The Emerging Flood Risk on the Lower Part of Transboundary Meric/Maritsa River Basin

In this study, changes in the surface area of Lake Burdur during the 2015-2025 period and the spatial direction of the associated shrinkage were examined using Sentinel-2 Level-2A satellite images. A total of 111 satellite images, each representing a monthly period, were analyzed using a fixed study window and a lake perimeter mask; a three-cluster unsuper-vised K-means segmentation method was applied to separate the water surface from bare/drained areas and vegetation classes. The resulting binary water masks were used to convert the lake surface area to km2 on a pixel-by-pixel basis, and the geometric center of the lake mass was calculated for each observation date. The unique aspect of this study is that it evaluates lake shrinkage not only through a decrease in surface area but also as a directional spatial process via the movement of the centroid center. In this context, the cumulative displacement was decomposed into X/West and Y/South components using the initial centroid point as a reference; OLS-based linear and logarithmic trend models were established for both directions. Model performances were compared using a 15-fold Monte Carlo cross-validation approach with R2, adjusted R2, NSE, KGE, MAE, MAPE, MSE, and RMSE metrics; additionally, the statistical significance of model differences was assessed using the Wilcoxon signed-rank test. The findings indicate that the logarithmic model yields more balanced and reliable results in the X/West direction, while the linear model does so in the Y/South direction. Based on this model structure, spatial projections were generated for the 2026-2035 period, and it was observed that the projection bands remained stable despite Monte Carlo-based coefficient uncertainty. In conclusion, the study demonstrates that lake drawdowns in semi-arid closed basins can be monitored in a more interpretable and statistically robust manner using Sentinel-2-based segmentation, centroid kinematics, and cross-validated trend modeling.

In this study, changes in the surface area of Lake Burdur during the 2015–2025 period and the spatial direction of the associated shrinkage were examined using Sentinel-2 Level-2A satellite images. A total of 111 satellite images, each representing a monthly period, were analyzed using a fixed study window and a lake vicinity mask; a three-cluster unsupervised K-means segmentation method was applied to separate the water surface from bare/drained areas and vegetation classes. The resulting binary water masks were used to convert the lake surface area to km² on a pixel-by-pixel basis, and the geometric center of the lake mass was calculated for each observation date. The unique aspect of this study is that it evaluates lake shrinkage not only through a decrease in surface area but also as a directional spatial process via the movement of the centroid center. In this context, the cumulative displacement was decomposed into X/West and Y/South components using the initial centroid point as a reference; OLS-based linear and logarithmic trend models were established for both directions. Model performances were compared using a 15-fold Monte Carlo cross-validation approach with R², adjusted R², NSE, KGE, MAE, MAPE, MSE, and RMSE metrics; additionally, the statistical significance of model differences was assessed using the Wilcoxon signed-rank test. The findings indicate that the logarithmic model yields more balanced and reliable results in the X/West direction, while the linear model does so in the Y/South direction. Based on this model structure, spatial projections were generated for the 2026–2035 period, and it was observed that the projection bands remained stable despite Monte Carlo-based coefficient uncertainty. In conclusion, the study demonstrates that lake drawdowns in semi-arid closed basins can be monitored in a more interpretable and statistically robust manner using Sentinel-2-based segmentation, centroid kinematics, and cross-validated trend modeling.

In this study, daily reference evapotranspiration (ET₀) values for the period 2025–2030 for Lake Burdur, located in the Mediterranean climate zone and within the Burdur closed basin, were estimated using nested architecture focused on high accuracy. The ET₀ target corresponds to the FAO-56 Penman–Monteith reference evapotranspiration variable provided by the Open-Meteo Historical Weather API, and it is treated throughout as a standardized measure of atmospheric evaporative demand rather than as actual lake-surface evaporation or basin water loss. For this purpose, daily mean air temperature, relative humidity, shortwave surface radiation, and evapotranspiration data for the period 1984–2024 were obtained from the Open-Meteo platform. In the first stage of the study (Model 1), separate SARIMAX (statistical), XGBoost (machine learning), and LSTM (deep learning) models were applied for temperature, relative humidity, and radiation series; the model with the highest validation mean for each variable was selected. Accordingly, LSTM (Mean $R^2$ = 0.967) was determined to be the most successful model for temperature, SARIMA(X) (Mean $R^2$ = 0.812) for relative humidity, and XGBoost (Mean $R^2$ = 0.845) for the radiation variable, which is non-linear, has strong autocorrelation, and exhibits distinct seasonality. In the second stage (Model 2), these best climate predictions were used as independent variables for evapotranspiration, and LSTM provided the highest success for evapotranspiration (Mean $R^2$ = 0.941). Trend analyses revealed that the increase in temperature and evapotranspiration and the decrease in relative humidity observed in the past period will continue in the near future. The uncertainty analysis conducted using the Monte Carlo/resampling approach on historical data showed that the 95% prediction intervals largely protected the upward trend in evapotranspiration against random fluctuations. These intervals reflect residual-based uncertainty under the fitted model rather than the full predictive uncertainty of future basin evapotranspiration. The findings indicate that designing model selection appropriate to the structure of the variables within a nested prediction framework significantly improves forecast accuracy and can provide a viable decision support input for sustainable water management in Mediterranean basins experiencing water scarcity.