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The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals. [ABSTRACT FROM AUTHOR]

The Serra da Mantiqueira Environmental Protection Area (SMEPA) is one of the most important and unique Brazilian conservation units of the Atlantic Forest biome, located in the eponymous relief unit. Although the environmental and water resources relevance of the SMEPA is widely known, it lacks specific studies about the hydrologic behavior of the region. This paper aims to contribute to the understanding of the rainfall behavior over the SMEPA, by detecting monotonic trends of total annual and seasonal rainfalls. The study was performed by applying the Mann-Kendall test to 37 years series from 23 rainfall stations located within and around the SMEPA. No trends were detected in total annual and spring seasonal rainfall. Few stations (one during summer, two during autumn and two during winter) presented statistically significant trends, indicating that there is no regional trend over the SMEPA region. The isolated significant trends detected suggest the existence of sites with distinct rainfall behavior in the mesoscale. [ABSTRACT FROM AUTHOR]

The Cantareira Water Production System (CWPS), which supplies water to the most populous region in Brazil, is facing significant challenges due to hydroclimate change, thus threatening its water security. This research integrates data from climate models and field observations with hydrological modeling, aimed at quantifying trends in key variables of the hydrological cycle. The GFDL-CM4 climate model, the most suitable for the study area, was employed to generate runoff data under both current conditions and future scenarios (SSP2-4.5 and SSP5-8.5). Our analysis reveals an increasing trend in the frequency of dry hydrological years. The Standard Precipitation Index (SPI) and Drought Magnitude (DM) confirm an increase in both the occurrence and duration of droughts in future scenarios. The runoff in all basins was reduced, causing a substantial decrease in minimum flows of 16.9%, medium flows of 11.8%, and high flows of 9.2% for the SSP5-8.5 scenario. This research introduces an approach to hydroclimate impact assessment, combining rigorous data analysis with advanced modeling techniques. Our findings not only provide a comprehensive understanding of the challenges faced by the CWPS, but also offer critical quantitative insights essential for developing effective public policies and adaptive strategies for sustainable water resource management. [ABSTRACT FROM AUTHOR]

Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning. [ABSTRACT FROM AUTHOR]

In hydrological modelling, it is important to consider the uncertainties related to a model's structures and parameters when different hydrological models are used to represent a system. Therefore, an adequate analysis of daily discharge forecasts that takes into account the performance of hydrological models can assist in identifying the best extreme discharge forecasts. In this context, this study aims to evaluate the performance of three hydrological models—Lavras Simulation of Hydrology (LASH), Variable Infiltration Capacity (VIC), and Distributed Hydrological Model (MHD-INPE) in the Verde River basin. The results demonstrate that LASH and MHD can accurately simulate discharges, thereby establishing them as crucial tools for managing water resources in the study region's basins. Moreover, these findings could serve as a cornerstone for future studies focusing on food and water security, particularly when examining their connection to climate change scenarios. [ABSTRACT FROM AUTHOR]

Floods are a recurring natural phenomenon during the rainy season in many Brazilian municipalities. Nevertheless, shifts in weather patterns have contributed to an increased incidence of these events in urban areas, where their impact can be amplified by the way the surrounding catchment is occupied. Hence, the present study sought to evaluate the susceptibility of the urban drainage infrastructure in João Monlevade, Brazil, to the effects of climate change by undertaking a comprehensive assessment of the Carneirinhos catchment, including its morphometric characteristics. For this purpose, we employed a hydrological model driven by regional rainfall projections from a high-resolution climate model (HadGEM2-ES downscaled to 5 km resolution) under the Representative Concentration Pathways (RCP8.5) scenario. Several combinations of rainfall occurrence were simulated, incorporating temporal aspects (different durations and return times), as well as spatial aspects (concentrated and distributed rainfall within the catchment). The results showed that the area of exposed soil in the Carneirinhos catchment experienced an increase of more than 140% from 2016 to 2019, favoring runoff surface and evaporation, which contributed to the increase in the number of flood events in the region. In addition, only 1 of the 56 heavy rainfall event simulations performed did not exceed the capacity of the macro drainage gallery. [ABSTRACT FROM AUTHOR]

