Light is an important factor that affects cyanobacterial growth and changes in light can influence their growth and physiology. However, an information gap exists regarding light-induced oxidative stress and the species-specific behavior of cyanobacteria under various light levels. This study was conducted to evaluate the comparative effects of different light intensities on the growth and stress responses of two cyanobacteria species, Pseudanabaena galeata (strain NIES 512) and Microcystis aeruginosa (strain NIES 111), after periods of two and eight days. The cyanobacterial cultures were grown under the following different light intensities: 0, 10, 30, 50, 100, 300, and 600 μmol m⁻² s⁻¹. The optical density (OD₇₃₀), chlorophyll a (Chl-a) content, protein content, H₂O₂ content, and the antioxidative enzyme activities of catalase (CAT) and peroxidase (POD) were measured separately in each cyanobacteria species. P. galeata was negatively affected by light intensities lower than 30 μmol m⁻² s⁻¹ and higher than 50 μmol m⁻² s⁻¹. A range of 30 to 50 μmol m⁻² s⁻¹ light was favorable for the growth of P. galeata, whereas M. aeruginosa had a higher tolerance for extreme light conditions. The favorable range for M. aeruginosa was 10 to 100 μmol m⁻² s⁻¹.

Sediment yield is a complex phenomenon of weathering, land sliding, and glacial and fluvial erosion. It is highly dependent on the catchment area, topography, slope of the catchment terrain, rainfall, temperature, and soil characteristics. This study was designed to evaluate the key hydraulic parameters of sediment transport for Kali Gandaki River at Setibeni, Syangja, located about 5 km upstream from a hydropower dam. Key parameters, including the bed shear stress (τ_b), specific stream power (ω), and flow velocity (v) associated with the maximum boulder size transport, were determined throughout the years, 2003 to 2011, by using a derived lower boundary equation. Clockwise hysteresis loops of the average hysteresis index of +1.59 were developed and an average of 40.904 ± 12.453 Megatons (Mt) suspended sediment have been transported annually from the higher Himalayas to the hydropower reservoir. Artificial neural networks (ANNs) were used to predict the daily suspended sediment rate and annual sediment load as 35.190 ± 7.018 Mt, which was satisfactory compared to the multiple linear regression, nonlinear multiple regression, general power model, and log transform models, including the sediment rating curve. Performance indicators were used to compare these models and satisfactory fittings were observed in ANNs. The root mean square error (RMSE) of 1982 kg s⁻¹, percent bias (PBIAS) of +14.26, RMSE-observations standard deviation ratio (RSR) of 0.55, coefficient of determination (R²) of 0.71, and Nash–Sutcliffe efficiency (NSE) of +0.70 revealed that the ANNs’ model performed satisfactorily among all the proposed models.

The ecological dynamics of riparian areas interact with sediment transport in river systems, which plays an active role in riparian vegetation growth in the floodplain. The fluvial dynamics, hydraulics, hydro-meteorological and geomorphological characteristics of rivers are associated with sediment transport in river systems and around the riparian area. The flood disturbance, sediment with nutrients and seeds transported by river, sediment deposition, and erosion phenomena in the floodplain change the bare land area to vegetation area and vice versa. The difference in riparian vegetation area in the river floodplain is dependent on the sediment grain size distribution which is deposited in the river floodplain. Mathematical models describing vegetation growth in a short period exist in literature, but long-term modelling and validations are still lacking. In order to cover long-term vegetation growth modelling, a Dynamic Riparian Vegetation Model (DRIPVEM) was proposed. This paper highlights the existing modelling technique of DRIPVEM coupled with a Dynamic Herbaceous Model used to establish the interactive relationship of sediment grain sizes and riparian vegetation in the floodplain.

Streamflow and sediment flux variations in a mountain river basin directly affect the downstream biodiversity and ecological processes. Precipitation is expected to be one of the main drivers of these variations in the Himalayas. However, such relations have not been explored for the mountain river basin, Nepal. This paper explores the variation in streamflow and sediment flux from 2006 to 2019 in central Nepal’s Kali Gandaki River basin and correlates them to precipitation indices computed from 77 stations across the basin. Nine precipitation indices and four other ratio-based indices are used for comparison. Percentage contributions of maximum 1-day, consecutive 3-day, 5-day and 7-day precipitation to the annual precipitation provide information on the severity of precipitation extremeness. We found that maximum suspended sediment concentration had a significant positive correlation with the maximum consecutive 3-day precipitation. In contrast, average suspended sediment concentration had significant positive correlations with all ratio-based precipitation indices. The existing sediment erosion trend, driven by the amount, intensity, and frequency of extreme precipitation, demands urgency in sediment source management on the Nepal Himalaya’s mountain slopes. The increment in extreme sediment transports partially resulted from anthropogenic interventions, especially landslides triggered by poorly-constructed roads, and the changing nature of extreme precipitation driven by climate variability.