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Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key process linking surface and atmospheric energy budgets, yet its drivers and patterns across wetlandscapes are poorly understood worldwide. Here we assess the ET dynamics in 12 wetland complexes across South America, revealing major differences under temperate, tropical, and equatorial climates. While net radiation is a dominant driver of ET seasonality in most environments, flooding also contributes strongly to ET in tropical and equatorial wetlands, especially in meeting the evaporative demand. Moreover, significant water losses through wetlands and ET differences between wetlands and uplands occur in temperate, more water-limited environments and in highly flooded areas such as the Pantanal, where slow river flood propagation drives the ET dynamics. Finally, floodplain forests produce the greatest ET in all environments except the Amazon River floodplains, where upland forests sustain high rates year round. Our findings highlight the unique hydrological functioning and ecosystem services provided by wetlands on a continental scale. Wetlands can affect regional climate by altering surface-atmosphere interactions. This paper investigates drivers and patterns of evapotranspiration in South American wetlands, from the Amazon floodplains to the large Pantanal system. [ABSTRACT FROM AUTHOR]

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]

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]

Global and regional climate models (GCM and RCM respectively) are the current mathematical tools used to project alterations on precipitation regimes given different greenhouse gases emissions scenarios. However, these models have specific resolutions, physical equations and numerical approaches that provide a diverse set of performances across different regions and spatial–temporal scales. In South America, most hydrological impact studies have used the Eta RCM to yield precipitation projections without a proper uncertainty analysis. It is important to acknowledge its uncertainties prior to any hydrological assessment to adequately support climate change investigations and related water decision making. Therefore, we aim to investigate how Eta extreme precipitation biases vary in different spatial–temporal scales from a hydrological perspective. Thus, we evaluate the extreme precipitation generated by the Eta RCM driven by four different GCMs. It is investigated Eta biases across different temporal (3 hr–5 days) and spatial scales (0.2–1.0°) and how those errors affect river streamflow simulations. It is used local intensity–duration–frequency (IDF) curves and gridded precipitation datasets (MSWEP and ERA5‐Land) as references for Eta assessment. In general, Eta underestimates subdaily extreme precipitation across South America, regardless of the driven GCM. The median bias of a 10‐year return period daily precipitation is −36 mm (1st and 3rd quantiles −58 and −17 mm) compared to MSWEP and −26 mm (1st and 3rd quantiles −45 and −11 mm) compared to ERA5‐Land. However, the relative errors reduce with temporal and spatial aggregation. For example, the average bias of extreme precipitation decreases 8.4 and 5.4 percentage points from 1‐ to 5‐day duration compared to MSWEP and ERA5‐Land, respectively. The negative biases observed for precipitation (≈20%) are propagated to the flood discharges (≈40%), and these errors reduce with the drainage area. In general, there are greater biases in extreme discharges for small basins, but these errors considerably reduce for basins larger than 30,000 km2 compared to MGB‐MSWEP simulations. Compared to MGB‐ERA5‐Land simulations, MGB‐Eta presents relatively similar errors for basins of different sizes, probably due to the high negative bias for not only extreme but average precipitation as well. [ABSTRACT FROM AUTHOR]

The anomalous entrance of water into groundwater systems can affect storage throughout long periods and normally relies on infrequent and irregular pulses of groundwater recharge defined by the term episodic recharge. Recently there was a groundwater recharge of large magnitude with unknown circumstances in the Caiuá aquifer. This unique event was explored in detail here and allowed to better understand the occurrence of such events in humid subtropical climates in South America. For this study, groundwater monitoring daily data from the Integrated Groundwater Monitoring Network was used combined with a specific yield obtained from geophysical wireline logging to obtain groundwater recharge rates. To improve the investigation, we also used a baseflow separation method to obtain the groundwater contribution into local rivers. The groundwater storage variations were also assessed by remote sensing with the GRACE data. Results showed the importance of high soil moisture storage on the occurrence of large episodic recharge events. We estimated that the groundwater recharger volumes derived from 1 year that included the unique episodic recharge observed (total of 866 mm for April 2015–March 2016) were comparable with the sum of 7 years of groundwater recharge (total of 867 mm). Atypical rainfall in winter periods were responsible for the increase in soil moisture that explained that unique event. GRACE‐based GWS showed concordance detecting the occurrence of the unique episodic recharge. However, the variation in terms of volumes obtained by GRACE does not represent the behaviour observed in the aquifer by the WTF method. The results also indicated that changes in aquifer storage caused by episodic recharge events directly affect low flows in rivers over long periods. The main knowledge gap addressed here relates to exploring a unique episodic recharge event quite rare to observe with its long‐term impacts on hydroclimatic variability over a humid subtropical portion of the Caiuá aquifer. [ABSTRACT FROM AUTHOR]

