The research team, led by Hyeong-Yun So, Hyeon-Cheol Yoon, and Tae-Gyun Kim of the National Integrated Drought Center at the National Disaster Management Research Institute, together with Se-Jeong Lee of the Korea Institute of Hydrogical Survey, focused on the domestic and industrial water supply sector of South Korea’s drought warning system. Unlike the meteorological sector, which relies on indices such as the Standardized Precipitation Index, or the agricultural sector, which monitors reservoir storage and soil moisture, the domestic and industrial sector bases its four warning stages—attention, caution, alert, and severe—on dam-specific water supply adjustment criteria tied to storage volumes and storage ratios. Predicting those storage levels quantitatively, months in advance, therefore translates directly into earlier and better-calibrated drought warnings for cities and industry.

The study area was Juam Dam itself, a multipurpose dam in the Boseonggang River basin, a tributary of the Seomjingang River, with a total storage capacity of 457 million cubic meters. During the prolonged meteorological drought that stretched from the second half of 2022 into the first half of 2023, Juam Dam’s storage fell to the severe stage, disrupting major industrial complexes at Yeosu and Gwangyang and prompting consideration of domestic water supply restrictions in Gwangju. The researchers assembled 32 years of daily hydrological and meteorological observations, spanning 1991 to 2022, from the National Water Resources Management Information System and the Korea Meteorological Administration’s Weather Data Portal, and reserved the drought-stricken first half of 2023 as an independent test period.

A central challenge emerged immediately from correlation analysis. Using the Pearson correlation coefficient after min–max normalization, the team found that even the most strongly related input variable, dam outflow, showed a correlation with storage of only r = 0.253, followed by inflow, humidity, wind speed (negatively correlated), temperature, and rainfall. In other words, the linear relationships between the available observations and the target variable were weak across the board. This is precisely the regime in which deep learning models, capable of capturing temporal lags and complex nonlinear interactions, outperform traditional regression and conventional machine learning approaches—but only if the input representation is designed well. The team therefore applied feature engineering, generating lagged variables, arithmetic transformations, and moving averages from the raw observations, and built three distinct training datasets. Dataset A contained only the original variables; Dataset B incorporated all expanded features; Dataset C retained only the strongly correlated expanded features derived from inflow, outflow, and antecedent storage, deliberately excluding meteorological parameters.

Each dataset was paired with four deep learning architectures, producing twelve candidate models. The Long Short-Term Memory network, a recurrent architecture with forget, input, and output gates and a cell state that preserves long-term information, served as the field’s standard baseline. The Bidirectional LSTM added a backward pass over the sequence, combining past and future context. The hybrid CNN-LSTM stacked one-dimensional convolutional feature extraction in front of recurrent sequence learning. The fourth architecture, the Temporal Convolutional Network, replaced recurrence entirely with causal one-dimensional convolutions—ensuring that predictions depend only on current and past inputs—and dilated convolutions, which insert gaps between filter elements to expand the receptive field exponentially across time. That dilation mechanism allows the TCN to learn patterns over very long horizons while remaining fully parallelizable during training, a structural advantage over step-by-step recurrent processing.

The comparative results were decisive. Models trained on Dataset B, with its full set of engineered features, reduced test RMSE by roughly 14 percent relative to Dataset A, while Dataset C—stripped of meteorological variables—performed dramatically worse, with test RMSE increasing by about 34 percent over Dataset A. The lesson was counterintuitive but important: although temperature, humidity, and wind speed are physically coupled to inflow and outflow through evapotranspiration, removing them deprived the models of nonlinear context about long-term storage depletion. Deep learning architectures, the authors conclude, thrive on multi-dimensional interactions among many weakly correlated variables rather than on a few strongly correlated linear inputs. Across architectures, the TCN dominated, achieving average RMSE values of 12.150 in training, 8.823 in validation, and 21.337 in testing, while the LSTM fared worst at 44.818, 17.296, and 57.950 respectively—reductions of roughly 73, 49, and 63 percent in the TCN’s favor. Qualitatively, however, the LSTM reproduced the temporal shape of storage variation most faithfully, and the TCN delivered the closest numerical agreement, a tension the team resolved by optimizing the TCN further.