Hydrological models allow water levels to be predicted at critical spots when the problem of flooding is being addressed. However, these models fall short in their attempts to provide timely warnings to communities at risk as they often involve complex setup requirements and incur high computation costs. Other approaches have been adopted that make use of water level monitoring sensors for detecting floods. Although accurate in their performance, these approaches often require a high level of maintenance because their predictions rely on critical readings from sensors that have to be immersed in rivers. We recommend a machine learning-based methodology for flood detection to address this issue, called FLORAS. It makes it possible to build models that make predictions solely on the basis of meteorological data from weather stations-water height measurements are only needed to employ ground truth for the purposes of training and validation. We evaluated the methodology with current data readings from São Carlos (SP - Brazil) in experimental analyses. Water height measurements from sensors placed at sites along the river were correlated with open weather data from a reputable, local source (Climatempo – Brazilian weather). The results show that the model achieved a higher degree of of accuracy and incurred lower computational costs than SwMM, a hydrological model. These results show that the recommended methodology is suitable for systems that run with resource-scarce devices, such as the IoT systems that are usually deployed in flood detection frameworks. [ABSTRACT FROM AUTHOR]

Flash floods are natural hazards and often occur in small and mountainous river basins with low monitoring. The hydrological and hydrodynamic reconstruction of past rainfall events is useful for understanding the phenomena that led to a flood. This study aims to reconstruct a rainfall event that triggered landslides and floods in 2017 in the Rolante basin (771 km2), southern Brazil, a region with low monitoring. Due to the large magnitude of the flood event, a question was raised whether only the basin response to intense rainfall could have caused that flood. Therefore, different rainfall scenarios were tested with the use of official rain gauges and unofficial rainfall information from local farmers to determine the spatial and temporal distribution of rainfall. The reconstruction of the rainfall event was performed with the use of a hydrologic model (HEC-HMS) to define hydrographs and a hydrodynamic model (Nays2D Flood) to simulate flood propagation, with adjusted methods for the poorly monitored basin. The maximum flood depth and extent were analysed for three rainfall scenarios. The results showed that, with the information provided by the residents, it was possible to determine that extreme and concentrated rainfall occurred in the mountainous area and the basin ordinary response to that rainfall may have caused a flood of that great magnitude. The analysis of past extreme events can contribute to verifying if there are changes in the rainfall patterns and can assist in risk mitigation and disaster management, primarily in ungauged basins. [ABSTRACT FROM AUTHOR]

This study quantified hydrological impacts caused by climate and anthropogenic changes in nine basins in the Upper Grande River Basin (URGB) region and estimated potential future impacts on runoff based on climate model projections. The Budyko framework was used as a conceptual approach to estimate the causes of runoff variation by the decomposition method. Assuming that land use will not change in future, the runoff was estimated for 2006–2040, 2041–2070, and 2071–2099 time-slices using regionalized projections from four different climate models. The decomposition method indicated a strong influence of both climate and land-use changes in the hydrological regime in the URGB, with the expansion of the agricultural areas being the most important driver of anthropogenic impacts. Future climate projection multi-model ensembles indicated a reduction in average runoff. The Budyko framework performance on future projections presented similar trends to hydrological modeling estimations from previous studies in the same region, indicating the suitability of the method as an additional tool for assessing future climate change impacts on water availability. [ABSTRACT FROM AUTHOR]

The current paper presents the evaluation of the ability of the SWAT hydrological model (Soil and Water Assessment Tool) to reproduce continuous daily streamflows of the upper Itapemirim River basin (Brazil). The model parameters were calibrated and validated with data collected from 1993 to 2000. The model was found to be highly sensitive to baseflow, its primary calibration variable. Validation analyses resulted in values of statistical indexes (NS = 0.67; NSlog = 0.68; PBIAS = 22% and RSR = 0.57) that show that SWAT performance was satisfactory in this application. [ABSTRACT FROM AUTHOR]

