| 24 Feb 2026
Human-Driven Runoff Decline and Hydrological Drought Intensification in Semi-arid Regions in the Last 40 Years Revealed by a Hybrid Physics-Deep Learning Framework
Abstract. Amid accelerating global warming, the intensification of hydrological drought in semi-arid regions has become a critical threat to water security. In this study, we present an integrated framework that couples a physics-based WRF-Hydro model with a deep-learning module (LSTM-Attention) for error correction and attribution analysis. Focusing on the Xilin River Basin, a representative semi-arid catchment, this approach quantifies the relative contributions of climate change and human activities to runoff variations across interannual and intra-annual scales over the 1980–2020 period. We further systematically assess the impact of human activities on hydrological drought. Results reveal a significant runoff declining trend of -23.79×10⁴ m³/a, with an abrupt shift in 2001. During the post-shift period (2001–2020), hydrological drought frequency surged from 7.54 % in the baseline period to 54.58 %. Rapid warming (0.5 °C/10a) caused sustained increase of potential evapotranspiration, while snow water equivalent decreased significantly at 1.27 mm/a; these dual effects drove the overall runoff decline. April exhibited the most pronounced runoff reduction, accounting for 58.87 % of the annual decrease. In contrast, March runoff increased, primarily due to earlier snowmelt triggered by climate warming. Attribution analysis indicates that human activities were the dominant driver of runoff decline, contributing 61.04 %. These activities exerted dual effects on hydrological drought: alleviating it in 29.58 % of months but intensifying or triggering events in 38.34 % of months. The proposed integrated framework offers a robust tool for hydrological attribution analysis and underscores the critical role of human activities in sustainable water resource management.
Title: Human-Driven Runoff Decline and Hydrological Drought Intensification in Semi-arid Regions in the Last 40 Years Revealed by a Hybrid Physics-Deep Learning Framework
The manuscript investigates runoff decline in semi-arid regions using a hybrid WRF-Hydro and LSTM-Attention framework. While the integration of physical modeling with error correction is interesting, several critical issues regarding data validation and methodology transparency must be addressed.
1.Scalability concerns
In distributed hydrological modeling, it is a standard and reasonable paradigm to conduct in-depth physical mechanism analysis within small or medium sized catchments. However, the authors are encouraged to supplement the discussion by clarifying to what extent the core findings can be generalized to other typical basins globally.
2.Validation of Downscaled Data
Line 102 mentions that dynamical downscaling techniques were applied to generate high-resolution meteorological forcing data. While downscaling increases spatial resolution, it does not inherently guarantee improved accuracy, and regional climate models are prone to systematic biases. The manuscript currently lacks a comparative validation between the 12.5 km WRF outputs and actual ground meteorological observations within the basin. Although an LSTM-Attention module is used for error correction, a rigorous physics based framework requires a quality assessment of the initial meteorological input sources.
3.Transparency of WRF-Hydro Parameter Calibration
Line 140 states that "parameters sensitive to hydrological processes were selected for calibration," but the specific parameters are not identified. It is recommended to include a table (in Supplementary) listing the names, physical meanings, initial ranges, and final calibrated values of the key parameters to enhance the transparency and reproducibility.
4.Sample Size and Overfitting Prevention
According to Line 159, the training period covers 1980–1996. If monthly resolution data were used, the sample length would be 204? Could the authors clarify the exact sample size? Given such a limited dataset, what specific measures were implemented to prevent the deep learning model from overfitting? Additionally, please provide the key hyperparameters to facilitate reproducibility and further academic exchange.
5.Link Human Activity Data with Attribution Results
The authors list data regarding human activities in lines 355-365. Please clarify how these multi-source datasets were statistically linked to the "effect of human activities" derived from attribution analysis. While "Correlation analysis" is explicitly mentioned in the technical flowchart (Figure 2), there appears to be a lack of corresponding methodological description or results in the main text. Please confirm if this analysis was performed and provide detailed evidence.