Daily Drought Prediction in the Huaihe River Basin Using VMD-informer-LSTM

Li, Min; Ou, Ming; Yao, Yuhang; Yin, Changman

doi:10.5194/egusphere-2025-5645

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https://doi.org/10.5194/egusphere-2025-5645

Preprints

23 Dec 2025

| 23 Dec 2025

Daily Drought Prediction in the Huaihe River Basin Using VMD-informer-LSTM

Min Li, Ming Ou, Yuhang Yao, and Changman Yin

Abstract. Accurate drought prediction is a key challenge in water resource management and agricultural planning. This study proposes a novel drought prediction framework that integrates Variational Mode Decomposition (VMD), Informer, and Long Short-Term Memory (LSTM) networks to enhance hydrological drought forecasting in the Huaihe River Basin, China. The VMD-Informer-LSTM model decomposes complex non-stationary drought sequences into multi-scale components, effectively extracting long-term trends and short-term fluctuations. Results show that the model outperforms LSTM, Transformer-LSTM, and Informer-LSTM, improving R², RMSE, MAE, and MAPE by 28.4 %, 46.2 %, 46.5 %, and 50.8 %, respectively, over the baseline LSTM. When the prediction period is 30 days, the VMD-Informer-LSTM achieves the highest prediction accuracy. During the 120–180 day prediction period, the prediction accuracy of all models declines, with drought intensity generally underestimated. Misclassifications are mainly concentrated in the transition zones between humid and semi-humid regions, with higher error frequency in semi-humid areas. Prediction accuracy is highest in the upstream and downstream regions, followed by the Yishuisi River Basin, while the midstream region performs poorly due to human interference. Shapley Additive Explanations (SHAP) further reveal that precipitation and temperature are the dominant meteorological drivers, jointly accounting for nearly half of the model’s predictive power. These results confirm that the VMD-Informer-LSTM provides the most accurate predictions among the tested models, offering valuable support for drought risk assessment and water resource management in the Huaihe River Basin and other similar regions.

Received: 14 Nov 2025 – Discussion started: 23 Dec 2025

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Min Li, Ming Ou, Yuhang Yao, and Changman Yin

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-5645', Anonymous Referee #1, 29 Dec 2025
This paper presents an innovative hybrid model combining VMD, Informer, and LSTM for daily drought prediction in the Huaihe River Basin. The methodological approach is innovative, integrating signal decomposition with advanced deep learning architectures for drought prediction. However, the manuscript has several critical issues related to experiment design, methodological justification, and interpretation of results, which must be addressed before it can be considered for publication.
The study is fundamentally built on the DEDI index, derived from ERA5 reanalysis-based actual evapotranspiration (AET). This choice introduces a significant, and largely unacknowledged, source of bias and uncertainty. The ERA5 model, like most land surface models, does not explicitly represent irrigation, a dominant human activity in the agriculturally intensive Huaihe River Basin. This omission likely leads to a systematic overestimation of drought severity during growing seasons.

The most severe shortcoming is the complete absence of validation against independent observational data.The entire modeling pipeline—from DEDI calculation to model training and evaluation—operates within the only ERA5 reanalysis. There is no validation against independent observational data (e.g., in-situ soil moisture, streamflow, reservoir levels, or satellite-based drought indices like SMAP soil moisture or GRACE terrestrial water storage). Consequently, the reported high performance may indicate skillful fitting to the internal structures of the reanalysis product rather than a predictive capability for real-world drought events.

The paper reports predictive skill at lead times (up to 180 days) that far exceed the theoretical limit of deterministic weather prediction (~2 weeks), yet fails to provide a coherent physical rationale. It is unclear what physical memory mechanisms the model is leveraging for predictions at 30-180 days. Is it capturing real hydrological memory (e.g., from deep soil moisture, groundwater) or merely fitting to statistical autocorrelation and the annual cycle?

The critical parameters for VMD—the number of modes (K) and the penalty factor (α)—are not justified. How were they determined (e.g., using center frequency observations, energy loss ratio)? Their arbitrary selection affects all subsequent analysis. The architecture of the "dual-branch parallel" Informer-LSTM fusion is described only at a high level. How exactly are the outputs from the Informer (global trends) and LSTM (local dynamics) combined (e.g., concatenation, weighted averaging)? A detailed diagram or formula is needed.

The current discussion is primarily a restatement of results.It must be expanded to include:Strengths of the proposed approach,Limitations and Weaknesses and A clearer illustration of the forecast value and potential applications.

Specific Comments
Figure 5: The x-axis label should be changed from "Time" or sample number to a standard calendar date format (e.g., YYYY-MM or YYYY).

Figure 7 and 8: As noted, these figures convey largely redundant information (scatter plots and line charts of the same 180-day predictions). Consider consolidating them into a single, more informative multi-panel figure or moving one to the supplementary material.

