Search

In this work, the role of initial conditions (ICs) and boundary conditions (BCs) in the predictability of natural flows of large river basins (>1000 km2) in South America using the South American Large Basins Model (MGB-SA) was investigated. We proposed an analysis based on a simple metric: predictability was estimated by the number of days in which the streamflow forecasts performed above a given threshold. The role of ICs was assessed through null precipitation experiments and the role of BCs through climatological rainfall. For experiments with a Kling-Gupta efficiency metric and threshold of 0.95, the study shows that streamflow predictability from the ICs is relatively high (up to 40 days) in the main river reaches of flat-relief basins. The increase in predictability due to climatology-based BCs mostly occurs in areas with high predictability based on ICs. Basins with fast response present low streamflow predictability (up to three days). [ABSTRACT FROM AUTHOR]

This paper discusses issues arising when Tropical Rainfall Measuring Mission (TRMM) estimates of rainfall characteristics are calculated for the sparsely gauged Brazilian Amazon. The particular issue considered is the calculation of statistical uncertainty in (1) comparisons between TRMM estimates of rainfall characteristics and their corresponding rain gauge estimates and (2) interpolated estimates of rainfall characteristics, which are conditional on local TRMM estimates near ungauged sites. Three characteristics selected for particular comparison are (1) mean annual rainfall, 1998-2005, (2) mean value, over these years, of the 95% quantile of within-year daily rainfall, and (3) mean number of days in the year with rainfall of 2 mm or more. The paper demonstrates how positive spatial correlation leads to underestimation of the uncertainty in the comparisons (i.e., the standard errors of differences), so that the significance of differences, when observed, is likely to be overestimated if spatial correlation is ignored. For the three characteristics mentioned, the Matérn spatial correlation function with shape parameter = 1/2, giving an exponential correlation function, was found to be adequate for modeling spatial correlation. It is also argued that the statistic 1 − b, where b is the slope of the regression of a rain gauge estimate of a rainfall characteristic on its counterpart derived from satellite instrumentation, is a measure of the errors in the satellite (spatially averaged) estimates. Finally, although issues of spatial correlation are discussed with particular reference to rainfall, it is argued that the same issues arise whenever satellite-derived estimates of a component of the water cycle must be combined with ground-based measurements. [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]

• Extreme rainfall events that lead to floods in large rivers are expected to decrease. • Antecedent soil moisture is expected to be reduced in most of South America. • Soil moisture seems more impactful than precipitation regarding ordinary floods. A warmer atmosphere is able to hold more water which consequently intensifies the hydrological cycle. The projected increase in extreme precipitation has been associated with greater floods; however, most recent studies have argued that the reduced soil moisture could be causing the opposite effect. We aim to understand how the hydrometeorological variables affect flood discharge and what the projections are for South America, a vulnerable continent that has been barely studied regarding flood trends. We used climate data from Eta simulations nested in 4 global climate models (BESM, CanESM2, HadGEM2-ES, MIROC5) as input for the MGB-SA hydrological model to yield flood discharge estimates. Then we were able to project the climate impacts on extreme precipitation, antecedent soil moisture, and flood discharge for large rivers (>1,000 km2) and understand how these variables are related. Our results showed a strong sign that antecedent soil moisture is expected to be reduced in most of the continent except in Southeastern South America (SESA). On the other hand, there are mixed signs for rarer precipitation and a clear spatial pattern for 2-year precipitations (RP2), which is expected to increase on the SESA and west Amazon and decrease on Central South America (CSA). For basins>100,000 km2, results indicate a negative change sign for 2-year precipitations, meaning that rainfall events that generate ordinary floods in large South American rivers are expected to decrease in the XXI century due to climate change. The change signs for flood discharge and extreme precipitation are spatially similar but more basins show a decrease for flooding than for rainfall. While only half of the South American basins are expected to present reduced 2-year precipitations, nearly 70 % of the rivers present a negative sign for 2-year floods, which can be attributed to the reduced antecedent soil moisture. [ABSTRACT FROM AUTHOR]

Recently developed methodologies such as climate reanalysis make it possible to create a historical record of climate systems. This paper proposes a methodology called Hydrological Retrospective (HR), which essentially simulates large rainfall datasets, using this as input into hydrological models to develop a record of past hydrology, making it possible to analyze past floods and droughts. We developed a methodology for the Amazon basin, where studies have shown an increase in the intensity and frequency of hydrological extreme events in recent decades. We used eight large precipitation datasets (more than 30 years) as input for a large scale hydrological and hydrodynamic model (MGB-IPH). HR products were then validated against several in situ discharge gauges controlling the main Amazon sub-basins, focusing on maximum and minimum events. For the most accurate HR, based on performance metrics, we performed a forecast skill of HR to detect floods and droughts, comparing the results with in-situ observations. A statistical temporal series trend was performed for intensity of seasonal floods and droughts in the entire Amazon basin. Results indicate that HR could represent most past extreme events well, compared with in-situ observed data, and was consistent with many events reported in literature. Because of their flow duration, some minor regional events were not reported in literature but were captured by HR. To represent past regional hydrology and seasonal hydrological extreme events, we believe it is feasible to use some large precipitation datasets such as i) climate reanalysis, which is mainly based on a land surface component, and ii) datasets based on merged products. A significant upward trend in intensity was seen in maximum annual discharge (related to floods) in western and northwestern regions and for minimum annual discharge (related to droughts) in south and central-south regions of the Amazon basin. Because of the global coverage of rainfall datasets, this methodology can be transferred to other regions for better estimation of future hydrological behavior and its impact on society. [ABSTRACT FROM AUTHOR]