Greetings! This study presents PLSTM-Reg v1.0, a regional physics-encoded LSTM framework that effectively simulates reservoir operations by integrating mass-balance constraints with deep learning. The methodology demonstrates strong spatial and temporal generalizability, particularly through its synergy with remote sensing data. Overall, I think this is a very high-quality, well-organized study that addresses a critical challenge in hydrological modeling, which fully meets the requirements for publication in Geoscientific Model Development (GMD).

I have two minor suggestions. First, I recommend that the authors move the descriptions of static attributes to the main text, as they are essential parts of the model structure. Second, I would like to see a little discussion on which of these attributes have the most important impacts on the model results and which are less so.

I also have three technical questions that I hope the authors could clarify to improve reproducibility. 1) What is the specific sequence length used for the training and testing of the sequence-to-sequence regional model? 2) Why did the authors choose a recursive sequence-to-one model instead of a direct sequence-to-sequence approach (e.g., outputting a 7-day vector) for the 7-day lead time forecast? 3) Did the authors encounter gradient vanishing or truncation issues, given that the hard physical constraints in the PLSTM cell might truncate the gradient flow during backpropagation?



Best

Ningpeng Dong

The manuscript addresses a critical challenge in large-scale hydrological modeling: the generalization of reservoir operation rules to data-scarce regions. The authors propose PLSTM-Reg v1.0, a physics-encoded Long Short-Term Memory (LSTM) model that integrates reservoir storage directly into the LSTM cell to enforce mass conservation. The model is evaluated using 256 reservoirs across the Continental United States (CONUS) through five distinct experimental setups designed to test temporal generalization, spatial generalization, and historical data reconstruction. Overall, the manuscript presents an innovative approach to reservoir modeling. The initial results are promising; however, there are several major concerns regarding the experimental logic, the clarity of the evaluation metrics, and the positioning of the work within the existing literature that need to be addressed.

Major comments

1. Logical Consistency in Experiment Comparison (IV vs. V): The authors conclude that incorporating remotely sensed (RS) surface area leads to superior model performance compared to local models (mentioned in places like lines 23-26 and lines 313-343). However, the logic underpinning this comparison in Table 1 is not fully sound.

Specifically, in Experiment IV, storage information is omitted. In Experiment V, remotely sensed surface area, which serves as a highly informative proxy for storage, is included. Given that Experiment V introduces a significant predictive variable that Experiment IV lacks, it is not a safe conclusion that V would outperform IV because of adding RS information. This comparison does not necessarily prove the superiority of RS integration in reconstructing historical records; rather, it highlights the value of the extra information provided. To make this claim robust, the authors must clarify if the “reconstruction” baseline is intended to represent a scenario where NO storage records exist. If storage data were available and utilized in IV, would the RS approach still be superior?

1. Justification of Model Input for Long-Term Simulations: Following the point above, why is storage omitted from the long-term simulation setups? In many operational contexts, historical storage records (even if fragmented) are the primary benchmark. The authors should justify the decision to exclude storage in these specific experiments or discuss the implications of this omission for the model's practical utility.

1. Review of Relevant Literature: The literature review (Lines 58-72) summarizes recent regionalization efforts (e.g., Turner et al., 2021; Steyaert et al., 2025) but misses several foundational or recent studies that utilize machine learning and remote sensing for parameter generalization. Including the following would provide a more comprehensive context:

• DZTR Model: Utilizing hydrological quantiles for straightforward generalization (Yassin et al., 2019).
• https://doi.org/10.5194/hess-23-3735-2019
• SBTS Model: Generalizing storage-based piecewise rules using ML and satellite observations (Shen et al., 2025).
• https://doi.org/10.1029/2024WR037620
• MODROM Model: A modular model using ML for generalization (Li & Villarini, 2026).
• https://doi.org/10.1029/2025MS005180

1. Clarity on Evaluation Metrics and Performance (Lines 214-229): The KGE scores presented in Figure 3 are exceptionally high, with medians near 1.0. This level of "perfection" suggests the evaluation might be influenced by the model's short-term configuration. Are these scores calculated on the training, testing, or overall dataset? If the model utilizes lagged information for next-day release predictions, the high performance is likely a reflection of persistence rather than operational rule learning. The authors need to provide a more detailed description of the evaluation: specifically, define the ground truth, the simulation period, and the exact lead time for the releases being scored.

Minor Comments

• Line 20: The term "local models" is used without a prior definition. Please provide a brief explanation at the first mention to ensure clarity for readers unfamiliar with the authors' specific terminology.
• Line 27: The hybrid nature of the model (Physics + DL) is a highlight of the work but is under-represented in the Abstract. Please mention the specific physical constraints (e.g., mass balance) earlier in the abstract to emphasize the "Physics-encoded" aspect.
• Line 110: Please add a formal citation for the Daymet dataset.
• Lines 119-120: When discussing "reconstructing records from remote sensing," please specify which variables are being reconstructed (e.g., inflow, storage, or release).
• Line 123: Regarding the use of SARAH-CONUS to supplement GRSAD: Did the authors perform a consistency check or bias correction between these two datasets? Please clarify if there were systematic differences and how they were handled.
• Line 183: The term "PLSTM-Loc" appears abruptly. Please define this counterpart model before using its abbreviation.
• Equations (1)-(3): There are inconsistencies in the fonts used for variables and operators. Please ensure all LaTeX formatting is uniform throughout the manuscript.