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall‐runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (IA) and baseflow index (IB). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (IA < 0.9) with low baseflow (IB < 0.4) were the easiest to model, with a single‐reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall‐runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (IB < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (IA < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features. Key Points: Baseflow and aridity help classify suitable model structures for different catchments in BrazilA single bucket model performs relatively well in wet catchments with low baseflow, mainly in southern BrazilIndependently of model structure, performance is generally lower in drier than in wetter catchments [ABSTRACT FROM AUTHOR]

Summary: With the objective of understanding the potential and limitations of available precipitation products for hydrological studies, this paper compares six daily and sub-daily precipitation datasets and their impacts on the water balance of the Negro River basin in the Amazon basin. The precipitation datasets contain gauge-based data [data derived from the Hybam Observatory Precipitation (HOP) dataset and provided by the Climate Prediction Center (CPC)], satellite-based data [the Global Precipitation Climatology Project (GPCP) one-degree daily and TRMM Multisatellite Precipitation Analysis (TMPA) datasets] and model-based data [the NCEP-DOE AMIP-II re-analysis (NCEP-2) and 40-year ECMWF Re-Analysis (ERA-40) datasets]. Each dataset has a common set of meteorological forcing data which are used to run the MGB-IPH hydrological model for the period from January 1998 to August 2002. The average precipitation of all the datasets is 2542mm for the Negro River basin, with a standard deviation of 317mm. TMPA and NCEP-2 have the lowest (2216mm/year) and the highest (3065mm/year) precipitation rates, respectively. The HOP and CPC datasets agree best with observed discharge. GPCP gives the best results among the ungauged datasets, followed by ERA-40. TMPA and NCEP-2 are found to be the least accurate. TMPA can reproduce the water cycles reasonably well, but underestimates the precipitation fields and discharges over the basin, while NCEP-2 is unable to represent the rainfall quantity and cycles, and the water discharge. Results suggest that gauge-based data are still the most representative of the actual precipitation in the northern Amazon basin. However, some satellite and model-based can reproduce fairly well the water cycle at the basin scale and monthly time step. [ABSTRACT FROM AUTHOR]

Summary: The relations between hydrology, geomorphology and vegetation provide the basis for understanding the ecological processes in the floodplains of rivers. In this paper, long-term (1969–2009) local flood characteristics (magnitude, duration, frequency, return period) are quantified for the floodplain of the Cuiabá River (Pantanal wetland in Brazil), using a predictive model based on artificial neural networks (ANNs) with input given by the historic river-level record. Morphological features were then described and their relation to vegetation analyzed and used to reconstruct floods. In addition to the river-level data used to train and validate a three-layer ANN, data were also used from 11 floodplain gauges across a 12km transect lateral to the river, which were monitored during the annual floods of 2004–2007. The resulting neural network gave satisfactory estimates of water depth over the floodplain, with an absolute error of 0.09m not exceeded in 95% of occurrences. The ANN showed that in almost all years, the level reached by the river was high enough to flood the profile completely: the exceptions were 1971 and 2001 when only 50% and 58% was flooded, to mean depths of 0.34 and 0.48m, respectively. The highest flood was that of 1995 when the floodplain was flooded to a mean depth of 2.56m. The median flood event (return period 2years) produced a mean flood depth 1.80m and lasted 119days. Among the most important morphological features is the presence of palaeo-channels which provide hydrological connectivity between the river and the floodplain. The distribution of phytophysiognomic units is significantly influenced by local geomorphology, which determines spatial variation in the magnitude, duration, and frequency of flooding. [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]

