Abstract

Environmental flow (Eflow) allocations of reservoir-river systems do not adequately address the connections between reservoir inflows, releases, and corresponding downstream river water quality rendering water resources management difficult. The study integrated the Soil and Water Assessment Tool (SWAT) for reservoir inflows estimation and corresponding releases with Global Environmental Flow Calculator (GEFC) for Eflows allocations to simulate the resulting Dissolved Oxygen (DO) of the downstream river stretch using QUAL2K. The study considered various plausible scenarios of inflows (10 to 20% reduction), pollution scenarios of Biological Oxygen Demand (BOD) (0 to 100% treatment) for arriving drains by altering headwaters DO (4 to 8 mg/l). Eflow water quality charts were developed for the regulation of reservoir downstream river water quality for Bhadra river in India as case study. The study revealed that by maintaining the headwater DO in conjunction with BOD treatment of drains and with sufficient Eflow allocation, downstream river water quality can be improved. By maintaining a headwater DO of 7 mg/l with BOD treatment of drains of 25% and by providing Eflow allocation of 40.12 m3/s with class D, the Bhadravathi river stretch has shown an improved river water quality with about 6.65 mg/l of average DO.

Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India

Abstract

mpact Assessment of Environmental Flows Using CORDEX Regional Climate Models: Case Study of Nagarjuna Sagar Dam, Krishna River, India

Abstract

Utilization of coal ash under a foundation of structure deals with huge quantity of its usage that solves the issue of its disposal, which is trivial in most parts of the world. Adaptability of coal ash under foundation needs to be examined on a large scale. In this study, it is attempted to investigate static and seismic response of shallow foundation resting on pond ash deposit. Two-dimensional (2D) response analysis is carried out for the ash-foundation system by employing finite element software PLAXIS 2D. It has been recognized since decades that soil-foundation interaction or ash-foundation interaction is prime factor in the seismic response of structure or portions of structure. Ash-foundation system is excited under the influence of Nepal earthquake motion (Mw: 7.8) and North East India earthquake motion (Mw: 7.5). The displacement was observed more in case of North East India earthquake, i.e. 6.028 m. However, acceleration and excess pore pressure response were high in case of Nepal earthquake, i.e. 0.152 g and 129.16 kPa, respectively. These responses can be utilized to assess the performance of ash-foundation system.

Abstract

Environmental flows play a major role in terms of quantity and quality for sustainable riverine ecosystems (Brisbane Declaration, 2007). Environmental Flows (EFs) have a variety of impacts in different regions of the world, including fisheries and other aquatic life, assimilative capacity, drinking water security, agriculture, transportation, navigation, industry, flood protection, recreation and tourism, and other cultural aspects (Iyer, 2005). The EFs are a measure of the amount and quality of water flowing in a freshwater river or stream over time. Estimation of EFs should be able to consider hydrologic, hydraulic, habitat and biodiversity, water quality, socioeconomic and cultural aspects with consideration of water regulation policies (Tennant, 1976). Estimation of EFs is generally practiced with consideration of one or multiple factors. Among these, the hydrological aspect with consideration of historical natural flow data is the most common and practiced by several river water management stakeholders (Zeiringer et al., 2018). Environmental flows based on hydrological criteria were conventionally estimated using flow indices based on a selected threshold level, which may be different for various river systems (Sharma and Dutta, 2020).

