identifiers (DOIs). Also, include key foundational studies on composite indices (e.g., more on VHI development) to strengthen the literature review.

Comments on the Quality of English Language

The manuscript has several grammatical errors and awkward phrasings that affect clarity. Examples:

Page 1, Highlights: "most of the MP faced a high risk of frequent flash droughts" → "most of the MP faces a high risk of frequent flash droughts."
Page 2, Abstract: "limits their ability to fully discovery the drought features" → "limiting their ability to fully discover drought features."
Page 6, Section 2.3.1: "The line DW represents the dry and wet edges" → "The line DW represents the dry-wet gradient." Recommend thorough proofreading to improve flow and academic tone. Use active voice where possible (e.g., "we developed" instead of "a STVDI dataset was developed").

Author Response

Comments 1: Suggestion for the Title

Response 1: Thank you for your constructive suggestion. The suggested title has more clear meaning and enhance the highlight for low lag response of this study. So, the title has been changed from "Discovering Drought Features on the Mongolian Plateau Using a Novel Low-Lag Response Composite Drought Index" to "A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Features on the Mongolian Plateau".

Comments 2: Suggestion for the Keywords

Response 2: Thank you for your kind comments. Following the suggestion, the keywords in the manuscript have been revised as "composite drought index; lag time; drought monitoring; Mongolian Plateau; remote sensing; soil moisture."

Comments 3: Suggestion for the Abstract

Response 3: Thank you for your very helpful suggestions. The abstract of this article has been modified according to your suggestion.

The revised Abstract is as follows: "Drought represents one of the most critical environmental hazards in arid and semi-arid regions, exerting profound impacts on ecological security and sustainable development. Nevertheless, many existing drought indices exhibit delayed responses to precipitation and soil moisture anomalies, thereby constraining their ability to characterize the rapid onset and evolution of drought events. To address this limitation, we propose the Standardized Temperature–Vegetation Drought Index (STVDI), which integrates precipitation, evapotranspiration, temperature, and vegetation dynamics within a Euclidean space framework, and explicitly incorporates lag-response analysis. Taking the Mongolian Plateau (MP)—a key transition zone from taiga forest to desert steppe—as the study region, we constructed a 1 km resolution STVDI dataset spanning 2000–2021. Results reveal that over 88% of the MP is highly susceptible to flash droughts, with an average lag time of only 0.52 days, underscoring the index’s capacity for rapid drought detection. Spatial analysis indicates that drought severity peaks during March and April, with moderate drought conditions concentrated in central Mongolia and severe droughts prevailing across southwestern Inner Mongolia. Although trend analysis suggests a slight long-term alleviation of drought intensity, nearly 70% of the MP is projected to experience further intensification in the future. This study delivers the first high-resolution, low-lag drought monitoring dataset for the MP and advances. "

Comments 4: Suggestion for the Introduction

Response 4: Thank you for your suggestions. The introduction of this article has been modified according to your suggestion.

The revised content is as follows: "Drought is widely recognized as a prolonged period of below-average precipitation, leading to water scarcity and a significant reduction in land productivity. The term encompasses distinct phenomena with different onset and impacts: meteorological drought signals precipitation deficits, agricultural drought reflects soil moisture shortages affecting crops, hydrological drought manifests as reduced water resources with a time lag, and socioeconomic drought arises when water shortages affect human activities. Among these, meteorological and agricultural droughts are most relevant in arid and semi-arid regions and form the focus of this study."

"Wang [32] reported that drought intensity increased and then decreased from 1901 to 2014, while Tong [33] observed that nearly 80% of the region experienced intensified drought during 1980–2014. Li [34]attributed 57% of the long-term aridification trend to atmospheric circulation changes., whereas Cao [35] highlighted progressive drought intensification from 1982 to 2018 with clear seasonal differences. Other studies predicted worsening drought trends toward mid-century [36]. While these findings are valuable, most analyses rely on indices with inherent lag responses, limiting their ability to capture rapid drought dynamics. In recent years, numerous international studies have developed drought indices using single or dual-variable approaches based on SPI, SPEI, NDVI, soil moisture (SM), TCI, or their combinations [37,38]. While models such as SPEI-VCI and Vegetation Health Index (VHI) have been widely used, many suffer from limitations including time-lagged responses to precipitation anomalies, low spatial resolution [39], and limited effectiveness in detecting flash droughts [40], especially in arid regions like the Mongolian Plateau.