Process-based hydrological models are of great importance to understand hydrological processes and support decision making. The LImburg Soil Erosion Model (LISEM) requires information on soil and land-use-related attributes to represent the transformation of rainfall into runoff for isolated rainfall events. This study aimed at evaluating LISEM for estimation of direct surface runoff (DSR) hydrographs in a watershed in Southern Brazil under the predominance of long-duration rainfall events, dominated by Argisols and with availability of a high-density rain gauge network. In addition, this study sought to: (i) suggest and evaluate a procedure for definition of initial soil moisture from antecedent 5-day rainfall depth; (ii) reduce the degree of subjectivity involved in the determination of some vegetation-related parameters by using remote sensing; and (iii) recommend a validation procedure. The saturated soil hydraulic conductivity and the Manning's surface roughness coefficient were calibrated considering 11 rainfall–runoff events, whereas the validation was performed for 4 events from the average calibrated parameters. The Nash–Sutcliffe coefficient was used to assess both calibration and validation, resulting in average values of 0.64 and 0.58, respectively. It can be inferred from the results that the use of remote sensing to derive some LISEM parameters, along with the suggested schemes for definition of initial soil moisture and validation, was effective and provided sound results even for long-duration rainfall events. The results of this study and its methodological procedures can serve as a basis for other professionals who intend to use LISEM for both conducting detailed analyses of DSR hydrographs and supporting water resources management. [ABSTRACT FROM AUTHOR]

The present work aimed at building the hydrological model for the Paraíba do Sul River watershed, using the MOHID Land model, in order to study channel flows considering six different precipitation inputs. First, two scenarios were created with temporal variation of rain from the precipitation measured by 17 automatic stations of the Brazilian National Institute of Meteorology (INMET) located in the watershed, and interpolated with the FillMatrix tool, by the Inverse Distance Weighting (TIN) and Triangulated Irregular Networks (IDW) methods. The other four scenarios were created from four experiments simulated in the atmospheric model WRF-ARW, corresponding to the spatial and temporal variation of precipitation. The hydrological model considered the Manning coefficients, Curve Numbers and cross sections extracted from the database of the Brazilian National Water Agency (ANA) together with the operation curve of the reservoirs extracted from the situation room on the Geographical and Geoenvironmental Information System of the Paraíba do Sul River Watershed (SIGA-CEIVAP) website. The analysis period corresponds to the month of January 2019 (01/01/2019 00 UTC and 01/02/2019 00 UTC), with hourly evaluation. The measured channel flow data used were extracted from the ANA fluviometric station, in the Campos dos Goytacazes city, State of Rio de Janeiro, and they were used for the statistical indices calculation, BIAS, MAE, MAPE, RMSE and Pearson's Correlation (R), in order to evaluate the model. The results showed that once the physical parameterization of better performance in the atmospheric model (WRF - Weather Research and Forecasting) was established, the use of the modeled rain yields a superior performance in comparison to the interpolated (observed) rain for implementation in the hydrological model (MOHID - MODagem HIDrodinâmica), with the aim to study the channel flows. [ABSTRACT FROM AUTHOR]

Fire significantly affects hydrological processes in the watershed because it changes land cover and it creates a double layer of hydrophobic soil covered with ash, increasing the surface runoff and the production of debris flow in the basin. Assessing the impacts of fire on overland flow requires the use of modeling softwares capable of simulating post-fire discharge. Because a total of 760 wildfires were detected in the Upper Uberabinha River subbasin in the last nine years, it is of dire importance to understand the consequential impacts of fire on hydrological processes in this basin. In this study, the HEC-HMS model was used to evaluate post-fire discharge in the Upper Uberabinha River watershed. Model was previously calibrated and validated using two representative storms observed in the wet season. After calibration, the 5-, 10-, 25-, 50-, 100-, and 200-year storms were simulated in scenarios with increasing burn severity. The calibrated model performed well in the prediction of discharge values at a daily basis (0% difference in peak timing; 0% difference in peak flow; 31.8% BIAS). Peak flow and discharge volume increased and peak timing shifted to the left as severity of burn increased. The highest increment in peak discharge was 74.7% for the 10-year storm, whereas overall discharge volume raised in up to 31.9% for the 50-year storm, both after simulation in the most fire-impacted scenario. The results reveal that fire highly affects hydrological characteristics, e.g. peak timing and flow and discharge volume, in the Upper Uberabinha River watershed. The authors suggest further investigations concerning the impacts of wildfire on other processes, such as the production of debris flow in the basin. [ABSTRACT FROM AUTHOR]

