Environmental flow assessments (e-flows) are relatively new practices, especially in developing countries such as Nepal. This study presents a comprehensive analysis of the influence of hydrologically based e-flow methods in the natural flow regime. The study used different hydrological-based methods, namely, the Global Environmental Flow Calculator, the Tennant method, the flow duration curve method, the dynamic method, the mean annual flow method, and the annual distribution method to allocate e-flows in the Kaligandaki River. The most common practice for setting e-flows consists of allocating a specific percentage of mean annual flow or portion of flow derived from specific percentiles of the flow duration curve. However, e-flow releases should mimic the river’s intra-annual variability to meet the specific ecological function at different river trophic levels and in different periods over a year covering biotas life stages. The suitability of the methods was analyzed using the Indicators of Hydrological Alterations and e-flows components. The annual distribution method and the 30%Q-D (30% of daily discharge) methods showed a low alteration at the five global indexes for each group of Indicators of Hydrological Alterations and e-flows components, which allowed us to conclude that these methods are superior to the other methods. Hence, the study results concluded that 30%Q-D and annual distribution methods are more suitable for the e-flows implementation to meet the riverine ecosystem’s annual dynamic demand to maintain the river’s health. This case study can be used as a guideline to allocate e-flows in the Kaligandaki River, particularly for small hydropower plants.

This study aimed to predict daily runoff using three machine learning models: artificial neural networks (ANNs), random forests (RFs), and extreme gradient boosting (XGBoost). Thirty-five years of discharge records and remote-sensing climate data were used to develop and validate the models, demonstrating the potential of satellite data for machine-learning-based runoff prediction, especially in data-poor regions like Nepal. The study identified 1-day lagged discharge (lag_1), average temperature, and maximum temperature as the three most important input variables. The developed ANN model demonstrates high accuracy in streamflow prediction, achieving an R2 of 0.928, a root-mean-square error (RMSE) of 435.43 m3/s, a Nash–Sutcliffe efficiency (NSE) of 0.93, and a Kling–Gupta efficiency (KGE) of 0.944 for the Narayani River Basin. When applied to the Trishuli River, a major tributary of the Narayani, the model demonstrated strong performance, with an R2 of 0.895, an RMSE of 425.92 m3/s, an NSE of 0.89, and a KGE of 0.908. Nonetheless, RF outperformed ANN in capturing low-flow conditions in the Narayani River. Overall, the models provide valuable tools for retrospective streamflow prediction, gap-filling in hydrological records, and supporting effective and sustainable water management.

In a pressurized water conveyance system, such as a hydropower system, during hydraulic transients, maximum and minimum pressures at various controlling sections are of prime concern for designing a safe and efficient surge tank. Similarly, quick damping of surge waves is also very helpful for the sound functioning of the hydro-mechanical system. Several parameters like diameter of the surge tank, diameter of the orifice, operating discharge, working head, etc., influence the maximum/minimum surge, damping of surge waves in the surge tank, and the difference of maximum pressure head at the bottom tunnel and maximum water level in the surge tank. These transient behaviors are highly conflicting in nature, especially for different diameters of orifices (D_O) and diameters of surge tanks (D_S). Hence, a proper optimization method is necessary to investigate the best values of D_O and D_S to enhance the safety and efficiency of the surge tank. In this paper, these variables are accurately determined through numerical analysis of the system by the Method of Characteristics (MOC). Furthermore, the influence on the transient behavior with changing D_O and D_S is investigated and finally, optimum values of D_O and D_S are determined using Particle Swarm Optimization (PSO) to minimize the effects of hydraulic transients on the system without compromising the stability and efficiency of the surge tank. The obtained results show significant improvements over the contemporary methods of finding D_O and D_S for surge tank design.

Anthropogenic activities have led to the transformation of river basins and natural flow alteration around the world. Alteration in flow regimes have adverse effects on river ecosystems. Flow value changes signify the alteration extent and a number of flow related indices can be used to assess the extent of alteration in a river ecosystem. Selection of a few and ecologically relevant indices from a large set of available indices is a daunting task. Principal Component Analysis helps to reduce these large indices to a few ecologically significant indices and removes statistical redundancy of data to give uncorrelated data sets. These representative indices are useful in the primary investigation of a less studied area like the Kaligandaki River basin, Nepal. This paper uses reduced indices from the Kaligandaki River to calculate the alteration on the river section downstream of a hydropower facility using the Histogram Comparison Approach (HCA) combined with Hydrologic Year Types (HYT). The combined approach eliminates the potential underestimation of alteration values which may occur due to the exemption of hydrologic year types from the analysis, a feature equally relevant in river ecology. A new metric is used for the calculation of combined alteration using HCA-HYT in this paper. The analysis showed 60.71 percent alteration in the natural flow regime in the area past a hydropower construction, which is classified in the high alteration category. The study can be a guide for further analysis of the ecological flow management of a river section and a parsimonious approach to other areas where hydrological data is limited to historical flow records only.