To address this gap, the present study introduces the Standardized Temperature–Vegetation Drought Index (STVDI), developed within a three-dimensional Euclidean space constructed from SPEI, TCI, and VCI. This formulation comprehensively integrates precipitation, evapotranspiration, temperature, and vegetation conditions while accounting for lag responses, thereby combining the strengths of meteorological and agricultural drought indices. The central hypothesis is that combining SPEI, TCI, and VCI in a Euclidean framework reduces lag effects and improves the capacity for near real-time drought detection compared to existing indices. The objectives of this study are therefore to: (1) validate the effectiveness of STVDI for drought monitoring on the MP;(2) analyze the spatiotemporal distribution and seasonal variations of drought on the MP from 2000 to 2021 at 1 km resolution;(3) examine drought propagation and lag characteristics based on STVDI; and (4) evaluate long-term drought trends and persistence using statistical approaches including the Hurst index. By achieving these objectives, this study seeks to provide both a practical drought monitoring tool and new theoretical insights into drought lag mechanisms in arid and semi-arid ecosystems."

Comments 5: Suggestion for the Materials and Methods

This section requires significant revision to ensure reproducibility and justify choices. Specifically:

Study Area Description (Page 3): Add more ecological and climatic details, such as dominant soil types or land use patterns, which influence drought propagation.
Data Processing (Page 4): The downscaling of precipitation data using the "proportional method" is not adequately described or cited. Provide equations or references to validate this approach. Similarly, justify the use of nearest-neighbor resampling for soil moisture data, as it may introduce errors.
STVDI Formulation (Pages 5–6): The transformation of SPEI to a 0–1 scale for the Euclidean space is not explained. SPEI typically ranges from -∞ to +∞, so how was it normalized? Without this, the vector magnitude (equation 9) may be mathematically flawed. Clarify this step and justify the choice of Euclidean distance over other metrics (e.g., Mahalanobis distance).
Classification Scheme (Table 1): The drought levels are based on national standards but lack validation against ground truth or historical events. Include a confusion matrix or error analysis to support the thresholds.
Validation Methods (Pages 6–7): The cross-correlation function (CCF) is applied, but the interpretation of lag days (e.g., negative values in Table 2) is confusing. Define lag direction clearly (e.g., positive lag means STVDI responds after precipitation) and justify the ±3-month window. Also, ensure that the Nash-Sutcliffe efficiency (NSE) is used appropriately, as it is typically for model output comparison, not index validation.

Response 3: Thank you for constructive and helpful suggestions. The following are the responses and modifications to each suggestion.

Response Study Area Description (Page 3): The research area has been modified according to your suggestions. The modifications are as follows: "The MP (87°43′–126°04′E, 37°22′–53°20′N) is an inland plateau in Asia. The total area of the study region is ~2,747,116 km2, comprising 1,183,000 km2 of Inner Mongolia and 1,564,116 km2 of Mongolia (Figure 1). Mountains dominate the northwest, hills occupy central and eastern regions, and the Gobi Desert spans the southwest, with an average elevation of 1,580 m. Vegetation shows a north–south gradient from forest and forest-steppe to typical steppe, desert steppe, and Gobi Desert. Soil types vary accordingly, including chestnut and dark brown soils in steppe areas, meadow soils in forest-steppe zones, and sandy or saline soils in deserts, influencing water retention and drought propagation. The climate transitions from subhumid zones in the north and east to semi-arid, arid, and hyper-arid zones in the southwest. Annual precipitation ranges from 250 to 450 mm, decreasing north to south and east to west, while high evapotranspiration in arid zones exacerbates drought severity. Land use patterns further shape vulnerability: northern areas support forestry and cropland, central and eastern regions are dominated by grazing, and overgrazing in steppe and desert-steppe zones reduces vegetation resilience. The Gobi region is sparsely populated but ecologically fragile, with sandy soils and limited water heightening drought risks. The interactions among topography, soil types, vegetation cover, climate, and land use drive the spatial heterogeneity of drought occurrence and propagation across the MP."