Ensure all figure captions are descriptive and consistent, following the format of Figs. 1, 5, etc.
Citation: https://doi.org/10.5194/egusphere-2025-5645-RC1
RC2:
'Comment on egusphere-2025-5645', Anonymous Referee #2, 20 Jan 2026
The authors present an innovative framework integrating Variational Mode Decomposition (VMD), Informer, and Long Short-Term Memory (LSTM) networks for long-term drought prediction in the Huaihe River Basin, China. The manuscript is well-structured, providing a clear explanation of the models and the indices used. The inclusion of SHAP (SHapley Additive exPlanations) to evaluate decision-making—identifying precipitation and temperature as dominant factors—adds significant value to the interpretability of the model.
However, several critical methodological concerns and limitations regarding data dependency and spatial representation must be addressed before the manuscript can be considered for publication.
Major Revisions
The model is trained and validated exclusively using ERA5 reanalysis data, including the DEDI index, which is itself formulated from ERA5 variables. This presents a significant limitation: the model may simply be learning the internal mathematical structure of the ERA5 atmospheric model rather than actual drought dynamics.
The authors must discuss how this dependency might amplify inherent biases within ERA5. For the results to be credible for public policy or operational use, the framework should be validated against observed in-situ station data to prove its real-world reliability.
Methodological Clarity:
Point Selection: The process for the "systematic selection" of the 108 control points is unclear. The authors should explicitly define the criteria or algorithms used to ensure these points are representative of the basin’s hydro-climatic diversity.

Spatial Connectivity: It is not specified whether these 108 grid points are treated as independent time series or if the model accounts for spatial interconnectivity. If the model treats them as isolated units, it ignores the spatial propagation of drought—a critical limitation that adds uncertainty to the findings.

It is recommended that a workflow be incorporated in order to facilitate a better comprehension of the methodology.

Model Robustness and Generalization: To demonstrate the model’s true forecasting potential, the following points should be addressed:
Evaluate the model’s response to significant historical drought events that were excluded from the calibration period. This is essential to assess performance under extreme, "out-of-sample" conditions.

Provide a technical justification for using the DEDI index derived from ERA5 data instead of more physically robust or internationally standardized methods, such as the FAO-56 Penman-Monteith equations for evapotranspiration.

Justify why other high-resolution satellite or hybrid datasets (e.g., AgERA5, CHIRPS, IMERG, or MODIS) were not used to provide a more robust benchmarking of the results.

The current framework seems to overlook terrain characteristics (topography, land cover, soil type). Due to the resolution used, a single pixel may cover multiple climatic zones or land uses. The authors should discuss how this loss of sub-grid heterogeneity limits the model's accuracy in representing spatial drought reality.
Minor Revisions
Figures 7 and 9 are currently non-intuitive. I recommend incorporating more descriptive labels or legends within the images and expanding the captions to ensure they are self-explanatory for the reader.

Please provide a brief but detailed summary of the training/validation/testing split and the hyperparameter tuning process to ensure reproducibility.
Citation: https://doi.org/10.5194/egusphere-2025-5645-RC2
RC3:
'Comment on egusphere-2025-5645', Anonymous Referee #3, 28 Jan 2026
This manuscript proposes a hybrid VMD–Informer–LSTM framework for daily hydrological drought prediction in the Huaihe River Basin using the DEDI index derived from ERA5. The topic is relevant, and the combination of signal decomposition and deep learning architectures is timely. The authors present extensive experiments across different lead times and regions, and the results indicate consistent improvements over benchmark models. However, the manuscript needs further revision before it can be published.
The VMD step is critical to the model’s performance, yet important details are missing:

How was the number of modes (K) selected?
Were K and the penalty parameter α fixed for all grid points, or tuned adaptively?
Was sensitivity analysis performed to assess how VMD parameters affect prediction skill?
The manuscript does not clearly describe: The train/validation/test split strategy (temporal split vs. random split). Whether hyperparameters were tuned using an independent validation set. And was the model tested on unseen data?

Did the author use in-situ measurements? What does “Observed data” mean, is it in-situ measurements? If so, please clarify the data source.
Discussion is a mixed of map results and its discussion. Please put the maps result to Section results, and expand the discussion content.

Minor comments
Some figures (e.g. Figs. 7–9) are information-dense and difficult to read at journal scale. Consider simplifying or merging panels.

In line 170, please explain why 0.25°?

In line 317, please explain “an average” refer to average of what.

In Fig 7, please explain what are x-axis and y-axis values for.

Figure 11 should use same color with different gradients for three columns. It’s more comparable.
Citation: https://doi.org/10.5194/egusphere-2025-5645-RC3

Min Li, Ming Ou, Yuhang Yao, and Changman Yin

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Short summary

This study examines the risk of possible water shortages in the Huaihe River Basin and explores whether dry periods can be predicted more reliably. Using decades of daily environmental records, we built a model that separates complex signals into simpler parts and learns their patterns. It forecasts dry conditions more accurately than earlier methods and performs well across regions and time spans. The findings can help the basin prepare for potential water shortages and support better planning.


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