Abstract

Short and long range reservoir inflow forecast is essential for efficient real time operational planning, scheduling of hydroelectric power system and management of water resources. Large-scale climate phenomenon indices have a strong influence on hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. This study aims to explore the relevance of large-scale climate phenomenon indices in improving the reservoir inflow prediction at short-term time scales. This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting ensemble method to quantify forecasting uncertainty and to improve the model performance by combining the inflow results of the single ML model and the highest vote is chosen based on the weights assigned to the single ML model. The developed framework was examined in two distinct reservoirs located in India and California, USA. The ensemble model consistently outperformed the standalone RF, GBR, KNN, and LSTM in predicting high (flood control and monsoon seasons) and low flows (runoff and non-monsoon seasons) of both study reservoirs. The demonstrated short-term reservoir forecasting model allows reservoir operators to adapt and add regional hydrological and large-scale climate indices in real-time decision-making. The presented framework can be applied for any reservoir inflow forecasting. Introduction Reservoirs are important hydraulic structures in terms of collection, storage, and diversion of freshwater for purposes such as drinking, irrigation, hydropower, flood management operations, and drought relief (Simonovic, 2020). Many developing countries (such as India) have made efforts towards sustainable reservoir operations to account for the adverse effects of dams for regional water resources management (Bhadoriya et al., 2020, Goel et al., 2020, Rehana and Mujumdar, 2014). Reservoir inflows play a major role in water allocations fulfilling various water demands and balancing the flows from upstream catchments to downstream regions during floods (Rehana et al., 2020). The operation of reservoir is complex, involving multiple time scales, multi-flow regimes, and unpredictable emergencies (Zhang et al., 2018). Forecasting reservoir inflows is a preliminary steps in reservoir operation, providing guidelines and rule curves for optimal water allocations to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements (Kasiviswanathan et al., 2020). In this context, long and short-term reservoir inflow forecasts are vital for sustainable water resources planning and management (Rehana et al., 2020). Short-term seasonal and monthly operations are relevant for optimal economic benefits, water supply, and downstream augmentation operations (Zhang et al., 2018). Daily time scale operations also relate to controlling floods, power grid loads, and emergency operations (Noorbeh et al., 2020). In this context, the prediction of daily reservoir inflows is prominent for real-time reservoir operation under hydroclimatic extremes such as low and high flows. Conventionally, real-time reservoir operations are associated with high variability and rapid changes, often deviating from the operation rule curves developed based on physical models (Oliveira & Loucks, 1997). Further, physically based models are subject to various uncertainties and need to account for complex natural and human-influenced hydrological conditions and water demands. Although hydrological models are best tools for predicting inflows, and providing details of precipitation, evapotranspiration, soil, and land use characteristics of the upstream catchment area, these models require a rigorous amount of landscape information at various spatiotemporal scales and several pre-processing efforts (Awol et al., 2019). Alternatively, data-driven algorithms based on Machine Learning (ML)

Abstract

The impact of climate change on the oxygen saturation content of the world’s surface waters is a signifcant topic for future water quality in a warming environment. While increasing river water temperatures (RWTs) with climate change signals have been the subject of several recent research, how climate change afects Dissolved Oxygen (DO) saturation levels have not been intensively studied. This study examined the direct efect of rising RWTs on saturated DO concentrations. For this, a hybrid deep learning model using Long Short-Term Memory integrated with k-nearest neighbor bootstrap resampling algorithm is developed for RWT prediction addressing sparse spatiotemporal RWT data for seven major polluted river catchments of India at a monthly scale. The summer RWT increase for Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, for 2071–2100 with ensemble of NASA Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway 8.5 scenario. The RWT increases up to7 °C for summer, reaching close to 35 °C, and decreases DO saturation capacity by 2–12% for 2071–2100. Overall, for every 1 °C RWT increase, there will be about 2.3% decrease in DO saturation level concentrations over Indian catchments under climate signals.

Abstract

Climate change exposes more frequent natural hazards and physical vulnerabilities to the built and natural environments. Extreme precipitation and temperature events will have a significant impact on both the natural environment and human society. However, it is unclear whether precipitation and temperature extremes increase physical vulnerabilities across scales and their links with large-scale climate indices. This study investigates the relationship between precipitation and temperature extremes, as recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), and large scale climatological phenomenon indices (Indian Summer Monsoon Index (ISMI), Arctic Oscillation (AO), and North Atlantic Oscillation (NAO)), using India as a case study. Our Bndings show that extreme warm indices were primarily negatively related to ISMI and positively related to extreme cold indices. According to Pearson’s correlation coefBcients and Wavelet Transform Coherence (WTC), extreme warm indices were negatively related to ISMI and positively related to extreme cold indices. The extreme precipitation indices had a significant positive relationship, primarily with AO. Furthermore, from 1951 to 2018, India experienced an increase in warm extremes over western, central, and peninsular India, while cold indices increased over northwest India. Precipitation extremes of more than one day, more than Bve days, very wet and extremely wet days have increased across India except in the Indo-Gangetic plains, while heavy and very heavy precipitation days, consecutive wet days, and consecutive dry days have decreased.