This study provides a spatio-temporal analysis of the great floods that occurred in South America in 1983 using hydrometeorological data and outputs from a continental-scale hydrological-hydrodynamic model. In the extreme year 1983, there were three main flooding periods (February, June and July) in many South American river basins, such as the Araguaia, Tocantins, São Francisco, Uruguay, La Plata and its tributaries, resulting in high discharge of the Paraguay River for many months. Depth–area–duration curves show that 3-day precipitation events in northern regions of South America were among the largest 15 events in the period 1980–2015 but only for specific locations, whereas in southern areas, the most extreme events in the same period were for larger durations (≥7-day precipitation). Modelled total export of water volume to the oceans indicates that rivers draining to the South Atlantic reached an anomaly of 3.7 during 1983, followed by 1998 (1.9) and 1992 (1.1), all of them corresponding to El Niño years. [ABSTRACT FROM AUTHOR]

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process. [ABSTRACT FROM AUTHOR]

• 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]

• We assess EMOS and ECC to postprocess daily streamflow forecasts in South America. • We used global rainfall data and ECMWF reforecasts as inputs to a continental model. • EMOS substantially improves forecast skill relative to climatology and persistence. • ECC performance is linked to the raw ensemble spread and width of EMOS distribution. • EMOS and ECC postprocessing are promising even with real-time data limitations. Probabilistic hydrological forecasting and ensemble techniques have leveraged streamflow prediction at regional to continental scales up to several weeks in advance. However, ensembles that only account for meteorological forecast uncertainty are typically biased and subject to dispersion errors, thus limiting their use for rational decision-making and optimization systems. Statistical postprocessing is therefore necessary to convert ensemble forecasts into calibrated and sharp predictive distributions, and it should also account for dependencies between lead times to enable realistic forecast trajectories. This work provides a continental-scale assessment of the use of statistical postprocessing on medium-range, ensemble streamflow forecasts over South America (SA). These forecasts were produced through a large-scale hydrologic–hydrodynamic model forced with a global precipitation dataset and ECMWF reforecast data. The Ensemble Model Output Statistics (EMOS) technique was used to generate conditional predictive distributions in 488 locations at each forecast lead time, while the Ensemble Copula Coupling method with the transformation scheme (ECC-T) was applied to derive ensemble traces from EMOS distributions. Postprocessed streamflow forecasts were cross-validated for the period from 1996 to 2014 using a range of verification metrics. Results showed that the skill and reliability of EMOS forecasts substantially improve over the raw ensembles, and that EMOS leads to skillful predictions relative to discharge climatology and persistence forecasts up to 15 days in advance in most locations. Furthermore, EMOS results in predictive distributions that are generally sharper than the climatology. Limitations in depicting autocorrelations of forecast trajectories were observed in rivers for which the raw ensemble spread is very low and EMOS has to largely increase dispersion, especially at short lead times. The study's findings suggest that combining a continental-scale hydrological model with EMOS and ECC-T methods can lead to skillful predictions and realistic ensemble traces in several locations in SA, even if in situ hydrometeorological observations are not available in real time. [ABSTRACT FROM AUTHOR]