A semi-distributed hydrological model is developed, calibrated and validated against unregulated river discharge from the Tocantins-Araguaia River Basin, northern Brazil. Climate change impacts are simulated using projections from the 41 Coupled Model Intercomparison Project Phase 5 climate models for the period 2071–2100 under the RCP4.5 scenario. Scenario results are compared to a 1971–2000 base line. Most climate models suggest declines in mean annual discharge although some predict increases. A large proportion suggest that the dry season experiences large declines in discharge, especially during the transition to the rising water period. Most models (>75%) suggest declines in annual minimum flows. This may have major implications for both current and planned hydropower schemes. There is greater uncertainty in projected changes in wet season and annual maximum discharges. Two techniques are investigated to reduce uncertainty in projections, but neither is able to provide more confidence in the simulated changes in discharge.Editor D. Koutsoyiannis Associate editor F. Hattermann [ABSTRACT FROM PUBLISHER]

A comprehensive assessment of water security can incorporate several water-related concepts, while accounting for Blue and Green Water (BW and GW) types defined in accordance with the hydrological processes involved. Here we demonstrate how a quantitative analysis of provision probability and use of BW and GW can be conducted, so as to provide indicators of water scarcity and vulnerability at the basin level. To illustrate the approach, we use the Soil and Water Assessment Tool (SWAT) to model the hydrology of an agricultural basin (291 km2) within the Cantareira Water Supply System in Brazil. To provide a more comprehensive basis for decision making, we analyze the BW and GW-Footprint components against probabilistic levels (50th and 30th percentile) of freshwater availability for human activities, during a 23 year period. Several contrasting situations of BW provision are distinguished, using different hydrological-based methodologies for specifying monthly Environmental Flow Requirements (EFRs), and the risk of natural EFR violation is evaluated by use of a freshwater provision index. Our results reveal clear spatial and temporal patterns of water scarcity and vulnerability levels within the basin. Taking into account conservation targets for the basin, it appears that the more restrictive EFR methods are more appropriate than the method currently employed at the study basin. The blue/green water-based accounting framework developed here provides a useful integration of hydrologic, ecosystem and human needs information on a monthly basis, thereby improving our understanding of how and where water-related threats to human and aquatic ecosystem security can arise. [ABSTRACT FROM AUTHOR]

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Reliable hydrometeorological forecasts are necessary to help reduce damages caused by extreme events. Coupling atmospheric and hydrological models can help improve those forecasts; therefore, the main objective of this work is to propose, apply, and evaluate a one-way coupled system involving an atmospheric and a hydrological model. The models were applied to a watershed in the Serra do Mar region, São Paulo State, Brazil. Because of the complex terrain and native tropical forest (Mata Atlântica), hydrometeorological modeling in this region requires specific studies and adequate models. The coupling was carried out between the atmospheric regional Eta model and the distributed hydrology soil vegetation model. The primary challenge in coupling hydrological and atmospheric models is the large difference in spatial and temporal scales in which these models operate. This study proposes a downscaling methodology to increase the spatial resolution of precipitation and temperature predicted by the atmospheric model from 10 to 2 km. The coupling was tested in three precipitation events that accumulated large amounts of precipitation. The results show that the quality of the predicted streamflows depends primarily on the quality of the predicted precipitation that is input to the hydrological model. The downscaling methodology applied does not significantly influence quality of the simulated and predicted basin outflow. [ABSTRACT FROM AUTHOR]

