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Streamflow forecasts from continental to global scale hydrological models have gained attention, but their performance against operational forecasts at local to regional scales must be evaluated. This study assesses the skill of medium-range, weekly streamflow forecasts for 147 large Brazilian hydropower plants (HPPs) and compares their performance with forecasts issued operationally by the National Electric System Operator (ONS). A continental-scale hydrological model was forced with ECMWF medium-range forecasts, and outputs were corrected using quantile mapping (QM) and autoregressive model approaches. By using both corrections, the percentage of HPPs with skillful forecasts against climatology and persistence for 1–7 days ahead increased substantially for low to moderate (9% to 56%) and high (72% to 94%) flows, while using only the QM correction allowed positive skill mainly for low to moderate flows and for 8–15 days ahead (29% to 64%). Compared with the ONS, the corrected continental-scale forecasts issued for the first week exhibited equal or better performance in 60% of the HPPs, especially for the North and Southeast subsystems, the DJF and MAM months, and for HPPs with less installed capacity. The findings suggest that using simple corrections on streamflow forecasts issued by continental-scale models can result in competitive forecasts even for regional-scale applications. [ABSTRACT FROM AUTHOR]

Defining river networks from a digital elevation model (DEM) is a critical step in many hydrological studies. Most methods rely on analyses of flow accumulation based on a single drainage area threshold value. Landscape heterogeneity hinders the definition of drainage pixels by exceeding a threshold. In this study, we propose a method that automatically accounts for variable drainage density in the definition of river networks from relatively coarse DEMs by using the topographic position index (TPI) as an auxiliary variable, therefore calling it the Topographic Position-based Stream definition (TPS). We tested the TPS method with the 90 m resolution MERIT-DEM (Multi-Error-Removed Improved-Terrain DEM) in Brazil, applying it to three river basins with contrasting landforms and climates. The method improved drainage representation in all analysed regions. It successfully represented stream networks with intra-basin variable drainage density by using a single DEM and three parameters, showing potential for hydrological applications at multiple spatial scales. [ABSTRACT FROM AUTHOR]

Large-scale hydrologic–hydrodynamic models are powerful tools for integrated water resources evaluation at the basin scale, especially in the context of flood hazard assessment. However, recent model developments have paid little attention to simulate reservoirs' hydrodynamics within river networks. This study presents an adaptation of the MGB model to simulate reservoirs as an internal boundary condition, enabling the explicit simulation of hydrodynamic processes along reservoirs and their interaction with upstream and downstream floodplains in large basins. A case study is carried out in the Itajaí-Açu River Basin in Brazil, which has periodic flood-related disasters and three flood control dams. The model was calibrated for the 1950–2016 period forced with daily observed precipitation. The adjustment was satisfactory, with Nash–Sutcliffe metrics between 0.54 and 0.84 for the 11 gauges analyzed and with flood frequency curves also well represented. Simulation scenarios with and without floodplains and reservoirs were performed to evaluate the relative role of these factors on flood control basin-wide through evaluation of simulated discharges, water levels and flood extent. Itajaí do Oeste tributary and Itajaí-Açu mainstem present major floodplain attenuation, while in Itajaí do Sul and Itajaí do Norte tributaries the main flood control occurs due to reservoir attenuation. Downstream from the dams, results indicated that the reservoirs reach their maximum discharge reduction capacity for 5- to 10-year floods, decreasing it for larger floods. The developed model may be very useful for operational uses as flood forecasting and coordinated reservoir operation studies, as well as to enhance the comprehension of flood dynamics at basin scale. [ABSTRACT FROM AUTHOR]

The paper compares forecasts of mean monthly water levels up to six months ahead at Ladário, on the Upper Paraguay River, Brazil, estimated from two long-range dependence models. In one of them, the marked seasonal cycle was removed and a fractionally differenced model was fitted to the transformed series. In the other, a seasonal fractionally differenced model was fitted to water levels without transformation. Forecasts from both models for periods up to six months ahead were compared with forecasts given by simpler "short-range dependence" Box-Jenkins models, one fitted to the transformed series, the other a seasonal autoregressive moving average (ARMA) model. Estimates of parameters in the four models (two "long-range dependence", two "short-range dependence") were updated at six-monthly intervals over a 20 year period, and forecasts were compared using root mean square errors (rmse) between water-level forecasts and observed levels. As judged by rmse, performances of the two long-range dependence models, and of the ARMA (1,1) short-range dependence model, were very similar; all three out-performed the seasonal short-range dependence ARMA model. There was evidence that all models performed better during recession periods, than on the hydrograph rising limb. [ABSTRACT FROM AUTHOR]