• An evaluation of the potential forecast skill over South America (SA) is presented. • A hydrologic–hydrodynamic model is forced by ensemble forecasts from ECMWF. • Hydrological forecasts are skillful mainly at warm temperate, fully humid climate. • A direct relation between forecast skill and basin size is not always valid. Recent scientific and computational developments have pushed hydrological forecasting systems up to continental and global scales. In this study, we evaluate the potential skill of ensemble streamflow forecasts for flood prediction in South America (SA), with a closer look on some of the large basins of the continent. Medium-range precipitation forecasts from the ECMWF Ensemble Prediction System were used to force a continental-scale, fully coupled hydrologic-hydrodynamic model to produce streamflow forecasts up to 15 days in advance. Forecasts were evaluated over a 4-year period against a reference simulation, derived from the hydrological model forced with a gridded precipitation dataset that merges data from multiple sources (MSWEP). To provide a benchmark for assessing the potential skill, forecasts based on the Ensemble Streamflow Prediction (ESP) technique were produced by running the hydrological model with precipitation data resampled from past MSWEP observations. Overall, ECMWF-based forecasts were skillful mainly at the eastern and southeastern regions of the continent (e.g., São Francisco, Parnaíba, Upper Paraná, Tocantins-Araguaia and Uruguay basins), showing little or no added skill in large rivers draining the Andes such as the Magdalena, Orinoco, Madeira (Amazon) and right-bank tributaries of the Paraguay. In the latter basins, ECMWF-based forecasts largely overestimated the exceedance probabilities of critical thresholds due to a positive bias in the predicted precipitation (in relation to MSWEP), visible mostly during the wet season. In addition, forecasts were found to perform better than ESP in terms of both overall quality and discrimination skill at regions characterized by a warm temperate, fully-humid climate. A direct relation between forecast skill and basin size was not always valid, since the former can vary geographically along tributaries. Limitations and future developments needed for continental-scale hydrological forecasting in SA are also discussed. [ABSTRACT FROM AUTHOR]

Because of the uncertainties cascading from numerical weather to hydrological models, raw hydrological ensemble forecasts are often biased and characterized by dispersion errors, thus limiting their use for decision making. Statistical postprocessing can help in reducing these shortcomings by improving the consistency between forecast probabilities and their corresponding observed frequencies, with the aim of producing forecasts that are sharp as possible. The Ensemble Model Output Statistics (EMOS), for instance, is a regression-based postprocessing method commonly used in atmospheric sciences to estimate predictive distributions based on past forecast errors, and has been recently applied for hydrological purposes with promising results (Hemri et al., 2015). Here we assess the performance of postprocessed ensemble hydrological forecasts over South America region using the EMOS method. Medium-range (15 days), bi-weekly precipitation re-forecasts from the 11-member ECMWF EPS (0.2º grid resolution) were acquired between 1996 – 2014 and were used to force the MGB hydrologic-hydrodynamic model developed for the continental domain (Siqueira et al., 2018). To derive postprocessed univariate predictive distributions, discharges were first transformed to an approximate Gaussian space through box-cox transformation, and then a truncated EMOS model fitted with subsets of training data according to the season (DJF, MAM, JJA, and SON) was applied using a leave-one-year-out approach. In addition, as univariate methods such as EMOS do not account for spatial and/or temporal dependence structures, the Ensemble Copula Coupling (ECC) technique was further applied to each EMOS distribution in order to produce realistic ensemble trajectories by preserving the statistical coherence among different lead times. The performance of hydrological forecasts was evaluated using appropriate metrics and a reference simulation instead of observed data, the latter derived from a long-term MGB run using historical precipitation as input. Results show that in many cases EMOS-ECC is able to reduce bias and improve reliability in comparison with the raw ensemble, but the skill improvement of postprocessed hydrological forecasts varies according to the region and forecast lead time.References Hemri, S., Lisniak, D., and Klein, B.: Multivariate postprocessing techniques for probabilistic hydrological forecasting. Water Resources Research, 51(9), 7436-7451, https://doi.org/10.1002/2014WR016473, 2015Siqueira, V. A., Paiva, R. C. D., Fleischmann, A. S., Fan, F. M., Ruhoff, A. L., Pontes, P. R. M., Paris, A., Calmant, S., and Collischonn, W.: Toward continental hydrologic–hydrodynamic modeling in South America, Hydrol. Earth Syst. Sci., 22, 4815-4842, https://doi.org/10.5194/hess-22-4815-2018, 2018. [ABSTRACT FROM AUTHOR]