Response Data Processing (Page 4): We have added a detailed description and operational details of "proportional method" to help readers understand it precisely. We have also cited directly relevant literature to support it. More details of the revision can be seen in section 2.2.2.

Response STVDI Formulation (Pages 5-6): Regarding your question about SPEI normalization, we have illustrated it and we explained the reason for choosing Euclidean distance as follows: Euclidean distance was chosen to calculate the STVDI because it provides a simple and intuitive measure of the combined drought severity in a normalized three-dimensional space. After min–max normalization, SPEI, TCI, and VCI all range from 0 to 1, so the Euclidean distance effectively captures the overall magnitude of drought at each pixel. Alternative metrics, such as Mahalanobis distance, consider correlations between variables, but estimating covariance matrices for each pixel across large spatial datasets can be unstable and computationally intensive. In contrast, Euclidean distance is computationally efficient, robust for large-scale spatial analysis, and allows direct interpretation of the combined drought magnitude. In order to make this clearly, we revised the related description in section 2.3.1.

Response Classification Scheme (Table 1): To validate the drought level thresholds of the proposed STVDI, we compared the STVDI-based classification results against the Global SPEI Database (version 2.08) over the Mongolian Plateau during the drought-prone year 2007, which has well-documented severe drought events. We conducted a pixel-by-pixel comparison (randomly selecting 7,000-pixel points) and constructed a five-category confusion matrix (Table 1). The validation results show an overall classification accuracy of 78.6%, demonstrating a substantial agreement between the STVDI and SPEI-derived drought intensity levels. This supports the reliability of the drought threshold values derived from national standards and confirms STVDI’s effectiveness in capturing real drought severity patterns.

We introduced historical drought events to verify the accuracy of the drought (STVDI) classification. We found that the severe drought events mainly occurred in 2003, 2005, 2007, and 2017. STVDI can well reflect the occurrence of these severe drought events (Figure 1).

"The severe drought events primarily occurred in 2003, 2005, 2007, and 2017 [35,57], and the STVDI effectively captures the occurrence of these events."

Figure 1 The interannual variation of STVDI and the comparison with historical drought events during the period from 2000 to 2021.

Table 2 Error matrix of drought index (STVDI) in Mongolian Plateau in 2007

		Evaluation results (STVDI)
	Drought levels	No drought	Mild	Moderate	Severe	Extreme
Filed observed Samples (SPEI)	No drought	1823	91	28	9	1
	Mild	109	1027	153	91	5
	Moderate	42	104	796	196	26
	Severe	9	36	215	894	164
	Extreme	2	3	43	168	965

Response Validation Methods (Pages 6-7): In response to your question about the CCF, we provide the following explanation and have added an explanation of the correlation lag in section 2.3.6. We applied the cross-correlation function (CCF) to quantify the temporal relationship between the STVDI and precipitation or soil moisture (SM) at different lags. In this study, a positive lag indicates that the STVDI responds after the climate variable, meaning that the drought index reflects the delayed effect of past precipitation or soil moisture. Conversely, a negative lag indicates that the STVDI leads the climate variable, which is not typically observed in arid environments. A zero lag indicates synchronous variation. To capture the short-term memory effects of precipitation and soil moisture on drought conditions, we selected a lag window of ±3 months, which aligns with the typical seasonal response of soil moisture and vegetation. It is important to note that the Nash-Sutcliffe efficiency (NSE) is primarily used to evaluate the performance of hydrological models on observed data, rather than directly for evaluating drought indices. Therefore, we rely on the CCF and the corresponding maximum correlation coefficient to quantify the lagged response of the STVDI, while avoiding the use of the NSE for this.