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]

Summary: Rainfall measurements by conventional raingauges provide relatively accurate estimates at a few points of a region. The actual rainfield can be approximated by interpolating the available raingauge data to the remaining of the area of interest. In places with relatively low gauge density such interpolated rainfields will be very rough estimates of the actual events. This is especially true for tropical regions where most rainfall has a convective origin with high spatial variability at the daily level. Estimates of rainfall by remote sensing can be very useful in regions such as the Amazon basin, where raingauge density is very low and rainfall highly variable. This paper evaluates the rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) satellite over the Tapajós river basin, a major tributary of the Amazon. Three-hour TRMM rainfall estimates were aggregated to daily values and were compared with catch of ground-level precipitation gauges on a daily basis after interpolating both data to a regular grid. Both daily TRMM and raingauge-interpolated rainfields were then used as input to a large-scale hydrological model for the whole basin; the calculated hydrographs were then compared to observations at several streamgauges along the river Tapajos and its main tributaries. Results of the rainfield comparisons showed that satellite estimates can be a practical tool for identifying damaged or aberrant raingauges at a basin-wide scale. Results of the hydrological modeling showed that TRMM-based calculated hydrographs are comparable with those obtained using raingauge data. [Copyright &y& Elsevier]

One of the main causes of water conflicts in transboundary watersheds all over the world is represented by the increasing water demand due to urban, industrial, and agricultural development. In this context, water scarcity plays a critical role since, during a drought period, water supply is not sufficient to cover the demand of all water uses. In this work, we have conceptualized and developed a new scenario-based framework able to improve the sustainability and equity of water allocation among two or more riparian countries. The proposed approach is in accordance with the United Nations Watercourses Convention. It considers a hydraulic/hydrologic model, a water-management model, and combines them with multi-criteria decision analysis (MCDA) and what if scenario analysis (WISA). The suggested framework was applied to the transboundary watershed of Cuareim/Quaraí river (Uruguay/Brazil) to tackle a real water-sharing conflict. It resulted in being very flexible in exploring various policy options and test and quantifying them with different scenarios to reach an objective and impartial decision in a water-sharing issue. This framework can effectively be applied to any other transboundary watershed to resolve any possible conflict related to water-allocation/water-management matter. [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]

Regional to global river hydrodynamic models are now a reality, and have largely improved in recent years due to increasing computation power and new modeling and remote sensing techniques. Despite all recent advances, it is still necessary to understand to which extent these models can provide locally relevant estimates of variables as discharge, water surface elevation and flood inundation, and how current and future remote sensing data should be used to improve them. We compare here three versions of a river hydrodynamic model (MGB) at local, regional and continental scales. It was applied to the Itajaí-Açu basin in Brazil (15,000 km²) using river spatial discretization, river bathymetry and floodplain DEM representative of local, regional and continental scales. Results are compared to a detailed HEC-RAS model applied at basin scale (>600 cross sections over ~850 km) as a benchmark reference. To evaluate model accuracy, discharge, water levels and flood extent metrics were classified into locally relevance categories based on the accuracy requirement for flood related studies, typical reach scale models' errors, and observation uncertainties. The hydrodynamic model at all scales provided locally relevant discharge estimates, while regional and local versions yielded satisfactory water level anomalies. Less accurate estimates of water level and flood extent indicated the general difficulty on predicting these variables by current large-scale hydrodynamic models. Smaller river reaches (i.e., 1 km river reaches) improved discharge, water level and flood extent estimates. A locally derived DEM did not lead to significant improvements in relation to a global DEM. The cross-scale comparisons also indicate the potential of improving river bathymetry, by means of in situ surveys or remotely sensed river widths and altimetry. In the context of remote sensing, studies like these provide interesting insights on how using future remote sensing data to improve large-scale hydrodynamic models. [ABSTRACT FROM AUTHOR]

