Search

Abstract: The increasing demand for energy, especially from renewable and sustainable sources, spurs the development of small hydropower plants and encourages investment in new survey studies. Preliminary hydropower survey studies usually carry huge uncertainties about the technical, economic and environmental feasibility of the undeveloped potential. This paper presents a methodology for large-scale survey of hydropower potential sites to be applied in the inception phase of hydroelectric development planning. The sequence of procedures to identify hydropower sites is based on remote sensing and regional streamflow data and was automated within a GIS-based computational program: Hydrospot. The program allows spotting more potential sites along the drainage network than it would be possible in a traditional survey study, providing different types of dam-powerhouse layouts and two types (operating modes) of projects: run-of-the-river and storage projects. Preliminary results from its applications in a hydropower-developed basin in Brazil have shown Hydrospot’s limitations and potentialities in giving support to the mid-to-long-term planning of the electricity sector. [ABSTRACT FROM AUTHOR]

• >350 large hydropower dams have been identified for construction in the Amazon basin. • Climate change will induce large-scale alterations in river hydrology basin-wide. • Frequency of zero hydropower generation increases and full-capacity operation decreases. • Mean hydropower generation decreases by ∼ 30% under worst-case climate change scenario. • Future Amazon hydropower's levelized cost of electricity ($ MWh−1) increases substantially under climate change. Numerous hydropower facilities are under construction or planned in tropical and subtropical rivers worldwide. While dams are typically designed considering historic river discharge regimes, climate change is likely to induce large-scale alterations in river hydrology. Here we analyze how future climate change will affect river hydrology, electricity generation, and economic viability of > 350 potential hydropower dams across the Amazon, Earth's largest river basin and a global hotspot for future hydropower development. Midcentury projections for the RCP 4.5 and 8.5 climate change scenarios show basin-wide reductions of river discharge (means, 13 and 16%, respectively) and hydropower generation (19 and 27%). Declines are sharper for dams in Brazil, which harbors 60% of the proposed projects. Climate change will cause more frequent low-discharge interruption of hydropower generation and less frequent full-capacity operation. Consequently, the minimum electricity sale price for projects to break even more than doubles at many proposed dams, rendering much of future Amazon hydropower less competitive than increasingly lower cost renewable sources such as wind and solar. Climate-smart power systems will be fundamental to support environmentally and financially sustainable energy development in hydropower-dependent regions. [ABSTRACT FROM AUTHOR]

Abstract: Length of river reaches is one of the most important characteristics of stream networks when applying hydrological or environmental simulation models. A common method of obtaining estimates of river lengths is based on deriving flow directions, accumulated area and drainage lines from raster digital elevation models (DEM). This method leads to length estimates with variable accuracy, which depends on DEM horizontal resolution, flatness of terrain, DEM vertical accuracy, the algorithm used to obtain flow directions and the way by which distances are calculated over raster structures. We applied an automatic river length extraction method for eight river reaches in the River Uruguay Basin (206000km2), in Southern Brazil, and compared its results to the lengths obtained from drainage vector lines digitalized over satellite images. Our results show that relative errors can be higher than 30% in flat regions with relatively low DEM resolution. Preprocessing of DEM by the method known as stream burning greatly improves results, reducing errors to the range 1.9–7.4%. Further improved estimates were obtained by applying optimized values for the length of orthogonal and diagonal steps called distance transforms, reducing the errors to the range −2.0–3.3%. [Copyright &y& Elsevier]

Mathematical modeling is an important tool for water quality studies, and the integration of water quality models with geographic information systems (GIS) is very useful for information extraction and for results interpretation. In this context, this work presents the development of a water quality model coupled with GIS (MapWindow GIS) for representing impacts of point-sourced pollutants released with distinct durations under different flow scenarios, allowing a systemic view of the entire basin, and capable of being used with low data availability. The model is called SIAQUA-IPH and uses a pollutograph convolution scheme to represent multiple discharges and confluences in the basin, based on analytical solutions of the longitudinal advection-dispersion equation. Operational tests presented a full operational performance from all technical solutions adopted, and a representation of plumes considered satisfactory in comparison to observations. Additionally, a simple sensitivity analysis is presented, that gives useful insights about the model application. [ABSTRACT FROM AUTHOR]

Abstract: We present a model describing phytoplankton growth in Lake Mangueira, a large subtropical lake in the Taim Hydrological System in South Brazil (817km2, average depth 2m). The horizontal 2D model consists of three modules: (a) a detailed hydrodynamic module for shallow water, which deals with wind-driven quantitative flows and water level, (b) a nutrient module, which deals with nutrient transport mechanisms and some conversion processes and (c) a biological module, which describes phytoplankton growth in a simple way. We solved the partial differential equations numerically by applying an efficient semi-implicit finite differences method to a regular grid. Hydrodynamic parameters were calibrated to continuous measurements of the water level at two different locations of the lake. An independent validation data set showed a good fit of the hydrodynamic module (R 2 ≥0.92). The nutrient and biological modules were parameterized using literature data and verified by comparing simulated phytoplankton patterns with remote sensing data from satellite images and field data of chlorophyll a. Moreover, a sensitivity analyses showed which parameters had the largest influence on the simulated phytoplankton biomass. The model could identify zones with a higher potential for eutrophication. It has shown to be a first step towards a management tool for prediction of the trophic state in subtropical lakes, estuaries and reservoirs. [Copyright &y& Elsevier]

Large-scale hydrological models are useful tools for water resources studies, however, river network flow routing is generally represented using simplified methods, which may lead to simulation errors in flat regions. We present recent improvements to the large-scale hydrological model MGB-IPH to improve its capability of simulating large river basins with extensive floodplains. We also describe the coupling of MGB-IPH to an open source GIS and a large set of developed pre-processing tools with a user-friendly interface for remote sensing data preparation and output visualization. The new features implemented are demonstrated applying the model to the whole Araguaia river basin (380,000 km 2 ). Results are compared to the previous MGB-IPH routing method, observed flow and water level data and remote sensing imagery, showing improvement in the representation of floodplain inundation dynamics. The test case also shows that the proposed model software framework amplifies possibilities of large-scale simulation of ungauged basins. [ABSTRACT FROM AUTHOR]