The modifications are as follows: "In this study, a positive lag k > 0, indicates that STVDI responds after precipitation or soil moisture, while a negative lag k < 0, suggests that STVDI leads these variables. A lag of zero implies that the two variables change in synchrony. To capture the short-term memory of precipitation and soil moisture on drought evolution, we applied a ±3-month lag window, which corresponds to the typical seasonal response time of soil moisture and vegetation growth in semi-arid ecosystems such as the Mongolian Plateau. For this study, x was STVDI and y represented precipitation or SM."

Comments 6: Suggestion for the Results

The results are comprehensive but often presented without direct linkage to objectives. For example:

Figure 3 and 4: Show correlations with other indices but do not explain why STVDI performs better in certain regions. Add spatial context (e.g., why low correlation in northeast?).
Table 2 and Figure 9: Report lag days but lack statistical significance tests. Include p-values or confidence intervals to validate claims.
Drought Trends (Page 15): The Sen’s slope and Hurst index analysis is well-presented, but the combination for future trends (Table 4) is not clearly explained. Define the categories (e.g., "+ -" meaning) in the text or caption. Ensure all figures have high-resolution maps with clear legends and scales, and refer to them in the text to guide the reader.

Response 6: Thank you for your suggestions. The following are the responses and modifications to each suggestion.

Response Study Area Description (Page 3): We have made some changes to the article to address this suggestion. The modifications are as follows: "Notably, both the median and the lower quartile of VCI exhibited a significant decline. This pattern reflects the spatial heterogeneity of drought responses across the MP: the south-central and western grassland regions show strong correlations because precipitation variability directly constrains soil moisture and vegetation activity, whereas the northeastern and southern areas—characterized by denser forest cover, wetter climatic conditions, and human disturbances such as irrigation—display weaker correlations between STVDI and VCI. These contextual factors explain the relatively low performance of STVDI in those regions (Figure 3c)."

Response Table 2 and Figure 9: We thank the reviewer for pointing out the need for statistical significance testing. In the revised manuscript, we calculated p-values and 95% confidence intervals for the reported lagged correlations. The results confirmed that most correlations were statistically significant (p < 0.05), particularly in drought-prone regions; however, non-significant correlations were primarily observed in Northeast China, consistent with the weaker vegetation response in that region.

Response Drought Trends (Page 15): We have revised Table 4 to provide a clearer definition of the combined categories of Sen’s slope and Hurst index. It can be seen from Table 4 that "+-" indicates that the value of Sen's slope is greater than 0 and the Hurst value is also greater than 0. Similarly, in the revised Table 4, we can clearly see the meanings of "++", "-+" and "--".

Sen’s slope (STVDI-12slope)

Hurst index > 0.5

Hurst index < 0.5

> 0

﹢﹢ Continuous

mitigation

﹢﹣ From mitigation to

aggravation

< 0

﹣﹢ Continuous

aggravation

﹣﹣ From aggravation to

mitigation

Comments 7: Suggestion for the Discussion

The discussion effectively interprets results in the context of existing literature but overlooks key limitations and counter-intuitive findings. For instance:

Lag Response (Page 17): The claim that STVDI has "minimal lag" is based on average values, but Figure 9 shows regional variations (e.g., positive lags in some areas). Explain these discrepancies—could they be due to data quality or ecological factors?
Flash Droughts (Page 18): The assertion that over 88% of areas exhibit "flash drought" susceptibility is overstated without defining flash drought criteria or linking to specific events. Differentiate between susceptibility and actual occurrence.
Limitations (Page 18): Acknowledged but superficial. Expand on how precipitation downscaling uncertainties or MODIS product limitations (e.g., cloud cover) might affect STVDI. Also, discuss the potential overfitting of the Euclidean model.

Response 7: Thank you for your comments. The following are the responses and modifications to each suggestion.

Response Lag Response (Page 17): Based on your suggestion, we have added a discussion about the positive values of the lag response in Section 4.1.