Hydrological models are useful to support ecosystem services restoration. However, when planning the expansion of Nature-based Solutions and Payments for Ecosystem Services, there are requirements including the principle of additionality, representation of multiple runoff mechanisms and calculation of farm-scale spatial outputs and epistemic uncertainty. While addressing these aspects is important to make future planning more effective (investment costs and water benefits) it is also challenging. This paper integrates, in a single modeling framework, the main requirements highlighted. This allows an improved selection of top priority areas with farm-scale spatial resolution and a deeper understanding of how epistemic modeling uncertainty affects the results, which is relevant to evaluate the risks of overestimating ecosystem services benefits. The modeling approach is demonstrated in a case study watershed in the Atlantic Forest biome (Brazil). The PLANS model is introduced, which uses the design of TOPMODEL to simulate both simulates both saturation-excess and infiltration-excess runoff at the farm-scale resolution. Also, it uses a novel saturation index based in a composition of Height Above the Nearest Drainage and Topographical Wetness Index terrain descriptors. We applied the Generalized Likelihood Uncertainty Estimation method, aided by an evolutionary algorithm, to estimate output epistemic uncertainty. Results show that uncertainty substantially affects priority definition (97% ranking change), and that simulated topographic effects can overcome local effects of land cover and soil type. With better data, uncertainty evaluation indicates that the restoration program in the study case could have the cost of its allocation policy reduced by up to 27%, showing that it can be made more cost- effective with the methods proposed. [Display omitted] [ABSTRACT FROM AUTHOR]

• An algorithm for automated endmember selection was assessed in geeSEBAL. • A sensitivity analysis of the automated calibration was conducted. • New set of quantiles were proposed to increase E T accuracy in humid climates. • Impacts of global meteorology in geeSEBAL were also assessed. The application of energy balance models for estimation of evapotranspiration (E T) still has challenges to be addressed for large scale applications. The algorithm for automated calibration using inverse modeling at extreme conditions (CIMEC) is based on the definition of endmembers that represent the extreme conditions of the E T spectrum, between hot (dry and sparse vegetation) and cold (wet and dense vegetation) surfaces, with pre-defined quantiles for the endmember selection. The main goal was to assess geeSEBAL algorithm uncertainties related to the (i) automated calibration, including the use of additional filters (land cover, homogeneity, and domain area) and (ii) the use of a global climate grid as input data. Based on a sensitivity analysis, we defined new set of quantiles to increase the accuracy of E T estimates in subtropical humid climates, since the default quantiles were adjusted to semiarid climates with dry summers. To validate our E T estimates we used eddy covariance measurements from five flux towers located in the South of Brazil. Processing 132 Landsat cloud free images and using adjusted quantiles, we found a root mean square error (RMSE) of 0.91 mm d −1 and a coefficient of determination (R²) of 0.82 with geeSEBAL driven by meteorological measurements. Using the pre-defined quantiles, we found an RMSE of 1.16 mm d −1 (27% higher) and R² of 0.75. The upscaling instantaneous E T to daily E T resulted in an underestimation of the daily E T using the pre-defined quantiles, while the optimized quantiles corrected the daily estimates. Furthermore, our results suggested a low sensitivity of geeSEBAL to meteorological inputs, since RMSE slightly increased to 1.04 mm d −1 (14.3% higher) and R² decreased to 0.76 (8.5% smaller) when driven by global climate data. For data scarce areas, geeSEBAL is a feasible alternative for cropland E T estimation and water resources management in subtropical humid climates. [ABSTRACT FROM AUTHOR]

While the development of flood warning systems on a national and continental scale are a reality in various places on the planet such as North America, Europe and Africa. In South America, most of the existing operational systems have as their application limits the watershed boundaries and the aim of supporting decision making for hydroelectric generation. At the same time, the agencies responsible for operating flood early warning systems and alerting about floods in countries such as Brazil are generally linked to national or regional government, with political boundaries differing from hydrographic boundaries. In this scenario, the present work presents the first development of a state-wide forecasting system in South America. The area covered by the system consists of the State of Rio Grande do Sul, located in the South of Brazil, bordering Uruguay and Argentina. In this way, the forecasts generated with the system are not only useful for the Brazilian side but are also useful for neighboring countries, helping on trans-boundary flood related issues. The system is operated in the Situation Room of the Department of Water Resources at the state level, generating one forecast per day, which is used to support the issuance of alert bulletins for the entire state. In addition, the results serve as a complement to existing local forecasts, focused on individual rivers. In the system, flood forecasts are obtained from the use of meteorological forecasts from a version of the WRF model that feeds the widely applied in South America hydrological model MGB (Modelo de Grandes Bacias) calibrated for the entire study region. The system has been in operation since September 2018, and the first results of its usage are shown in the present research, as well as the challenges of implementing, operating and disseminating the forecasts results. In a broader point of view, we expected that those experiences will help to improve the practices of a future South American Flood Awareness System, currently under development. [ABSTRACT FROM AUTHOR]