| Watershed Description: | 795,000 km^2 Mekong River, which drains parts of China, Myanmar, Laos, Thailand, Cambodia, and Vietnam in Southeast Asia (see Table 1). |
| Abstract: | In establishing adequate climate change policies regarding water resource development and
management, the most essential step is performing a rainfall-runoff analysis. To this end, although
several physical models have been developed and tested in many studies, they require a complex
grid-based parameterization that uses climate, topography, land-use, and geology data to simulate
spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty
originating from insuffcient data quality and quantity, unreliable parameters, and imperfect model
structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station
on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based
black-box method. Future runoff variations were simulated by applying a climate change scenario.
To assess the applicability of the LSTM model, its result was compared with a runoff analysis
using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods
in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005),
verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process
of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data
were fed into the algorithms, with the exception of the observed discharge and temperature data,
which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs)
4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility
at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the
SWAT model showed a Nash–Sutcliffe effciency (NSE) value of 0.84, while the LSTM model showed
a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station
over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model.
However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater
variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction
presents a higher reproducibility than that of the SWAT model in simulating runoff variation according
to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a
small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff
time-series are available. |