This manuscript develops PLSTM-Reg, a regional LSTM model trained and evaluated using long-term daily records from 256 reservoirs across the CONUS to simulate reservoir release and storage under temporal and spatial generalization settings. The study is valuable because it uses a high-quality reservoir dataset, considers both short-term and long-term simulation performances, designs multiple experiments to test transferability, and tests the added value of remote-sensing surface area. The manuscript is generally well organized. However, the novelty relative to recent pooled or transferable reservoir-operation models is fully stated, and several aspects of the validation require clarification, including possible information leakage from static attributes, and the fairness of local-model comparisons. Overall, I find the study promising and potentially useful for large-scale hydrologic modeling, but I recommend major revision before publication.

Major comments

1. Table S1 lists “Average discharge” as an input. This raises two concerns. First, it may partly explain the improvement of the regional model over local models, since local models do not use this static input. Providing an ablation excluding average discharge can be more convincing. Second, in the spatial-transfer experiment, average discharge for held-out reservoirs may create leakage. This would weaken the claim that the model is tested on truly “unseen” or data-scarce reservoirs.

2. The model seems to assume that reservoir operation depends only on local basin forcings, local inflow/storage, and reservoir attributes. However, many real reservoirs are operated as part of cascade reservoir systems. The authors should clarify whether such cases are included and whether PLSTM-Reg can handle coupled upstream–downstream operation (such as the flexibility to add upstream reservoir information as static input in current framework). If not, the application boundary should be stated more clearly.

3. The comparison with recent advances is incomplete. Comparing mainly with older rule-based models weakens the significance of the claimed innovation, especially because the key claim is multi-reservoir pooling/regional learning. The following studies should be compared with, or at least discussed:

Tran et al. (2025), “Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM.” This is a closed predecessor: a pooled/conditioned LSTM across nearly 200 CONUS reservoirs using static reservoir attributes.

The following three are cited in this work, but only as background. They should be discussed more directly against PLSTM-Reg as baselines:

• Ford and Sankarasubramanian (2023), “Generalizing reservoir operations using a piecewise classification and regression approach.”
• Turner et al. (2021), “Water storage and release policies for all large reservoirs of conterminous United States.”
• Chen et al. (2022), “Developing a generic data-driven reservoir operation model.”

4. The source of improvement should be interpreted more carefully. The gains over rule-based methods may come from both regional pooling and the flexible neural-network architecture. Simply attributing the improvement to the regional setting is not fully fair. The authors should distinguish the effects of regional training, static attributes, nonlinear LSTM architecture, and physical constraints.

Minor comments

• Line 14. “Representative reservoirs” needs clearer definition in the data section to avoid selection bias. Is data length the only selection criteria?
• Line 50. Previous models require water-demand data for parameterization, while this model does not use demand data but still performs well. The authors should explain why demand information may be implicitly captured, for example through storage, release history, seasonality, reservoir purpose, and static attributes.
• Line 124-126. It would be helpful to know the missing ratios of the remote sensing data.
• Line 165. The authors use the term “forecasting.” However, if future forcings/inflows are assumed to be known when running the model recursively, this is closer to conditional simulation or hindcast prediction than true operational forecasting. Please clarify.
• Line 176. Long-term simulation capability depends on the training window length. Longer sequence windows may help the model learn to avoid error accumulation. This does not require a new experiment, but it is worth mentioning in the discussion.
• Line 197. The authors should clarify how the monthly surface-area time series is used at daily steps. If the current month’s value is used for all days in that month, it may include future information for earlier days.
• Lines 227–229. It is interesting that release performance differs clearly between PLSTM-Reg and PLSTM-Loc, while storage performance is nearly the same. The authors should explain this.
• Figure 3. Since all statistical tests appear significant, the asterisks could be removed and the caption can simply state that all comparisons pass the significance test. Same for Figure 6.
• Lines 273–274. In the “unseen reservoir” experiment, it is unclear how PLSTM-Loc is trained. Does it use local data from the held-out reservoir, or random initial weights? This needs clarification.
• Line 280. The improvement over rule-based methods may come from both regional pooling and flexible black-box modeling. The manuscript should avoid attributing the gain only to regional learning.
• Figure 5. Please clarify why the metric values are not consistent with Figure S2. Do they correspond to different experimental settings or evaluation periods?
• Text S2. Rule-based benchmarks use default parameters, not calibrated ones. It does not necessarily imply that PLSTM-Reg outperforms these schemes.