"However, regional variations are evident in Figure 9, where some areas exhibit positive lag values. These discrepancies can be attributed to several factors: (1) residual soil moisture and local ecological conditions, such as vegetation type, soil texture, and land cover, which modulate the speed at which drought conditions propagate; (2) heterogeneity in precipitation and soil moisture distribution, which can delay the local response of vegetation; and (3) data quality and measurement uncertainty in remote sensing and meteorological datasets. Despite these localized variations, the overall lag remains minimal, and STVDI captures drought dynamics effectively across most regions. Additionally, the STVDI-12 timescale is more stable (STVDICV = 8.21%) because annual aggregation smooths out high-frequency fluctuations [52]."

Response Flash Droughts (Page 18): The modifications are as follows: "Regarding rapid-onset droughts, our analysis indicates that over 88% of the MP exhibits near-zero lag between STVDI and both soil moisture and precipitation, reflecting highly synchronized drought development. It is important to clarify that this finding represents potential susceptibility to rapid-onset (flash) drought events rather than actual occurrences. Flash droughts are typically defined based on specific criteria such as rapid soil moisture decline or vegetation stress over a short period (e.g., 5–30 days) [58,59]. Thus, while the near-zero lag highlights regions where drought signals can develop quickly, additional event-based validation would be required to confirm actual flash drought events. The observed average lag time of 0.52 days further emphasizes the rapid propagation of drought signals in these susceptible regions."

Response Limitations (Page 18): Based on your suggestion, we have updated the content of the Limitation section. The updated content is as follows: "However, precipitation downscaling inevitably introduces uncertainties, particularly in regions with sparse observation networks, where interpolation errors or parameter selection may propagate into STVDI calculations. Future efforts should include cross-validation with independent ground-based measurements to quantify these uncertainties. Second, although the SPEI, VHI, Pre, and SM indices largely reflect the dry and wet conditions of the surface, they are insufficient to comprehensively describe the drought condition. The MODIS products used in STVDI construction also have inherent limitations, such as cloud contamination, gaps in high-latitude winter retrievals, and NDVI saturation in dense vegetation canopies. These limitations may weaken the reliability of STVDI in certain regions or seasons, suggesting the need for blending with alternative remote sensing products (e.g., Sentinel or VIIRS). Drought in the study area is closely associated with climate change, cyclone circulation patterns, and high-temperature heatwave events. Finally, the Euclidean distance framework effectively integrates multiple drought-related variables, but it may risk overfitting if too many indicators are included or their weights are unbalanced. This could inflate correlations with reference indices. Validation and sensitivity testing are therefore essential to ensure robustness. Consequently, there is a need to propose an effective approach to rigorously assess the newly developed drought index, accounting for both input data limitations and methodological uncertainties."

Comments 8: Suggestion for the Conclusion

Response 8: Thank you for your suggestion. Based on your suggestion, we have updated the content of the Conclusion section. The updated content is as follows: "Nearly 90% of the Mongolian Plateau exhibited near-instant drought responses, suggesting a potential susceptibility to rapid-onset drought events. However, it should be noted that this conclusion is based on index-based lag analysis, and further validation against ground observations or historical drought records is required. The proposed multi-scale composite drought index (STVDI) enables timely and dynamic drought detection with less lag (0.52 days on average) and shows strong spatiotemporal consistency with established drought indices. It demonstrates stable performance across multiple timescales, capturing both short-term meteorological droughts and longer-term agricultural droughts. Monthly STVDI captures distinct seasonal drought variations on the MP, with events predominantly occurring in spring and summer. On the annual scale, drought conditions from 2000 to 2021 remained relatively mild and stable, with a slight overall alleviation trend. Spatially, drought intensified in some southeastern areas, and projections suggest that 47% of the region may experience worsening drought in the future, with 21% moving from mitigation to deterioration. It is important to note that the southwestern region of MP exhibits relatively mild drought conditions during the autumn and winter. The 1-km monthly STVDI dataset developed in this study offers a useful reference for regional drought monitoring and climate impact assessment, but its broader application requires validation against independent ground data and comparison with historical drought events. Future work should focus on integrating higher-resolution climate and remote sensing datasets, applying the STVDI framework to other drought-prone regions, and testing its robustness under different climate scenarios."

Comments 9: Suggestion for the References

Response 9: Thank you for your suggestions. We have updated the format of the relevant references and added three more related references.

