Missing data has been a common problem and has been confronted by many researchers in the field of hydrology. Rainfall and Temperature time series data are often found missing and such missingness have huge implication on hydrological modelling, flood frequency analysis, trend analysis and dam operation schemes. Owing to the presence of missing data it hinders the performance analysis of the data and inhibits in concluding the correct inferences from the data. In this study, missing data in the rainfall and temperature has been imputed using kNN model and Tree-based model and subsequently these imputed data have been used as predictors to predict the river flow data using Artificial Neural Network (ANN). Uncertainty from kNN imputation model has been found with bootstrapping techniques, while the tree based and ANN model were assessed by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
This study identifies the basin scale factors and potential remedies to restore the severely polluted Hindon River in India, by comparing with another basin with high population density: the River Thames in the UK. Biochemical oxygen demand (BOD) and dissolved oxygen (DO) in the Thames River are usually around 8 mg/l and 7.5 mg/l respectively, while phosphorus and ammonium range between 0.1–0.6 mg/l and 0.1–0.4 mg/l respectively. The Thames has seen great improvements in water quality over the past decades, due to high levels of sewage treatment and regulation of industrial effluents which have improved water quality conditions. Conversely, the Hindon River suffers from extremely poor water quality and this is mainly attributed to the direct discharge of partially treated or untreated municipal and industrial wastewater into the river. BOD is in the range of 15–60 mg/l and DO is below 5 mg/l. Phosphorus ranges around 2–6 mg/l at most of the monitoring stations and ammonia-nitrogen in the range of 10–40 mg /l in Galeta at Hindon. The analysis of variance also depicts the spatial and temporal variation in water quality in the Hindon River. Besides, non-point sources, pollution from point sources with minimal base flow in the river during dry season, result in low dilution capacity causing high pollutant concentrations which impacts the river ecosystem and fisheries. To restore the Hindon River, resources must be focussed on mainly treating sewage and industrial effluents and by developing appropriate river basin management and regulatory plans.
Hydropower plays a pivotal role in the socio-economic development of Bhutan where water resource is abundantly available and therefore several hydropower plants are being planned and a few under construction. However, with the presence of several potentially dangerous glacier lakes within the higher elevations, the country is always at the risk of Glacial Lake Outburst Flood (GLOF) and climate change which poses higher uncertainty regarding the sustainability of hydropower reservoirs in the long run. To understand the hydrological response of the basin, where new hydropower plants are going to be installed soon, a complex semi-distributed hydrological model has been prepared for the Punatshangchu basin using RS MINERVE. After calibration and validation of the model, it is observed that the model reflects low relative volume bias (-0.196 - 0.050) and high Nash efficiency (0.540 - 0.990) which is an important aspect to be considered for any hydropower dam and its operational schemes. Such a model is a viable tool well adapted to an operational flood forecasting system and management. With the built-in scheme for hydropower, reservoir, planner, and turbines within RS Minerve, it could be used to understand the array of scenarios for planning and operations.
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