This study investigated the sensitivity of a dynamic downscaling atmospheric model system coupled with a rainfall-runoff model to hindcast an example of reservoir water management in the semi-arid region of Northeast Brazil (NEB). A regional atmospheric spectral model (RSM) is driven by the outputs of an atmospheric general circulation model (AGCM), itself forced by the observed sea surface temperature over the World Ocean. Daily precipitation simulated by the RSM was then used as the input to a hydrological rainfall-runoff model for the Upper Jaguaribe River Basin to estimate inflows at the Orós Reservoir in the state of Ceará. A hindcast analysis of precipitation was performed during the rainy season over NEB (January to June) from 1971 to 2000. The RSM captured the precipitation variability relatively well when a probability density function (PDF) was used to correct the numerical bias. Three hindcast series of inflow using (i) the observed rainfall, (ii) the simulated rainfall before the PDF correction, and (iii) the simulated rainfall after the PDF correction were performed during the study period and then compared to the series of observed inflow. The atmospheric-rainfall-runoff 'cascade' model efficiency was evaluated by comparing the Orós Reservoir release decisions from different scenarios based on observed, simulated (RSM, RSM-PDF), and mean historical reservoir inflows. The cascade model has the potential, relatively well balanced during dry, normal or wet years, to be a useful tool to correctly forecast the decision managements of reservoirs in the semi-arid region of NEB. Additional progress in the numerical simulation is however necessary to improve the performance. [ABSTRACT FROM AUTHOR]

Dense reservoir networks, with thousand of small dams, can be increasingly found throughout the world, especially in water-scarce environments, such as the Brazilian northeastern region. Although the effect of individual small dams might be negligible, their joint effect has proved to be relevant on water and sediment connectivity. Literature, however, is scarce concerning the effect of such networks on water availability and/or sustainability of mesoscale or large-scale basins. This research intended both to assess the effect of the dense reservoir network of the semiarid Upper Jaguaribe Basin (UJB; 24,200 km2, in Brazil) for a 45-year period (1961-2005) and to investigate a network arrangement that maximized its hydrologic sustainability. Imagery of the years 1970 and 2002 was analyzed to assess temporal evolution of the network. The Water Availability in Semiarid Environments (WASA) model, which proved valid for the Upper Jaguaribe Basin, was used to assess its sustainability for almost 100 different arrangements. The optimization process was performed using the Simplex-MSX algorithm. Results showed that the hydrologic sustainability of the basin increased throughout the analysis period. Nonetheless, since the late 1990s, the system showed clear signs of saturation. The optimization process identified that the best network arrangement should have residence time of approximately 3 years, with volumetric density of 0.2 hm3·km-2, and the importance of the reservoir size continuity to obtain effective hydrologic performance. [ABSTRACT FROM AUTHOR]

An urban inundation model was developed and coupled with 1-D drainage network model (EPA-SWMM5). The objective was to achieve a 1-D/2-D coupled model that is simple and fast enough to be consistently used in planning stages of urban drainage projects. The 2-D inundation model is based on a non-standard simplification of the shallow water equation, lays between diffusion-wave and full dynamic models. Simplifications were made in the process representation and numerical solving mechanisms and a depth scaled Manning coefficient was introduced to achieve stability in the cell wetting-drying process. The 2-D model is coupled with SWMM for simulation of both network flow and surcharge induced inundation. The coupling is archived by mass transfer from the network system to the 2-D system. A damage calculation block is integrated within the model code for assessing flood damage costs in optimal planning of urban drainage networks. The model is stable in dealing with complex flow conditions, and cell wetting/drying processes, as demonstrated by a number of idealised experiments. The model application is demonstrated by applying to a case study in Brazil. [ABSTRACT FROM AUTHOR]