[38]. Bento, V. A., Gouveia, C. M., DaCamara, C. C., & Trigo, I. F. A climatological assessment of drought impact on vegetation health index. Agricultural and forest meteorology. 2018, 259, 286-295.

[47]. Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., ... & Xia, G. Monitoring the vegetation activity in China using vegetation health indices. Agricultural and forest meteorology. 2018, 248, 215-227.

[48]. Bento, V. A., Gouveia, C. M., DaCamara, C. C., Libonati, R., & Trigo, I. F. The roles of NDVI and Land Surface Temperature when using the Vegetation Health Index over dry regions. Global and Planetary Change. 2020, 190, 103198.

Comments: Comments on the Quality of English Language

The manuscript has several grammatical errors and awkward phrasings that affect clarity. Examples:

Page 1, Highlights: "most of the MP faced a high risk of frequent flash droughts" → "most of the MP faces a high risk of frequent flash droughts."
Page 2, Abstract: "limits their ability to fully discovery the drought features" → "limiting their ability to fully discover drought features."
Page 6, Section 2.3.1: "The line DW represents the dry and wet edges" → "The line DW represents the dry-wet gradient." Recommend thorough proofreading to improve flow and academic tone. Use active voice where possible (e.g., "we developed" instead of "a STVDI dataset was developed").

Response: Thank you for your helpful comments on the quality of language. We made the required modifications above mentioned. Meanwhile, we have optimized the language of the entire text as a whole.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All my comments have been adequately addressed, and as far as I am concerned, the manuscript is now suitable for acceptance for publication in Remote Sensing.

Author Response

We sincerely thank the reviewer for the positive evaluation and valuable comments throughout the review process. We are very grateful for your time and constructive feedback, which have greatly improved the quality and clarity of our manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

The revised manuscript shows substantial improvement but still requires further refinement. First, the description of lag analysis remains inconsistent: the reported “average lag of 0.52 days” is derived from monthly-scale data(?, seems still lacks methodological rigor) . The unit should be unified or the conversion method clearly explained. Second, independent validation is still insufficient, relying only on SPEI and historical events (comparison with ERA5-Land soil moisture or crop yield data is recommended to strengthen reliability). Third, the argument for innovation remains weak (the claimed “low-lag” advantage should be quantitatively demonstrated through significant improvements over TVPDI, TVMDI, and TSWDI if possible). In addition, several figures and tables still contain inconsistent abbreviations, unit confusion, and labeling issues that require systematic correction. Finally, statements in the conclusion such as “near-instantaneous response” remain overstated; these should be rephrased as “potential susceptibility” and accompanied by clarification on the need for event-based validation. Overall, while the structure and content have improved, targeted revisions addressing these issues are necessary to enhance methodological rigor and result reliability.

Author Response

Response to Reviewer 2

Dear Reviewer:

We highly appreciate the reviewer’s recognition of the improvements made in the previous revision, as well as the valuable suggestions for further refinement regarding methodological rigor, data validation, and clarity of presentation. We have carefully addressed each of your comments to enhance the methodological soundness, clarity, and overall quality of the manuscript. Detailed point-by-point responses are provided below.

Comments 1: First, the description of lag analysis remains inconsistent: the reported “average lag of 0.52 days” is derived from monthly-scale data(?, seems still lacks methodological rigor). The unit should be unified or the conversion method clearly explained.

Response 1: Thank you for your suggestion. To make this result more rigorous, we have added a 0.52 days conversion explanation (monthly data are converted to daily values by assuming a 30-day month) in Figure 9, as shown below.

"Figure 9. Spatial distribution of drought lag response (±3 months) between STVDI and Pre (a)-–(d), SM (e)–(h). The lag days are calculated on the basis of a 30-day month."

Comments 2: Second, independent validation is still insufficient, relying only on SPEI and historical events (comparison with ERA5-Land soil moisture or crop yield data is recommended to strengthen reliability).

Response 2: Thank you for your comments. We added independently verification of the linear correlation (r²) between STVDI and soil moisture in 2007 and 2017. Soil moisture data were derived from the monthly ERA5-Land dataset (layer 1, 0–7 cm depth) accessed via Google Earth Engine. The results of the analysis are presented in Table 1.

