Juan Martín Bravo

Large-Scale Hydrodynamic Modeling of a Complex River Network and Floodplains

This paper presents a one-dimensional hydrodynamic modeling of a large-scale river network and floodplains. The study site comprises the Upper Paraguay River and its main tributaries (a total of 4,800 km of river reaches) in South American central area, including a complex river network flowing along the Pantanal wetland. The main issues are related to preparing input data for the hydraulic model in a consistent and georeferenced database and to representing different flow regimes. Geographic information systems-based automatic procedures were developed in order to produce cross-sectional profiles that encompass the large floodplains and to link hydraulic data and spatial location. The marked seasonal flow regime and relative smooth hydrographs of Paraguay River were quite well reproduced by the hydraulic model. For the tributaries, it must be mentioned the model’s ability to simulate both cases when the hydrograph does not present a marked peak flow, due to water loss for the floodplain, and when the hyd...

Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting

This study reports on the performance of two medium-range streamflow forecast models: (1) a multilayer feed-forward artificial neural network; and (2) a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models. The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km(2), with lead times up to 12 days, at daily intervals. The Nash-Sutcliffe efficiency index, the root-mean-square error, the mean absolute error, and the mean relative error were used to assess the relative performance of the models. Results showed that the performance of streamflow forecasts was strongly dependent on the quality of quantitative precipitation forecasts used. The artificial neural network (ANN) method seemed to be less sensitive to precipitation forecast error relative to the distributed hydrological model. Hence, the latter presented a better skill in flow forecasting when using the more accurate perfect precipitation forecast. The conceptual hydrological model also demonstrates better forecast skill than ANN models for longer lead times, when the representation of the rainfall-runoff process and of the water storage in the watershed becomes more important than the flow routing along the drainage network. In addition, results obtained by incorporating a quantitative precipitation forecast in both models performed better than the current streamflow obtained by the Brazilian national electric system operator using statistical models which do not utilize information on precipitation, whether observed or forecast. (Less)

Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm

Real-time updating of channel flow routing models is essential for error reduction in hydrological forecasting. Recent updating techniques found in scientific literature, although very promising, are complex and often applied in models that demand much time and expert knowledge for their development, posing challenges for using in an operational context. Since powerful and well-known computational tools are currently available, which provide easy-to-use and less time-consuming platforms for preparation of hydrodynamic models, it becomes interesting to develop updating techniques adaptable to such tools, taking full advantage of previously calibrated models as well as the experience of the users. In this work, we present a real-time updating procedure for streamflow forecasting in HEC-RAS model, using the Shuffled Complex Evolution - University of Arizona (SCE-UA) optimization algorithm. The procedure consists in a simultaneous correction of boundary conditions and model parameters through: (i) generation of a lateral inflow, based on Soil Conservation Service (SCS) dimensionless unit hydrograph and; (ii) estimation of Manning roughness in the river channel. The algorithm works in an optimization window in order to minimize an objective function, given by the weighted sum of squared errors between simulated and observed flows where differences in later intervals (start of forecast) are more penalized. As a case study, the procedure was applied in a river reach between Salto Caxias dam and Hotel Cataratas stream gauge, located in the Lower Iguazu Basin. Results showed that, with a small population of candidate solutions in the optimization algorithm, it is possible to efficiently improve the model performance for streamflow forecasting and reduce negative effects caused by lag errors in simulation. An advantage of the developed procedure is the reduction of both excessive handling of external files and manual adjustments of HEC-RAS model, which is important when operational decisions must be taken in relatively short times.

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

Efficient number of calibrated cross sections bottom levels on a hydrodynamic model using the SCE-UA algorithm. Case study: Madeira River

Hydrodynamic models are important tools for simulating river water level and flow. A considerable fraction of the hydrodynamic model errors are related to parameters uncertainties. As cross sections bottom levels considerably affect water level simulation, this parameter has to be well estimated for flood studies. Automatic calibration performance and processing time depend on the search space dimension, which is related to the number of calibrated parameters. This paper shows the application of the Shuffled Complex Evolution (SCE-UA) optimization algorithm to assess the number of cross sections bottom levels used in calibration. Also was evaluated the extent of algorithm exploration regarding computational processing time and accuracy. It was tested the calibration of 2, 4, 7 and 10 cross sections bottom levels (2PAR, 4PAR, 7PAR and 10PAR calibration configurations) of a 1,100 km reach of the Madeira River. 7PAR and 10PAR representation had better fitness (lower objective function value) on cross sections used for calibration; however, the error on other cross sections (2 validation gauging stations) was higher than 2PAR and 4PAR calibration. The short number (5) of gauging stations used in calibration has limited the number of calibrated parameters to represent adequately the river level profile. Finally, this paper shows a contribution for the parsimonious selection of parameters regarding the spatial distribution of observation sites used in calibration.

Projeções de Vazão Afluente à Lagoa Mangueira com Base em Cenários de Mudanças Climáticas

This manuscript presents the assessment of climate change impacts on the streamflow at the Mangueira lake watershed, located in Southern Brazil, based on precipitation predictions of twenty Atmospheric/Ocean General Circulation Models (AOGCMs) that feed a hydrologic model named IPH II. The projections were based on two emission scenarios of the IPCC that set the forcing conditions for the AOGCMs to estimate future climate: A2, characterized by higher emissions and B2, characterized by lower emissions. The MAGICC/ScenGen was used to obtain the projected monthly anomalies of precipitation for the scenarios A2/B2 at two future time intervals centered at 2030 and 2070. Time series of projected precipitation were estimated using the delta change approach. The results in terms of average annual flow shows that mean value of the anomalies on the near horizon resulted very similar, equal to +2.86%(A2) and +2.48%(B2). This statistic increased on the long horizon, with a mean value of the anomalies of +16.94%(A2) and +11.83%(B2). The dispersion among results of the AOGCMs showed anomalies that may reach [+10%,−7%] in the near future and [+30%,−20%], in the long horizon. Thus, although there was higher agreement between AOGCMs in increasing flows, it is important to highlight the dispersion of results.