Large hydropower dams usually have serious impacts on river fish communities, yet there are few studies documenting their specific effects in the Balkans and Eastern Mediterranean countries. This study describes before-and-after changes in ichthyofaunal composition under the new hydroelectric flow regimes downstream of the Ilarion Hydroelectric Dam, located on Greece’s longest river, the Aliakmon. The dam became operational in 2012 and is located approximately 7 km upstream of the Polyphytos hydroelectric reservoir, which was created in 1972. The investigated reach below the Ilarion Dam is thus confined between two large artificial hydroelectric reservoir systems, forming a highly regulated and spatially restricted lotic–lentic transition zone. Contemporary electrofishing surveys were compared with historical surveys predating the construction of the dam and its upstream reservoir. Results indicate marked compositional shifts in the fish assemblage, with four native rheophilic species not detected in the contemporary downstream surveys, while at least three other species showed marked declines. In contrast, generalist and lentic species increased in abundance, including the non-native lentic species Lepomis gibbosus and Gambusia holbrooki. Our analyses of available hydrological data from recently installed monitoring stations show altered flow conditions, including hydropeaking and modified water-temperature regimes downstream of the dam, together with the destabilizing effects of water-level fluctuations in the downstream reservoir. Furthermore, topographic maps of the river reach indicate substantial alteration of the riverbed associated with gravel quarrying during dam construction, conditions that may also promote lotic habitat degradation. Although this study is not based on long-term monitoring, it provides insights that may help guide further research and adaptive management in highly regulated hydropower river systems.
Water conservation is acritical ecohydrological function linking land-cover patterns, soil hydraulic properties and basin-scale water regulation services. To improve the accuracy of large-basin assessments and reduce local bias, this study integrated multi-source data, including land use, soil properties, and saturated hydraulic conductivity, and developed a refined sub-basin framework coupling the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with a quadratic interpolation function constructed based on the principle of a constant second-order difference. Using sub-basin calibration, independent computation, raster mosaicking, and spatial-continuity optimisation, the spatiotemporal dynamics of water conservation in the Yellow River Basin (YRB) from 2000 to 2023 were simulated. The results indicated high simulation accuracy: after adopting sub-basin optimal Z parameters, relative errors between simulated water yield and bulletin data were <1% for most sub-basins (except Sub-basin 3 and years with invalid Z values), with a basin-wide error below 5%.; the relative errors of sub-basin water yield simulated using the quadratic interpolation-based spatial optimisation were within ±3%. After raster mosaicking, sub-basins had relative errors below 5% between simulated water yield and bulletin data. And the mean water conservation depth in the YRB was 6.86 mm, showing marked spatial heterogeneity, with low values in the northwest and high values in the southwest, southeast, and central-eastern parts of the basin. Interannually, water conservation depth increased significantly at a rate of 0.097 mm/a (p < 0.05), in synchrony with water yield depth. This study provides a transferable framework for accurately estimating large-basin-scale water conservation capacity.
River ecosystems are critical socio-ecological systems to support human well-being but face increasing pressures from pollution and climate variability. The Yamuna River has experienced persistent water quality (WQ) degradation despite long-term restoration efforts. This study evaluated whether predictor importance changed across 25 years of restoration activities (1997–2022). A Hydro-Climatic (HC) and Water-Chemistry (WC) Domain Decomposition Framework was combined with explainable artificial intelligence (SHAP), and twelve machine-learning algorithms were evaluated using leakage-controlled spatial and temporal cross-validation. WC models showed high spatial transferability, with R2 values of 0.962–0.975 for the five strongest algorithms, whereas all HC models produced negative spatial R2 values (−0.166 to −0.693). Among the stable HC models with positive temporal performance, R² ranged from 0.354 to 0.582. SHAP analysis identified Fe, PO₄-P, NO₂-N, NO₃-N, and NH₃-N as the five highest-attributed WC predictors. Their ranking remained stable across the three policy eras, while the mean pairwise rank correlation across held-out station folds was 0.942. The five predictors accounted for 58.0% of total absolute SHAP attribution. A Water-Chemistry-to-Hydro-Climatic Attribution RatioRWC/HC was calculated from a single combined-domain model, with a full-record reference value of 21.88 (95% CI: 21.63–22.13). The framework provides a transparent basis for interpreting water-quality prediction and supporting evidence-informed river-basin management.
Understanding long-term changes in water-related ecosystem services (WESs) on the Qinghai–Tibet Plateau (QTP) remains challenging, particularly regarding spatiotemporal patterns, spatial spillovers, and driving mechanisms. This study integrates four decades of geospatial data with ecosystem-service models, spatial econometric analysis, and geographically weighted regression (GWR). Using this framework, we quantified changes in water yield (WY), soil conservation (SC), and water purification (WP) and assessed direct and indirect effects and spatial heterogeneity in their relationships with drivers. WY and SC declined from 1980 to 1994 and subsequently increased, whereas the WP indicator showed temporal fluctuations with improved purification capacity after 2002. Spatial heterogeneity was evident among sub-watersheds, with distinct regional patterns across the QTP. Climate, vegetation, socioeconomic, and landscape factors were significantly associated with WES variations. Precipitation showed significant positive associations with WY and SC, while vegetation cover and temperature were associated with reduced nitrogen export and improved WP capacity. Landscape metrics, including LPI and LSI, exhibited direct and indirect effects, highlighting the importance of spatial interactions among neighboring sub-watersheds. These findings underscore the need for integrated watershed management, vegetation restoration, and landscape optimization considering regional ecological heterogeneity and hydrological connectivity. The study provides a long-term integrated assessment of WES dynamics on the QTP and offers insights for sustainable ecosystem management under future environmental changes.