Table 1 The linear correlation (r²) between STVDI and soil moisture

	Jan.	Feb.	Mar.	Apr.	May	Jun.	Jul.	Aug.	Sep.	Oct.	Nov.	Dec.
2007_r²	0.839	0.811	0.846	0.885	0.806	0.817	0.888	0.851	0.796	0.841	0.796	0.787
2017_r²	0.876	0.757	0.768	0.852	0.847	0.881	0.813	0.825	0.843	0.851	0.866	0.794

The description of the independent verification result has been incorporated following the historical event validation (revised details can be seen in section 3.2.2).

"The severe drought events primarily occurred in 2003, 2005, 2007, and 2017, and the STVDI effectively captures the occurrence of these events. Furthermore, the independent validation of the monthly ERA5-Land soil moisture and STVDI revealed an average linear correlation coefficient (r²) of 0.83 between two variables during 2007 and 2017."

Comments 3: Third, the argument for innovation remains weak (the claimed “low-lag” advantage should be quantitatively demonstrated through significant improvements over TVPDI, TVMDI, and TSWDI if possible).

Response 3: Thank you for your suggestions. To highlight the "low-lag" advantage of STVDI, in "Discussion", we have added a comparison of the lag effect between STVDI and other drought indices, as well as relevant literature.

"Compared with traditional drought indices such as SPEI, which typically lag behind by 1 to 3 months in capturing drought responses [70], STVDI demonstrates a more immediate response (0.52 days on average). The relative precipitation lag area of STVDI is less than 3% (Temperature-SIF-Water Balance Dryness Index, TWSDI is about 11%) [71], shows superior low-lag performance."

Reference:

Wei, W.; Liu, T.; Zhou, L.; Wang, J.; Yan, P.; Xie, B.; Zhou, J. Drought-Related Spatiotemporal Cumulative and Time-Lag Effects on Terrestrial Vegetation across China. Remote Sens. 2023, 15, 4362. https://doi.org/10.3390/rs15184362
Liu, Y., Yu, X., Dang, C., Yue, H., Wang, X., Niu, H., ... & Cao, M. A dryness index TSWDI based on land surface temperature, sun-induced chlorophyll fluorescence, and water balance. ISPRS Journal of Photogrammetry and Remote Sensing. 2023, 202, 581-598.

Comments 4: In addition, several figures and tables still contain inconsistent abbreviations, unit confusion, and labeling issues that require systematic correction.

Response 4: Sorry for these mistakes. We have reviewed all the figures and tables and corrected the relevant errors (updated Figure 7, Figure 9, and Table 4).

Comments 5: Finally, statements in the conclusion such as “near-instantaneous response” remain overstated; these should be rephrased as “potential susceptibility” and accompanied by clarification on the need for event-based validation.

Response 5: Thank you for suggestions. We have modified the expression "near-instantaneous response" to "potential susceptibility", and checked all the manuscript to avoid overstated situation.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Second-round review report on ‘A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Features on the Mongolian Plateau’

General Comments

The authors have done a really solid job in revising the manuscript. The improvements in the Introduction, Methods, and Discussion are particularly strong and have significantly boosted the paper's rigor. I believe the manuscript is now much closer to publication. However, for it to be fully replicable, I think we need to tie up a few loose ends in the methodology. The issues aren't major, but they are crucial for clarity. Specifically, the description of the SPEI normalization and the data resampling choices needs a final polish. Once these are clarified, I am confident the manuscript will be ready for acceptance. Following are specifics:

Specific Comments

2.5 Suggestion for the Materials and Methods

I'm quite happy with the extensive revisions here, but I feel we're not quite over the finish line on methodological transparency. My main lingering concern is that a reader trying to replicate your STVDI calculation might get tripped up. You mention normalizing SPEI, which is great, but I don't see this critical step explicitly reflected in Equation (9). The equation just says "SPEI," which could be misinterpreted. How about revising the text to clearly state that the equation uses the normalized values of SPEI, TCI, and VCI? That would seal the deal on reproducibility. Also, while you detailed the precipitation downscaling, you skipped justifying why you used nearest-neighbor resampling for soil moisture. For a continuous variable like soil moisture, I don't think nearest-neighbor is the best choice as it can create blocky patterns; a quick sentence explaining this choice—or even better, switching to a bilinear method—would strengthen the data foundation. Finally, a small but helpful suggestion: a single, consolidated table listing all your input datasets would be a fantastic quick-reference for readers and would make the data section perfectly clear.

