River damming has brought great benefits to flood mitigation, energy and food production, and will continue to play a significant role in global energy supply, particularly in Asia, Africa, and South America. However, dams have extensively altered global river dynamics, including riverine connectivity, hydrological, thermal, sediment and solute regimes, and the channel morphology. These alterations have detrimental effects on the quality and quantity of fish habitat and associated impacts on aquatic life. Indeed, dams have been implicated in the decline of numerous fishes, emphasizing the need for effective conservation measures. Here, we present a global synthesis of critical issues concerning the impacts of river damming on physical fish habitats, with a particular focus on key fish species across continents. We also consider current fish conservation measures and their applicability in different contexts. Finally, we identify future research needs. The information presented herein will help support sustainable dam operation under the constraints of future climate change and human needs.

The main objective of this study is to evaluate the performance of the integrated hydrological model, MIKE SHE in a small watershed to analyze the effect of two different precipitation sources on model outputs (groundwater elevation and surface water flows). The model was calibrated and validated with observed groundwater elevations and surface water flows measured at the United States Geological Survey (USGS) gage stations in the basin. The model calibration performance for surface water flows (R = 0.80, MAE= 0.20 m³/s, BIAS = −0.14 m³/s, NSE = 0.59) and groundwater elevations (R = 0.74, MAE = 0.45 m, BIAS = 0.08 m, NSE = 0.35) showed that the model was able to predict hydrological processes based on forcing variables in a small watershed. The analysis did not show the model with precipitation at the nearer (NOAA-Edwardsville) gauge station has better performance than the farther gauge station (NOAA-St. Louis). The quantitative analyses for the most sensitive model output variable suggested that precipitation uncertainties had noticeable impacts on surface water flows (0.81% to 11.19%), than groundwater elevations (0.06% to 0.07%), with an average of 6.71% and 0.66%, respectively. Our results showed noticeable differences in simulated surface water flows in spring (12.9%) and winter (36%) seasons compared to summer (11.4%) and fall (4.6%) as a result of difference (6% to 18%) in precipitation, which indicated that uncertainties in precipitation impact simulated surface water flows in a small watershed vary with different seasons. Our analyses have shown that precipitation affects the simulated hydrological processes and care should be taken while selecting input datasets (i.e., precipitation) for better hydrological model performance, specifically for surface water flows.