Abstract

The present study compared various empirical and data-driven algorithms to predict Actual Evapotranspiration (AET) using various hydro climatic variables. The AET over semi-arid climatic conditions of Hyderabad, Telangana, India, and Waipara (New Zealand) was estimated using different empirical methods-based PET using Budyko and Turc models. Modelled PET from five data-driven algorithms, such as Long short-term memory neural networks (LSTM), Artificial Neural Network (ANN), Gradient Boosting Regressor, Random Forest, and Support Vector Regression were trained to predict AET using meteorological variables. The results show simple empirical-based AET models, Budyko and Turc, can estimate AET very well. The results indicated that 99% accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with limited data. Both LSTM and ANN models based on PET have been noted as the most robust models for estimating AET with minimal climate data. It was observed that the meteorological variables of temperature and solar radiation have more significant contributions than other variables in the estimation of AET. In addition, the effects of the meteorological variables were found to be essential and effective in the estimation of AET. The research findings of the study reveal that under limited data availability, the best input combinations were identified as temperature and wind speed for estimating PET; temperature, wind speed, and precipitation for estimating AET for semi-arid climatology.

Abstract

Maintaining the full scale of naturally occurring river flow is usually impossible due to the development of the water resources and variations of land and soil usage in the catchment. These developed resources can create differences in the balance of the ecosystem and socio-economic activities. These designed structures also cause a decrease in the minimum flow regime in downstream. Environmental flows (EFs) are the medium that help maintain river flow in healthy or ecological conditions. The river’s hydrologic (river mapping), hydraulic (cross-section, water depth, and velocity), and environmental conditions (riparian flora and fauna) are significant considerations for estimating environmental flows (EF’s). There are various desktop assessment methods for calculating the environmental flows. In this present study, the global environmental flow calculator (GEFC) method is used to estimate the ecological flows by using the flow duration curves (FDC) generated from the given monthly discharge data of the river. The FDC in this system contains 17 fixed percentile points concerning the discharge. In the current study, we analyze the environmental flows of the Tungabhadra River basin by considering the different discharge stations, which are Balehonuur, Haralahalli, Hosaritti, Shivamogga, Honalli, Rattihalli, and Tungabhadra Dam, with a mean annual flow (MAF) of 36%, 24.8%, 27.2%, 16.2%, 23.3%, 21.1%, and 12.2%, respectively, to maintain the ecological conditions of the river. The monthly discharge data from 1995 to 2017 for those stations are obtain from the Advance Center for Integrated Water Resource (ACIWR) Bengaluru, India. The river flow health is a study which helps in understanding the environmental variables that effects the habitat structure, flow regime, water quality, and biological conditions of the river. To estimate the Flow Health of Tungabhadra River, we used a tool called Flow Health which uses nine indicators to represent the Flow Health (FH) score for the stations Balehonnur, Haralahalli, Hosaritti, Shivamogga, Honalli, Rattihalli, and Tungabhadra. This tool uses the gauge discharge data in the form of reference (1995–2005) and test periods (2006–2017), with Flow Health score of 0.72, 0.4, 0.72,0.70, 0.58, 0.73, 0.71 and 0.72, 0.63, 0.63, 0.7, 0.66, 0.67, 0.66 for test and reference period with respect to stations. The study noted that majority of the discharge stations along the Tungabhadra River show a moderate to low flow variations for the reference and test periods. Overall, Tungabhadra river health, measured by the flow indices, had declined from 1995–2005 to 2006–2017.