Author Response

Response to Reviewer 3

Dear Reviewer:

We sincerely appreciate your careful re-evaluation of our revised manuscript and your insightful comments that helped us further improve its methodological clarity and data transparency. We have carefully revised the manuscript according to your suggestions, and a detailed point-by-point response is provided below.

Comments 1: My main lingering concern is that a reader trying to replicate your STVDI calculation might get tripped up. You mention normalizing SPEI, which is great, but I don't see this critical step explicitly reflected in Equation (9). The equation just says "SPEI," which could be misinterpreted. How about revising the text to clearly state that the equation uses the normalized values of SPEI, TCI, and VCI?

Response 1: Thank you for your suggestion. We have added instructions on the standardized values of SPEI, TCI and VCI that are used in section 2.3.1, equation (9) above. The specific text is as follows.

"The STVDI was obtained through the following formula, where SPEI, TCI and VCI are all normalized values."

Comments 2: Also, while you detailed the precipitation downscaling, you skipped justifying why you used nearest-neighbor resampling for soil moisture. For a continuous variable like soil moisture, I don't think nearest-neighbor is the best choice as it can create blocky patterns; a quick sentence explaining this choice—or even better, switching to a bilinear method—would strengthen the data foundation.

Response 2: Thank you for your comments. We have added the reasons for selecting nearest-neighbor resampling in "2.2.2. Meteorological data", also added relevant references. The specific modification contents are as follows.

"Nearest neighbor resampling is adopted to enhance computational efficiency and avoid non-physical values (e.g., negative soil moisture) that may occur when using bi-linear interpolation in regions with strong spatial heterogeneity [44]."

Reference:

Sharma, V., Kilic, A., & Irmak, S. Impact of scale/resolution on evapotranspiration from Landsat and MODIS images. Water Resources Research. 2016, 52(3), 1800-1819.

Comments 3: Finally, a small but helpful suggestion: a single, consolidated table listing all your input datasets would be a fantastic quick-reference for readers and would make the data section perfectly clear.

Response 3: Thank you for your constructive suggestion. We have added a brief data sources and description table in "2.2. Data and Processing". The specific modified table is shown in Table 1 below.

Table 1. Data sources and description.

Data	Data description	Data sources	Data use
MOD11A2 (LST)	1 km, 8-day land surface temperature	NASA Earth Observation System Data Gateway	Calculate the Temperature Condition Index
MOD13Q1 (NDVI)	250 m, 16-day normalized difference vegetation index	NASA Earth Observation System Data Gateway	Calculate the Vegetation Condition Index
MOD16A2 (PET)	500 m, 8-day potential evapotranspiration	NASA Earth Observation System Data Gateway	Calculate the Standardized Precipitation Evapotranspiration Index
CRU TS v4.06 (Precipitation)	0.5°×0.5°, monthly precipitation	Centre for Environmental Data Analysis (CEDA), Climatic Research Unit (CRU)	Lag analysis
ERA5-Land (Soil Moisture)	0.1°×0.1°, monthly volumetric soil water in layer 1 (0–7 cm)	European Centre for Medium-Range Weather Forecasts (ECMWF)	Independent data for STVDI validation; lag analysis
Global gridded SPEI database	0.5°×0.5°, monthly global NetCDF data	https://digital.csic.es/handle/10261/268088	Independent data for STVDI validation
Topographic Data (DEM, slope, aspect)	1 km spatial resolution	Derived from SRTM digital Elevation Model	Precipitation downscaling

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A Novel Composite Drought Index with Low Lag Response for Monitoring Drought Features on the Mongolian Plateau

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