Optimization proceeded through a two-stage grid search over the TCN’s hyperparameters, which are known to strongly influence its behavior: activation function, dilation rate, filter size, kernel size, learning rate, batch size, number of epochs, and the lookback period of historical input. The first stage, generating 256 model configurations, converged on the ReLU activation, a dilation rate sequence of 1, 2, 4, and 8, and 200 training epochs. The second stage revealed a striking finding: the optimal lookback window was 1,460 days—approximately four years. The authors attribute this to the operational rhythm of South Korean dam management, in which the standardized Dam Water Supply Adjustment Criteria are revised every two to four years. A four-year memory allows the model to internalize these cyclical regulatory shifts and the human-controlled discharge patterns they produce. Notably, the configuration with the lowest training and validation error showed unstable oscillatory predictions and poor generalization, so the final hyperparameters were selected from the first-stage results to guarantee robustness over raw precision.

The final optimized model was evaluated four times across 2023, each with a different forecast origin, and its predictions were compared against Juam Dam’s operational warning thresholds, where consecutive drought stages are separated by roughly 4.5 to 5 percent of total storage. The model captured the severe drought of early 2023, reproduced the storage recovery toward the end of June, tracked spring and flood-season dynamics, and matched the stable conditions of late year. Its one clear weakness was the abrupt storage surge in mid-July driven by an extreme rainfall event totaling 306 millimeters in five days, which the model underestimated. Quantitatively, the four evaluations yielded an average Mean Absolute Error of 28.491, an average RMSE of 35.440, and an average error rate of 6.23 percent of total storage, with the best quarter achieving an error rate of just 3.68 percent. In earlier comparative testing, the TCN model also achieved a 78.9 percent hit rate within a 5 percent error margin of observed storage, with a correlation coefficient exceeding 0.6 in the Taylor diagram analysis.

The authors are candid about what a 6.23 percent error rate means operationally. Because warning stages are densely spaced, a forecast of this precision could misclassify a specific stage if used for short-term, deterministic actions such as triggering water restrictions, which demand errors below roughly 3 percent. But the purpose of a six-month outlook is different: it functions as an early-stage monitoring tool for macro-level storage trends and precursory signs of impending shortage. Within that framing, capturing a downward storage trajectory within about 6 percent provides water managers an extended proactive window to secure alternative supplies and establish conservative safety margins well before crisis points arrive—the very lead time that was missing during the Juam and Obong crises.

The research team outlines an ambitious path forward. They plan to extend the framework to all 33 dams covered by South Korea’s domestic and industrial drought forecasting system, developing dam-specific datasets and models that reflect each reservoir’s unique hydrology, and to investigate stretching predictions to a twelve-month horizon. An ensemble framework is planned that would combine the TCN’s error-suppressing precision, the LSTM’s skill at reproducing temporal trends, and the Transformer’s capacity for long-range context, with performance assessed using additional metrics such as the Nash-Sutcliffe Efficiency and the Kling-Gupta Efficiency. To address the models’ struggle with the non-stationary extremes of a changing climate—intense short-duration rainfall and prolonged drought alike—the team intends to accumulate training data on unprecedented events and generate synthetic, physics-informed virtual datasets. If those efforts succeed, the six-month dam storage forecast demonstrated here could evolve from a research result into a routine instrument of national drought preparedness, giving water managers the one resource drought victims never have enough of: time.

Subject of Research: Midterm drought forecasting using deep learning prediction of dam reservoir storage dynamics

Article Title: Midterm drought forecasting based on dam storage prediction using deep learning algorithms

Article References: So, H.-Y., Yoon, H.-C., Kim, T.-G., & Lee, S.-J. (2026). Midterm drought forecasting based on dam storage prediction using deep learning algorithms. Environmental Earth Sciences, 85(16), Article 407. https://doi.org/10.1007/s12665-026-13138-2

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13138-2

Keywords: drought forecasting, deep learning, temporal convolutional network, LSTM, dam storage prediction, feature engineering, grid search, hyperparameter optimization, water resource management, South Korea, Juam Dam, hydrological modeling