Water management in the Pirapama River Basin in northeastern Brazil is affected by both water quantity and water quality constraints. The region is known for significant sugarcane-based ethanol production—which is the key to the Brazilian economy and expected to grow dramatically under recent changes in climate and energy policies. Sugarcane production in the region goes hand in hand with controlled fertirrigation practices with potentially significant adverse impacts on the environment. To assess sustainable water allocation in the basin, an integrated hydrologic-economic basin model is adapted to study both water quantity and quality aspects. The model results show that incorporating water quality aspects into water allocation decisions leads to a substantial reduction in application of vinasse to sugarcane fields. Embedding highly nonlinear water quality constraints in a basin optimization model is demonstrated to be an effective tool to address the water management problems in rapidly developing biofuel areas of Brazil and elsewhere. [ABSTRACT FROM AUTHOR]

This article uses a basin-wide hydrologic model to assess the hydrologic and economic effects of expanding agriculture in the Sao Francisco River Basin, Brazil. It then uses a basin-wide economic model of agriculture to examine the effects of implementing water use regulations. Preliminary results suggest that substantially expanding agriculture would put pressure on some of the river's environmental flows. Agricultural output and rural employment would increase, though not in spatially uniform ways. The economic model demonstrates how cropping area, crop mix and production technology respond simultaneously to water shortages. While farmers can adjust, the costs of doing so may be beyond the reach of resource-poor farmers. [ABSTRACT FROM AUTHOR]

Satellite precipitation estimates are used as an alternative or as a supplement to the records of the in situ stations. Although some satellite precipitation products have reasonably consistent time series, they are often limited to specific geographic areas. The main objective of this study was to evaluate CHIRPS version 2, MSWEP version 2, and PERSIANN-CDR, compared to gridBR, as daily mean and extreme inputs represented on a monthly scale and their respective seasonal trends of rainfall in the Mearim River Drainage Basin (MDB), Maranhão state, Brazil. Estimates of errors were calculated (relative error, pbias; root mean square error, RMSE, and Willmott concordance index, d), and the chances of precipitation were estimated by remote sensing (RES). In addition, trends in precipitation were estimated by the two-sample Mann–Kendall test. Given the overall performance, the best products for estimating monthly mean daily rainfall in the MDB are CHIRPS and PERSIANN-CDR, especially for rainy months (December to May). For daily extremes on the monthly scale, the best RES is PERSIANN-CDR. There is no general agreement between gridBR and RES methods for the trend signal, even a nonsignificant one, much less a significant one. The use of MSWEP for the MDB region is discouraged by this study because it overestimates monthly averages and extremes. Finally, studies of this kind in drainage basins are essential to improve the information generated for managing territories and developing regionalized climate and hydrological models. [ABSTRACT FROM AUTHOR]

Groundwater vulnerability assessment to delineate areas that are more susceptible to contamination has become an important element for resource management and land use planning. The objective of this work was to integrate hydrogeologic data in a geographic information system (GIS) for phreatic groundwater vulnerability assessment of a pilot area from the Coastal Plain of the Rio Grande do Sul State, Brazil, using the DRASTIC method and adsorption capacity of soils. The studied ions were copper, lead, sulfate and phosphate. Using the original DRASTIC method, the vulnerability presented high values, mainly due to the texture of the sediments and the low depths of waters, which favor the accessibility of the contaminants to the groundwaters. DRASTIC with adsorption capacity of soils showed specifics results for each studied ion. Groundwater vulnerability to metals in relationship to anions was lower. These results show that the original DRASTIC method represents better contaminants with high mobility. [ABSTRACT FROM AUTHOR]

Stream-water flows and in-stream nitrate and ammonium concentrations in a small (36.7 ha) Atlantic Forest catchment were simulated using the Integrated Nitrogen in CAtchments (INCA) model version 1.9.4. The catchment, at Cunha, is in the Serra do Mar State Park, SE Brazil and is nearly pristine because the nearest major conurbations, São Paulo and Rio, are some 450 km distant. However, intensive farming may increase nitrogen (N) deposition and there are growing pressures for urbanisation. The mean-monthly discharges and NO3-N concentration dynamics were simulated adequately for the calibration and validation periods with (simulated) loss rates of 6.55 kg.ha-1 yr-1 for NO3-N and 3.85 kg.ha-1 yr-1 for NH4-N. To investigate the effects of elevated levels of N deposition in the future, various scenarios for atmospheric deposition were simulated; the highest value corresponded to that in a highly polluted area of Atlantic Forest in Sao Paulo City. It was found that doubling the atmospheric deposition generated a 25% increase in the N leaching rate, while at levels approaching the highly polluted São Paulo deposition rate, five times higher than the current rate, leaching increased by 240%, which would create highly eutrophic conditions, detrimental to downstream water quality. The results indicate that the INCA model can be useful for estimating N concentration and fluxes for different atmospheric deposition rates and hydrological conditions. [ABSTRACT FROM AUTHOR]

