Showing papers by "Walter Collischonn published in 2012"

A MODIS-Based Energy Balance to Estimate Evapotranspiration for Clear-Sky Days in Brazilian Tropical Savannas

Abstract: Evapotranspiration (ET) plays an important role in global climate dynamics and in primary production of terrestrial ecosystems; it represents the mass and energy transfer from the land to atmosphere. Limitations to measuring ET at large scales using ground-based methods have motivated the development of satellite remote sensing techniques. The purpose of this work is to evaluate the accuracy of the SEBAL algorithm for estimating surface turbulent heat fluxes at regional scale, using 28 images from MODIS. SEBAL estimates are compared with eddy-covariance (EC) measurements and results from the hydrological model MGB-IPH. SEBAL instantaneous estimates of latent heat flux (LE) yielded r 2= 0.64 and r2 = 0.62 over sugarcane croplands and savannas when compared against in situ EC estimates. At the same sites, daily aggregated estimates of LE were r 2 = 0.76 and r2 = 0.66, respectively. Energy balance closure showed that turbulent fluxes over sugarcane croplands were underestimated by 7% and 9% over savannas. Average daily ET from SEBAL is in close agreement with estimates from the hydrological model for an overlay of 38,100 km2 (r2 = 0.88). Inputs to which the algorithm is most sensitive are vegetation index (NDVI), gradient of temperature (dT) to compute sensible heat flux (H) and net radiation (Rn). It was verified that SEBAL has a tendency to overestimate results both at local and regional scales probably because of low sensitivity to soil moisture and water stress. Nevertheless the results confirm the potential of the SEBAL algorithm, when used with MODIS images for estimating instantaneous LE and daily ET from large areas.

Coupled Hydrologic-Hydraulic Modeling of the Upper Paraguay River Basin

Abstract: This paper presents a detailed modeling of rainfall-runoff processes and flow routing along a complex large-scale region, the Upper Paraguay River Basin (UPRB), encompassing a drainage area of approximately 600,000 km2, which extends over Brazil, Paraguay, and Bolivia. Within the UPRB lies the Pantanal, the world’s largest wetland, with extraordinary biodiversity and great ecologic value, but which currently is threatened by anthropogenic activities. A conceptual model was applied with two main components: (1) simulation of the basin and part of the Paraguay River tributaries by means of the distributed large-scale hydrological model MGB-IPH using simpler flow routing methods; and (2) simulation of the main drainage network, approximately 4,800 km of river reaches, with a one-dimensional hydrodynamic model. Despite the data scarcity, complexity, and the intricate river drainage network of the region, the coupled model was able to represent the hydrological regime of the basin. Comparisons between...

On the sources of hydrological prediction uncertainty in the Amazon

Abstract: Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

Comparison of forecasts of mean monthly water level in the Paraguay River, Brazil, from two fractionally differenced models

Abstract: [1] The paper compares forecasts of mean monthly water levels up to six months ahead at Ladario, 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.

Assimilação de Dados de Vazão na Previsão de Cheias em Tempo-Real com o Modelo Hidrológico MGB-IPH

Abstract: This paper presents an empirical data assimilation method applied to a real-time flood forecasting hydrological model based on the MGB-IPH hydrological model. A medium-size basin located in Southeastern Brazil was selected as a case study, primarily because of the availability of hourly hydrologic information. Streamflow forecasts were calculated with lead times up to 48 hours, at hourly intervals. Several forecast scenarios were simulated with the MGB-IPH model: (1) forecasting with data assimilation and "perfect" precipitation forecasts (in which observed precipitation was used as a forecast of precipitation); (2) forecasting with data assimilation and zero precipitation forecasts; and (3) forecasting without data assimilation and "perfect" precipitation forecasts. Forecasts from these three scenarios were compared to observed streamflows and to forecasts from a naive model which assumes that the last recorded streamflow will be held constant up to the end of the forecast horizon. Several performance measures such as the Nash—Sutcliffe efficiency coefficient, the mean absolute error and the mean relative error were used to assess the relative performance of the models. Results show that the data assimilation method has positive impacts on real-time flood forecasting, increasing the forecast accuracy for all lead-times

Conservação de Volume em Modelos Simplificados de Propagação de Vazão

Abstract: The Muskingum-Cunge method is widely used to calculate streamflow routing in rivers, especially as a module for more complex hydrological models. In its non-linear form, this method allows representing flood routing in rivers with flood plains and allows estimating the extreme flows more effectively. However, volume conservation problems may occur because of the use of non-linear schemes. Recent modifications were proposed for the non-linear Muskingum-Cunge method, to compensate or avoid these volume errors. This modification was called MuskingumCunge-Todini (method (MCT). The present paper evaluates the MCT method comparing it to other streamflow routing methods in a hypothetical canal, besides looking at its application in a stretch of River Sâo Francisco, Brazil. To evaluate the results , two performance indicators were used: the Nash-Sutcliffe coefficient and volume error. The comparative application in the hypothetical canal confirmed that the conventional non-linear MuskingumCunge model does not conserve the volume adequately, and that this problem is more marked in low slope rivers (error greater than 4% for a 0.0001 m/m slope). Besides, it was observed that the MCT model practically eliminates the conservation errors of volume (error of 0.01% for a slope of 0.0001m/m ). The MCT method used for a stretch of the River Sao Francisco also presented satisfactory results. The Nash-Sutcliffe coefficient achieved a maximum value of 0.98, while the mean volume error was only 0.8%