Hydropower potential assessment using spatial technology and hydrological modelling in Nigeria river basin

2021 ◽  
Author(s):  
O.A. Fasipe ◽  
O.C. Izinyon ◽  
J.O. Ehiorobo
Keyword(s):  
River Basin ◽  
Hydrological Modelling ◽  
Hydropower Potential ◽  
Spatial Technology

scholarly journals Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin

2016 ◽  
Vol 8 (4) ◽  
pp. 279 ◽  
Author(s):  
Gijs Simons ◽  
Wim Bastiaanssen ◽  
Le Ngô ◽  
Christopher Hain ◽  
Martha Anderson ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modelling ◽  
Red River ◽  
Red River Basin ◽  
Data Products ◽  
Modelling Studies ◽  
Derived Data

In-stream turbines for sustainable hydropower development in the Amazon river basin

2021 ◽  
Author(s):  
Suyog Chaudhari ◽  
Erik Brown ◽  
Raul Quispe-Abad ◽  
Emilio Moran ◽  
Norbert Mueller ◽  
...  
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Ecological Impacts ◽  
Amazon River ◽  
River Ecology ◽  
Amazon River Basin ◽  
Hydropower Development ◽  
Continental Scale ◽  
Large Dams ◽  
Hydropower Potential

<p>Given the ongoing and planned hydropower development projects in the Amazon River basin, appalling losses in biodiversity, river ecology and river connectivity are inevitable. These hydropower projects are proposed to be built in exceptionally endemic sites, setting records in environmental losses by impeding fish movement, altering flood pulse, causing large-scale deforestation, and increasing greenhouse gas emissions. With the burgeoning energy demand combined with the aforementioned negative impacts of conventional hydropower technology, there is an imminent need to re-think the design of hydropower to avoid the potentially catastrophic consequences of large dams. It is certain that the Amazon will undergo some major hydrological changes in the near future because of the compounded effects of climate change and proposed dams, if built with the conventional hydropower technology. In this study, we present a transformative hydropower outlook that integrates low-head hydropower technology (e.g., in-stream turbines) and multiple environmental aspects, such as river ecology and protected areas. We employ a high resolution (~2km) continental scale hydrological model called LEAF-Hydro-Flood (LHF) to assess the in-stream hydropower potential in the Amazon River basin. We particularly focus on quantifying the potential and feasibility of employing instream turbines in the Amazon instead of building large dams. We show that a significant portion of the total energy planned to be generated from conventional hydropower in the Brazilian Amazon could be harnessed using in-stream turbines that utilize kinetic energy of water without requiring storage. Further, we also find that implementing in-stream turbines as an alternative to large storage-based dams could prove economically feasible, since most of the environmental and social costs associated with dams are eliminated. Our results open multiple pathways to achieve sustainable hydropower development in the Amazon to meet the ever-increasing energy demands while minimizing hydrological, social, and ecological impacts. It also provides important insight for sustainable hydropower development in other global regions. The results presented are based on a manuscript under revision for Nature Sustainability.</p>

scholarly journals Climate Change Effects on Hydropower Potential in the Alcantara River Basin in Sicily (Italy)

2013 ◽  
Vol 17 (19) ◽  
pp. 1-22 ◽  
Author(s):  
G. T. Aronica ◽  
B. Bonaccorso
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Coupled Model ◽  
Daily Rainfall ◽  
Hydrological Regime ◽  
Future Climate Change ◽  
Hydropower Generation ◽  
Energy Potential ◽  
Hydropower Potential ◽  
The Impact

Abstract In recent years, increasing attention has been paid to hydropower generation, since it is a renewable, efficient, and reliable source of energy, as well as an effective tool to reduce the atmospheric concentrations of greenhouse gases resulting from human activities. At the same time, however, hydropower is among the most vulnerable industries to global warming, because water resources are closely linked to climate changes. Indeed, the effects of climate change on water availability are expected to affect hydropower generation with special reference to southern countries, which are supposed to face dryer conditions in the next decades. The aim of this paper is to qualitatively assess the impact of future climate change on the hydrological regime of the Alcantara River basin, eastern Sicily (Italy), based on Monte Carlo simulations. Synthetic series of daily rainfall and temperature are generated, based on observed data, through a first-order Markov chain and an autoregressive moving average (ARMA) model, respectively, for the current scenario and two future scenarios at 2025. In particular, relative changes in the monthly mean and standard deviation values of daily rainfall and temperature at 2025, predicted by the Hadley Centre Coupled Model, version 3 (HadCM3) for A2 and B2 greenhouse gas emissions scenarios, are adopted to generate future values of precipitation and temperature. Synthetic series for the two climatic scenarios are then introduced as input into the Identification of Unit Hydrographs and Component Flows from Rainfall, Evapotranspiration and Streamflow Data (IHACRES) model to simulate the hydrological response of the basin. The effects of climate change are investigated by analyzing potential modification of the resulting flow duration curves and utilization curves, which allow a site's energy potential for the design of run-of-river hydropower plants to be estimated.

scholarly journals Comparison of two model approaches in the Zambezi river basin with regard to model reliability and identifiability

2005 ◽  
Vol 2 (6) ◽  
pp. 2625-2661 ◽  
Author(s):  
H. C. Winsemius ◽  
H. H. G. Savenije ◽  
A. M. J. Gerrits ◽  
E. A. Zapreeva ◽  
R. Klees
Keyword(s):  
River Basin ◽  
Hydrological Modelling ◽  
Model Structure ◽  
Remotely Sensed Data ◽  
Determine Parameter ◽  
Independent Observations ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Stream Model ◽  
Zambezi River Basin ◽  
Zambezi River

Abstract. Variations of water stocks in the upper Zambezi river basin have been determined by 2 different hydrological modelling approaches. The purpose was to provide preliminary terrestrial storage estimates in the upper Zambezi for comparison with estimates derived from the Gravity Recovery And Climate Experiment (GRACE). The first modelling approach is GIS-based, distributed and conceptual (STREAM). The second approach uses lumped elementary watersheds identified and modelled conceptually (LEW). The STREAM model structure has been assessed using GLUE (Generalized Likelihood Uncertainty Estimation) a posteriori to determine parameter identifiability. The LEW approach could, in addition, be tested for model structure, because computational efforts of LEW are low. Both models are threshold models, where the non-linear behaviour of the Zambezi river basin is explained by a combination of thresholds and linear reservoirs. The models were forced by time series of gauged and interpolated rainfall. Where available, runoff station data was used to calibrate the models. Ungauged watersheds were generally given the same parameter sets as their neighbouring calibrated watersheds. It appeared that the LEW model structure could be improved by applying GLUE iteratively. Eventually, it led to better identifiability of parameters and consequently a better model structure than the STREAM model. Hence, the final model structure obtained, better represents the true hydrology. After calibration, both models show a comparable efficiency in representing discharge. However the LEW model shows a far greater storage amplitude than the STREAM model. This emphasizes the storage uncertainty related to hydrological modelling in data-scarce environments such as the Zambezi river basin. It underlines the need and potential for independent observations of terrestrial storage to enhance our understanding and modelling capacity of the hydrological processes. GRACE could provide orthogonal information that can help to constrain and further enhance our models. In the near future, other remotely sensed data sources will be used to force modelling efforts of the Zambezi (e.g. satellite rainfall estimates) and to identify individual storage components in the GRACE observations (e.g. altimeter lake levels and microwave soil moisture). Ultimately, this will create possibilities for state updating of regional hydrological models using GRACE.

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