As watershed management programs have become more common globally, so have efforts to support these initiatives through hydrologic modeling and monitoring. However, these efforts are often guided by oversimplified assumptions of how management programs work and the quantity, quality, and type of information needed to support their planning, implementation, and evaluation. Semi‐structured interviews and focus groups with project managers, funders, and participants in three watershed management programs in the Atlantic Forest of Brazil revealed a range of hydrologic modeling and monitoring needs of watershed management programs. We identify five opportunities for hydrologic information to support overlapping management contexts: (1) inspire action and support, (2) inform investment decisions, (3) engage with potential participants, (4) prioritize location and types of activities at regional to national scales, and (5) evaluate program success. Within these opportunities, understanding who will use the information generated and how they will do so is critical to increasing the salience, credibility, and legitimacy of modeling efforts. Hydrologic modeling and monitoring play a small but critical role in the larger context of program conceptualization, design, implementation, and evaluation; grounding these efforts in local contexts supports watershed management projects in relevant and effective ways. Plain Language Summary: Active land management for a variety of benefits, including sustaining and enhancing clean and ample water supplies, is becoming more common worldwide. To achieve these ends, watershed management programs need the support of hydrologic data and models, but promising efforts by the hydrologic community often go unused when the information program managers need is not well matched to modeling efforts. We interviewed a wide range of participants in water management programs in Brazil and found five key areas where modeling and monitoring can support these programs: (1) inspire action and support, (2) inform investment decisions, (3) engage with potential participants, (4) prioritize location and types of activities at regional to national scales, and (5) evaluate program success. Our study emphasizes the importance of focusing on who will use modeling results and tailoring efforts to meet these needs. When grounded in real‐world contexts, hydrologic monitoring and modeling can play a small but critical role in supporting sustainable watershed management Key Points: Different stages in the development of watershed management programs have distinct needs from hydrologic models and dataInterviews and focus groups with program stakeholders revealed many contexts with low requirements for model accuracyHydrologic modeling responsive to user needs increases information salience, credibility, and legitimacy and improves uptake [ABSTRACT FROM AUTHOR]

The Paraguaçu watershed in northeastern Brazil faces increasing water scarcity, with water resources unable to meet the increasing demand. Accurate assessment of water availability is thus essential for efficient planning and management of local resources. In this work, the potential of the SWAT model for predicting daily and monthly variability of the hydrologic regime of the Paraguaçu River was assessed. Model calibration/validation followed: (i) A hierarchical framework; (ii) the assessment of maximum, average and minimum streamflow based on paired t-test and linear regression analysis; and (iii) the definition of permanence curves for streamflow with a probability of occurrence of 90% (Q90) and 95% (Q95). The goodness-of-fit indicators revealed a "satisfactory" model performance (model efficiency ranged from 0.42 to 0.83) when predicting streamflow in monitored sub-basins using a unique set of parameters for wet and dry conditions. The flow duration curves also showed that the model underestimated higher flows resulting from extreme events but performed well for flows with exceedance probabilities of <90%. The regression analysis and paired t-test demonstrated that the SWAT model can be used for estimating maximum, average and minimum monthly streamflow in a region where information is insufficient to support water authorities in the decision-making process. The SWAT model can thus be considered adequate for simulating monthly streamflow in the Paraguaçu watershed. [ABSTRACT FROM AUTHOR]